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  • Published: 01 July 2020

The effect of social media on well-being differs from adolescent to adolescent

  • Ine Beyens   ORCID: orcid.org/0000-0001-7023-867X 1 ,
  • J. Loes Pouwels   ORCID: orcid.org/0000-0002-9586-392X 1 ,
  • Irene I. van Driel   ORCID: orcid.org/0000-0002-7810-9677 1 ,
  • Loes Keijsers   ORCID: orcid.org/0000-0001-8580-6000 2 &
  • Patti M. Valkenburg   ORCID: orcid.org/0000-0003-0477-8429 1  

Scientific Reports volume  10 , Article number:  10763 ( 2020 ) Cite this article

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  • Human behaviour

The question whether social media use benefits or undermines adolescents’ well-being is an important societal concern. Previous empirical studies have mostly established across-the-board effects among (sub)populations of adolescents. As a result, it is still an open question whether the effects are unique for each individual adolescent. We sampled adolescents’ experiences six times per day for one week to quantify differences in their susceptibility to the effects of social media on their momentary affective well-being. Rigorous analyses of 2,155 real-time assessments showed that the association between social media use and affective well-being differs strongly across adolescents: While 44% did not feel better or worse after passive social media use, 46% felt better, and 10% felt worse. Our results imply that person-specific effects can no longer be ignored in research, as well as in prevention and intervention programs.

Introduction

Ever since the introduction of social media, such as Facebook and Instagram, researchers have been studying whether the use of such media may affect adolescents’ well-being. These studies have typically reported mixed findings, yielding either small negative, small positive, or no effects of the time spent using social media on different indicators of well-being, such as life satisfaction and depressive symptoms (for recent reviews, see for example 1 , 2 , 3 , 4 , 5 ). Most of these studies have focused on between-person associations, examining whether adolescents who use social media more (or less) often than their peers experience lower (or higher) levels of well-being than these peers. While such between-person studies are valuable in their own right, several scholars 6 , 7 have recently called for studies that investigate within-person associations to understand whether an increase in an adolescent’s social media use is associated with an increase or decrease in that adolescent’s well-being. The current study aims to respond to this call by investigating associations between social media use and well-being within single adolescents across multiple points in time 8 , 9 , 10 .

Person-specific effects

To our knowledge, four recent studies have investigated within-person associations of social media use with different indicators of adolescent well-being (i.e., life satisfaction, depression), again with mixed results 6 , 11 , 12 , 13 . Orben and colleagues 6 found a small negative reciprocal within-person association between the time spent using social media and life satisfaction. Likewise, Boers and colleagues 12 found a small within-person association between social media use and increased depressive symptoms. Finally, Coyne and colleagues 11 and Jensen and colleagues 13 did not find any evidence for within-person associations between social media use and depression.

Earlier studies that investigated within-person associations of social media use with indicators of well-being have all only reported average effect sizes. However, it is possible, or even plausible, that these average within-person effects may have been small and nonsignificant because they result from sizeable heterogeneity in adolescents’ susceptibility to the effects of social media use on well-being (see 14 , 15 ). After all, an average within-person effect size can be considered an aggregate of numerous individual within-person effect sizes that range from highly positive to highly negative.

Some within-person studies have sought to understand adolescents’ differential susceptibility to the effects of social media by investigating differences between subgroups. For instance, they have investigated the moderating role of sex to compare the effects of social media on boys versus girls 6 , 11 . However, such a group-differential approach, in which potential differences in susceptibility are conceptualized by group-level moderators (e.g., gender, age) does not provide insights into more fine-grained differences at the level of the single individual 16 . After all, while girls and boys each represent a homogenous group in terms of sex, they may each differ on a wide array of other factors.

As such, although worthwhile, the average within-person effects of social media on well-being obtained in previous studies may have been small or non-significant because they are diluted across a highly heterogeneous population (or sub-population) of adolescents 14 , 15 . In line with the proposition of media effects theories that each adolescent may have a unique susceptibility to the effects of social media 17 , a viable explanation for the small and inconsistent findings in earlier studies may be that the effect of social media differs from adolescent to adolescent. The aim of the current study is to investigate this hypothesis and to obtain a better understanding of adolescents’ unique susceptibility to the effects of social media on their affective well-being.

Social media and affective well-being

Within-person studies have provided important insights into the associations of social media use with cognitive well-being (e.g., life satisfaction 6 ), which refers to adolescents’ cognitive judgment of how satisfied they are with their life 18 . However, the associations of social media use with adolescents’ affective well-being (i.e., adolescents’ affective evaluations of their moods and emotions 18 ) are still unknown. In addition, while earlier within-person studies have focused on associations with trait-like conceptualizations of well-being 11 , 12 , 13 , that is, adolescents’ average well-being across specific time periods 18 , there is a lack of studies that focus on well-being as a momentary affective state. Therefore, we extend previous research by examining the association between adolescents’ social media use and their momentary affective well-being. Like earlier experience sampling (ESM) studies among adults 19 , 20 , we measured adolescents’ momentary affective well-being with a single item. Adolescents’ momentary affective well-being was defined as their current feelings of happiness, a commonly used question to measure well-being 21 , 22 , which has high convergent validity, as evidenced by the strong correlations with the presence of positive affect and absence of negative affect.

To assess adolescents’ momentary affective well-being (henceforth referred to as well-being), we conducted a week-long ESM study among 63 middle adolescents ages 14 and 15. Six times a day, adolescents were asked to complete a survey using their own mobile phone, covering 42 assessments per adolescent, assessing their affective well-being and social media use. In total, adolescents completed 2,155 assessments (83.2% average compliance).

We focused on middle adolescence, since this is the period in life characterized by most significant fluctuations in well-being 23 , 24 . Also, in comparison to early and late adolescents, middle adolescents are more sensitive to reactions from peers and have a strong tendency to compare themselves with others on social media and beyond. Because middle adolescents typically use different social media platforms, in a complementary way 25 , 26 , 27 , each adolescent reported on his/her use of the three social media platforms that s/he used most frequently out of the five most popular social media platforms among adolescents: WhatsApp, followed by Instagram, Snapchat, YouTube, and, finally, the chat function of games 28 . In addition to investigating the association between overall social media use and well-being (i.e., the summed use of adolescents’ three most frequently used platforms), we examined the unique associations of the two most popular platforms, WhatsApp and Instagram 28 .

Like previous studies on social media use and well-being, we distinguished between active social media use (i.e., “activities that facilitate direct exchanges with others” 29 ) and passive social media use (i.e., “consuming information without direct exchanges” 29 ). Within-person studies among young adults have shown that passive but not active social media use predicts decreases in well-being 29 . Therefore, we examined the unique associations of adolescents’ overall active and passive social media use with their well-being, as well as active and passive use of Instagram and WhatsApp, specifically. We investigated categorical associations, that is, whether adolescents would feel better or worse if they had actively or passively used social media. And we investigated dose–response associations to understand whether adolescents’ well-being would change as a function of the time they had spent actively or passively using social media.

The hypotheses and the design, sampling and analysis plan were preregistered prior to data collection and are available on the Open Science Framework, along with the code used in the analyses ( https://osf.io/nhks2 ). For details about the design of the study and analysis approach, see Methods.

In more than half of all assessments (68.17%), adolescents had used social media (i.e., one or more of their three favorite social media platforms), either in an active or passive way. Instagram (50.90%) and WhatsApp (53.52%) were used in half of all assessments. Passive use of social media (66.21% of all assessments) was more common than active use (50.86%), both on Instagram (48.48% vs. 20.79%) and WhatsApp (51.25% vs. 40.07%).

Strong positive between-person correlations were found between the duration of active and passive social media use (overall: r  = 0.69, p  < 0.001; Instagram: r  = 0.38, p  < 0.01; WhatsApp: r  = 0.85, p  < 0.001): Adolescents who had spent more time actively using social media than their peers, had also spent more time passively using social media than their peers. Likewise, strong positive within-person correlations were found between the duration of active and passive social media use (overall: r  = 0.63, p  < 0.001; Instagram: r  = 0.37, p  < 0.001; WhatsApp: r  = 0.57, p  < 0.001): The more time an adolescent had spent actively using social media at a certain moment, the more time s/he had also spent passively using social media at that moment.

Table 1 displays the average number of minutes that adolescents had spent using social media in the past hour at each assessment, and the zero-order between- and within-person correlations between the duration of social media use and well-being. At the between-person level, the duration of active and passive social media use was not associated with well-being: Adolescents who had spent more time actively or passively using social media than their peers did not report significantly higher or lower levels of well-being than their peers. At the within-person level, significant but weak positive correlations were found between the duration of active and passive overall social media use and well-being. This indicates that adolescents felt somewhat better at moments when they had spent more time actively or passively using social media (overall), compared to moments when they had spent less time actively or passively using social media. When looking at specific platforms, a positive correlation was only found for passive WhatsApp use, but not for active WhatsApp use, and not for active and passive Instagram use.

Average and person-specific effects

The within-person associations of social media use with well-being and differences in these associations were tested in a series of multilevel models. We ran separate models for overall social media use (i.e., active use and passive use of adolescents’ three favorite social media platforms, see Table 2 ), Instagram use (see Table 3 ), and WhatsApp use (see Table 4 ). In a first step we examined the average categorical associations for each of these three social media uses using fixed effects models (Models 1A, 3A, and 5A) to investigate whether, on average, adolescents would feel better or worse at moments when they had used social media compared to moments when they had not (i.e., categorical predictors: active use versus no active use, and passive use versus no passive use). In a second step, we examined heterogeneity in the within-person categorical associations by adding random slopes to the fixed effects models (Models 1B, 3B, and 5B). Next, we examined the average dose–response associations using fixed effects models (Models 2A, 4A, and 6A), to investigate whether, on average, adolescents would feel better or worse when they had spent more time using social media (i.e., continuous predictors: duration of active use and duration of passive use). Finally, we examined heterogeneity in the within-person dose–response associations by adding random slopes to the fixed effects models (Models 2B, 4B, and 6B).

Overall social media use.

The model with the categorical predictors (see Table 2 ; Model 1A) showed that, on average, there was no association between overall use and well-being: Adolescents’ well-being did not increase or decrease at moments when they had used social media, either in a passive or active way. However, evidence was found that the association of passive (but not active) social media use with well-being differed from adolescent to adolescent (Model 1B), with effect sizes ranging from − 0.24 to 0.68. For 44.26% of the adolescents the association was non-existent to small (− 0.10 <  r  < 0.10). However, for 45.90% of the adolescents there was a weak (0.10 <  r  < 0.20; 8.20%), moderate (0.20 <  r  < 0.30; 22.95%) or even strong positive ( r  ≥ 0.30; 14.75%) association between overall passive social media use and well-being, and for almost one in ten (9.84%) adolescents there was a weak (− 0.20 <  r  < − 0.10; 6.56%) or moderate negative (− 0.30 <  r  < − 0.20; 3.28%) association.

The model with continuous predictors (Model 2A) showed that, on average, there was a significant dose–response association for active use. At moments when adolescents had used social media, the time they spent actively (but not passively) using social media was positively associated with well-being: Adolescents felt better at moments when they had spent more time sending messages, posting, or sharing something on social media. The associations of the time spent actively and passively using social media with well-being did not differ across adolescents (Model 2B).

Instagram use

As shown in Model 3A in Table 3 , on average, there was a significant categorical association between passive (but not active) Instagram use and well-being: Adolescents experienced an increase in well-being at moments when they had passively used Instagram (i.e., viewing posts/stories of others). Adolescents did not experience an increase or decrease in well-being when they had actively used Instagram. The associations of passive and active Instagram use with well-being did not differ across adolescents (Model 3B).

On average, no significant dose–response association was found for Instagram use (Model 4A): At moments when adolescents had used Instagram, the time adolescents spent using Instagram (either actively or passively) was not associated with their well-being. However, evidence was found that the association of the time spent passively using Instagram differed from adolescent to adolescent (Model 4B), with effect sizes ranging from − 0.48 to 0.27. For most adolescents (73.91%) the association was non-existent to small (− 0.10 <  r  < 0.10), but for almost one in five adolescents (17.39%) there was a weak (0.10 <  r  < 0.20; 10.87%) or moderate (0.20 <  r  < 0.30; 6.52%) positive association, and for almost one in ten adolescents (8.70%) there was a weak (− 0.20 <  r  < − 0.10; 2.17%), moderate (− 0.30 <  r  < − 0.20; 4.35%), or strong ( r  ≤ − 0.30; 2.17%) negative association. Figure  1 illustrates these differences in the dose–response associations.

figure 1

The dose–response association between passive Instagram use (in minutes per hour) and affective well-being for each individual adolescent (n = 46). Red lines represent significant negative within-person associations, green lines represent significant positive within-person associations, and gray lines represent non-significant within-person associations. A graph was created for each participant who had completed at least 10 assessments. A total of 13 participants were excluded because they had completed less than 10 assessments of passive Instagram use. In addition, one participant was excluded because no graph could be computed, since this participant's passive Instagram use was constant across assessments.

WhatsApp use

As shown in Model 5A in Table 4 , just as for Instagram, we found that, on average, there was a significant categorical association between passive (but not active) WhatsApp use and well-being: Adolescents reported that they felt better at moments when they had passively used WhatsApp (i.e., read WhatsApp messages). For active WhatsApp use, no significant association was found. Also, in line with the results for Instagram use, no differences were found regarding the associations of active and passive WhatsApp use (Model 5B).

In addition, a significant dose–response association was found for passive (but not active) use (Model 6A). At moments when adolescents had used WhatsApp, we found that, on average, the time adolescents spent passively using WhatsApp was positively associated with well-being: Adolescents felt better at moments when they had spent more time reading WhatsApp messages. The time spent actively using WhatsApp was not associated with well-being. No differences were found in the dose–response associations of active and passive WhatsApp use (Model 6B).

This preregistered study investigated adolescents’ unique susceptibility to the effects of social media. We found that the associations of passive (but not active) social media use with well-being differed substantially from adolescent to adolescent, with effect sizes ranging from moderately negative (− 0.24) to strongly positive (0.68). While 44.26% of adolescents did not feel better or worse if they had passively used social media, 45.90% felt better, and a small group felt worse (9.84%). In addition, for Instagram the majority of adolescents (73.91%) did not feel better or worse when they had spent more time viewing post or stories of others, whereas some felt better (17.39%), and others (8.70%) felt worse.

These findings have important implications for social media effects research, and media effects research more generally. For decades, researchers have argued that people differ in their susceptibility to the effects of media 17 , leading to numerous investigations of such differential susceptibility. These investigations have typically focused on moderators, based on variables such as sex, age, or personality. Yet, over the years, studies have shown that such moderators appear to have little power to explain how individuals differ in their susceptibility to media effects, probably because a group-differential approach does not account for the possibility that media users may differ across a range of factors, that are not captured by only one (or a few) investigated moderator variables.

By providing insights into each individual’s unique susceptibility, the findings of this study provide an explanation as to why, up until now, most media effects research has only found small effects. We found that the majority of adolescents do not experience any short-term changes in well-being related to their social media use. And if they do experience any changes, these are more often positive than negative. Because only small subsets of adolescents experience small to moderate changes in well-being, the true effects of social media reported in previous studies have probably been diluted across heterogeneous samples of individuals that differ in their susceptibility to media effects (also see 30 ). Several scholars have noted that overall effect sizes may mask more subtle individual differences 14 , 15 , which may explain why previous studies have typically reported small or no effects of social media on well-being or indicators of well-being 6 , 11 , 12 , 13 . The current study seems to confirm this assumption, by showing that while the overall effect sizes are small at best, the person-specific effect sizes vary considerably, from tiny and small to moderate and strong.

As called upon by other scholars 5 , 31 , we disentangled the associations of active and passive use of social media. Research among young adults found that passive (but not active) social media use is associated with lower levels of affective well-being 29 . In line with these findings, the current study shows that active and passive use yielded different associations with adolescents’ affective well-being. Interestingly though, in contrast to previous findings among adults, our study showed that, on average, passive use of Instagram and WhatsApp seemed to enhance rather than decrease adolescents’ well-being. This discrepancy in findings may be attributed to the fact that different mechanisms might be involved. Verduyn and colleagues 29 found that passive use of Facebook undermines adults’ well-being by enhancing envy, which may also explain the decreases in well-being found in our study among a small group of adolescents. Yet, adolescents who felt better by passively using Instagram and WhatsApp, might have felt so because they experienced enjoyment. After all, adolescents often seek positive content on social media, such as humorous posts or memes 32 . Also, research has shown that adolescents mainly receive positive feedback on social media 33 . Hence, their passive Instagram and WhatsApp use may involve the reading of positive feedback, which may explain the increases in well-being.

Overall, the time spent passively using WhatsApp improved adolescents’ well-being. This did not differ from adolescent to adolescent. However, the associations of the time spent passively using Instagram with well-being did differ from adolescent to adolescent. This discrepancy suggests that not all social media uses yield person-specific effects on well-being. A possible explanation may be that adolescents’ responses to WhatsApp are more homogenous than those to Instagram. WhatsApp is a more private platform, which is mostly used for one-to-one communication with friends and acquaintances 26 . Instagram, in contrast, is a more public platform, which allows its users to follow a diverse set of people, ranging from best friends to singers, actors, and influencers 28 , and to engage in intimate communication as well as self-presentation and social comparison. Such diverse uses could lead to more varied, or even opposing responses, such as envy versus inspiration.

Limitations and directions for future research

The current study extends our understanding of differential susceptibility to media effects, by revealing that the effect of social media use on well-being differs from adolescent to adolescent. The findings confirm our assumption that among the great majority of adolescents, social media use is unrelated to well-being, but that among a small subset, social media use is either related to decreases or increases in well-being. It must be noted, however, that participants in this study felt relatively happy, overall. Studies with more vulnerable samples, consisting of clinical samples or youth with lower social-emotional well-being may elicit different patterns of effects 27 . Also, the current study focused on affective well-being, operationalized as happiness. It is plausible that social media use relates differently with other types of well-being, such as cognitive well-being. An important next step is to identify which adolescents are particularly susceptible to experience declines in well-being. It is conceivable, for instance, that the few adolescents who feel worse when they use social media are the ones who receive negative feedback on social media 33 .

In addition, future ESM studies into the effects of social media should attempt to include one or more follow-up measures to improve our knowledge of the longer-term influence of social media use on affective well-being. While a week-long ESM is very common and applied in most earlier ESM studies 34 , a week is only a snapshot of adolescent development. Research is needed that investigates whether the associations of social media use with adolescents’ momentary affective well-being may cumulate into long-lasting consequences. Such investigations could help clarify whether adolescents who feel bad in the short term would experience more negative consequences in the long term, and whether adolescents who feel better would be more resistant to developing long-term negative consequences. And while most adolescents do not seem to experience any short-term increases or decreases in well-being, more research is needed to investigate whether these adolescents may experience a longer-term impact of social media.

While the use of different platforms may be differently associated with well-being, different types of use may also yield different effects. Although the current study distinguished between active and passive use of social media, future research should further differentiate between different activities. For instance, because passive use entails many different activities, from reading private messages (e.g., WhatsApp messages, direct messages on Instagram) to browsing a public feed (e.g., scrolling through posts on Instagram), research is needed that explores the unique effects of passive public use and passive private use. Research that seeks to explore the nuances in adolescents’ susceptibility as well as the nuances in their social media use may truly improve our understanding of the effects of social media use.

Participants

Participants were recruited via a secondary school in the south of the Netherlands. Our preregistered sampling plan set a target sample size of 100 adolescents. We invited adolescents from six classrooms to participate in the study. The final sample consisted of 63 adolescents (i.e., 42% consent rate, which is comparable to other ESM studies among adolescents; see, for instance 35 , 36 ). Informed consent was obtained from all participants and their parents. On average, participants were 15 years old ( M  = 15.12 years, SD  = 0.51) and 54% were girls. All participants self-identified as Dutch, and 41.3% were enrolled in the prevocational secondary education track, 25.4% in the intermediate general secondary education track, and 33.3% in the academic preparatory education track.

The study was approved by the Ethics Review Board of the Faculty of Social and Behavioral Sciences at the University of Amsterdam and was performed in accordance with the guidelines formulated by the Ethics Review Board. The study consisted of two phases: A baseline survey and a personalized week-long experience sampling (ESM) study. In phase 1, researchers visited the school during school hours. Researchers informed the participants of the objective and procedure of the study and assured them that their responses would be treated confidentially. Participants were asked to sign the consent form. Next, participants completed a 15-min baseline survey. The baseline survey included questions about demographics and assessed which social media each adolescent used most frequently, allowing to personalize the social media questions presented during the ESM study in phase 2. After completing the baseline survey, participants were provided detailed instructions about phase 2.

In phase 2, which took place two and a half weeks after the baseline survey, a 7-day ESM study was conducted, following the guidelines for ESM studies provided by van Roekel and colleagues 34 . Aiming for at least 30 assessments per participant and based on an average compliance rate of 70 to 80% reported in earlier ESM studies among adolescents 34 , we asked each participant to complete a total of 42 ESM surveys (i.e., six 2-min surveys per day). Participants completed the surveys using their own mobile phone, on which the ESM software application Ethica Data was installed during the instruction session with the researchers (phase 1). Each 2-min survey consisted of 22 questions, which assessed adolescents’ well-being and social media use. Two open-ended questions were added to the final survey of the day, which asked about adolescents’ most pleasant and most unpleasant events of the day.

The ESM sampling scheme was semi-random, to allow for randomization and avoid structural patterns in well-being, while taking into account that adolescents were not allowed to use their phone during school time. The Ethica Data app was programmed to generate six beep notifications per day at random time points within a fixed time interval that was tailored to the school’s schedule: before school time (1 beep), during school breaks (2 beeps), and after school time (3 beeps). During the weekend, the beeps were generated during the morning (1 beep), afternoon (3 beeps), and evening (2 beeps). To maximize compliance, a 30-min time window was provided to complete each survey. This time window was extended to one hour for the first survey (morning) and two hours for the final survey (evening) to account for travel time to school and time spent on evening activities. The average compliance rate was 83.2%. A total of 2,155 ESM assessments were collected: Participants completed an average of 34.83 surveys ( SD  = 4.91) on a total of 42 surveys, which is high compared to previous ESM studies among adolescents 34 .

The questions of the ESM study were personalized based on the responses to the baseline survey. During the ESM study, each participant reported on his/her use of three different social media platforms: WhatsApp and either Instagram, Snapchat, YouTube, and/or the chat function of games (i.e., the most popular social media platforms among adolescents 28 ). Questions about Instagram and WhatsApp use were only included if the participant had indicated in the baseline survey that s/he used these platforms at least once a week. If a participant had indicated that s/he used Instagram or WhatsApp (or both) less than once a week, s/he was asked to report on the use of Snapchat, YouTube, or the chat function of games, depending on what platform s/he used at least once a week. In addition to Instagram and WhatsApp, questions were asked about a third platform, that was selected based on how frequently the participant used Snapchat, YouTube, or the chat function of games (i.e., at least once a week). This resulted in five different combinations of three platforms: Instagram, WhatsApp, and Snapchat (47 participants); Instagram, WhatsApp, and YouTube (11 participants); Instagram, WhatsApp, and chatting via games (2 participants); WhatsApp, Snapchat, and YouTube (1 participant); and WhatsApp, YouTube, and chatting via games (2 participants).

Frequency of social media use

In the baseline survey, participants were asked to indicate how often they used and checked Instagram, WhatsApp, Snapchat, YouTube, and the chat function of games, using response options ranging from 1 ( never ) to 7 ( more than 12 times per day ). These platforms are the five most popular platforms among Dutch 14- and 15-year-olds 28 . Participants’ responses were used to select the three social media platforms that were assessed in the personalized ESM study.

Duration of social media use

In the ESM study, duration of active and passive social media use was measured by asking participants how much time in the past hour they had spent actively and passively using each of the three platforms that were included in the personalized ESM surveys. Response options ranged from 0 to 60 min , with 5-min intervals. To measure active Instagram use, participants indicated how much time in the past hour they had spent (a) “posting on your feed or sharing something in your story on Instagram” and (b) “sending direct messages/chatting on Instagram.” These two items were summed to create the variable duration of active Instagram use. Sum scores exceeding 60 min (only 0.52% of all assessments) were recoded to 60 min. To measure duration of passive Instagram use, participants indicated how much time in the past hour they had spent “viewing posts/stories of others on Instagram.” To measure the use of WhatsApp, Snapchat, YouTube and game-based chatting, we asked participants how much time they had spent “sending WhatsApp messages” (active use) and “reading WhatsApp messages” (passive use); “sending snaps/messages or sharing something in your story on Snapchat” (active use) and “viewing snaps/stories/messages from others on Snapchat” (passive use); “posting YouTube clips” (active use) and “watching YouTube clips” (passive use); “sending messages via the chat function of a game/games” (active use) and “reading messages via the chat function of a game/games” (passive use). Duration of active and passive overall social media use were created by summing the responses across the three social media platforms for active and passive use, respectively. Sum scores exceeding 60 min (2.13% of all assessments for active overall use; 2.90% for passive overall use) were recoded to 60 min. The duration variables were used to investigate whether the time spent actively or passively using social media was associated with well-being (dose–response associations).

Use/no use of social media

Based on the duration variables, we created six dummy variables, one for active and one for passive overall social media use, one for active and one for passive Instagram use, and one for active and one for passive WhatsApp use (0 =  no active use and 1 =  active use , and 0 =  no passive use and 1 =  passive use , respectively). These dummy variables were used to investigate whether the use of social media, irrespective of the duration of use, was associated with well-being (categorical associations).

Consistent with previous ESM studies 19 , 20 , we measured affective well-being using one item, asking “How happy do you feel right now?” at each assessment. Adolescents indicated their response to the question using a 7-point scale ranging from 1 ( not at all ) to 7 ( completely ), with 4 ( a little ) as the midpoint. Convergent validity of this item was established in a separate pilot ESM study among 30 adolescents conducted by the research team of the fourth author: The affective well-being item was strongly correlated with the presence of positive affect and absence of negative affect (assessed by a 10-item positive and negative affect schedule for children; PANAS-C) at both the between-person (positive affect: r  = 0.88, p < 0.001; negative affect: r  = − 0.62, p < 0.001) and within-person level (positive affect: r  = 0.74, p < 0.001; negative affect: r  = − 0.58, p < 0.001).

Statistical analyses

Before conducting the analyses, several validation checks were performed (see 34 ). First, we aimed to only include participants in the analyses who had completed more than 33% of all ESM assessments (i.e., at least 14 assessments). Next, we screened participants’ responses to the open questions for unserious responses (e.g., gross comments, jokes). And finally, we inspected time series plots for patterns in answering tendencies. Since all participants completed more than 33% of all ESM assessments, and no inappropriate responses or low-quality data patterns were detected, all participants were included in the analyses.

Following our preregistered analysis plan, we tested the proposed associations in a series of multilevel models. Before doing so, we tested the homoscedasticity and linearity assumptions for multilevel analyses 37 . Inspection of standardized residual plots indicated that the data met these assumptions (plots are available on OSF at  https://osf.io/nhks2 ). We specified separate models for overall social media use, use of Instagram, and use of WhatsApp. To investigate to what extent adolescents’ well-being would vary depending on whether they had actively or passively used social media/Instagram/WhatsApp or not during the past hour (categorical associations), we tested models including the dummy variables as predictors (active use versus no active use, and passive use versus no passive use; models 1, 3, and 5). To investigate whether, at moments when adolescents had used social media/Instagram/WhatsApp during the past hour, their well-being would vary depending on the duration of social media/Instagram/WhatsApp use (dose–response associations), we tested models including the duration variables as predictors (duration of active use and duration of passive use; models 2, 4, and 6). In order to avoid negative skew in the duration variables, we only included assessments during which adolescents had used social media in the past hour (overall, Instagram, or WhatsApp, respectively), either actively or passively. All models included well-being as outcome variable. Since multilevel analyses allow to include all available data for each individual, no missing data were imputed and no data points were excluded.

We used a model building approach that involved three steps. In the first step, we estimated an intercept-only model to assess the relative amount of between- and within-person variance in affective well-being. We estimated a three-level model in which repeated momentary assessments (level 1) were nested within adolescents (level 2), who, in turn, were nested within classrooms (level 3). However, because the between-classroom variance in affective well-being was small (i.e., 0.4% of the variance was explained by differences between classes), we proceeded with estimating two-level (instead of three-level) models, with repeated momentary assessments (level 1) nested within adolescents (level 2).

In the second step, we assessed the within-person associations of well-being with (a) overall active and passive social media use (i.e., the total of the three platforms), (b) active and passive use of Instagram, and (c) active and passive use of WhatsApp, by adding fixed effects to the model (Models 1A-6A). To facilitate the interpretation of the associations and control for the effects of time, a covariate was added that controlled for the n th assessment of the study week (instead of the n th assessment of the day, as preregistered). This so-called detrending is helpful to interpret within-person associations as correlated fluctuations beyond other changes in social media use and well-being 38 . In order to obtain within-person estimates, we person-mean centered all predictors 38 . Significance of the fixed effects was determined using the Wald test.

In the third and final step, we assessed heterogeneity in the within-person associations by adding random slopes to the models (Models 1B-6B). Significance of the random slopes was determined by comparing the fit of the fixed effects model with the fit of the random effects model, by performing the Satorra-Bentler scaled chi-square test 39 and by comparing the Bayesian information criterion (BIC 40 ) and Akaike information criterion (AIC 41 ) of the models. When the random effects model had a significantly better fit than the fixed effects model (i.e., pointing at significant heterogeneity), variance components were inspected to investigate whether heterogeneity existed in the association of either active or passive use. Next, when evidence was found for significant heterogeneity, we computed person-specific effect sizes, based on the random effect models, to investigate what percentages of adolescents experienced better well-being, worse well-being, and no changes in well-being. In line with Keijsers and colleagues 42 we only included participants who had completed at least 10 assessments. In addition, for the dose–response associations, we constructed graphical representations of the person-specific slopes, based on the person-specific effect sizes, using the xyplot function from the lattice package in R 43 .

Three improvements were made to our original preregistered plan. First, rather than estimating the models with multilevel modelling in R 43 , we ran the preregistered models in Mplus 44 . Mplus provides standardized estimates for the fixed effects models, which offers insight into the effect sizes. This allowed us to compare the relative strength of the associations of passive versus active use with well-being. Second, instead of using the maximum likelihood estimator, we used the maximum likelihood estimator with robust standard errors (MLR), which are robust to non-normality. Sensitivity tests, uploaded on OSF ( https://osf.io/nhks2 ), indicated that the results were almost identical across the two software packages and estimation approaches. Third, to improve the interpretation of the results and make the scales of the duration measures of social media use and well-being more comparable, we transformed the social media duration scores (0 to 60 min) into scales running from 0 to 6, so that an increase of 1 unit reflects 10 min of social media use. The model estimates were unaffected by this transformation.

Reporting summary

Further information on the research design is available in the Nature Research Reporting Summary linked to this article.

Data availability

The dataset generated and analysed during the current study is available in Figshare 45 . The preregistration of the design, sampling and analysis plan, and the analysis scripts used to analyse the data for this paper are available online on the Open Science Framework website ( https://osf.io/nhks2 ).

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Acknowledgements

This study was funded by the NWO Spinoza Prize and the Gravitation grant (NWO Grant 024.001.003; Consortium on Individual Development) awarded to P.M.V. by the Dutch Research Council (NWO). Additional funding was received from the VIDI grant (NWO VIDI Grant 452.17.011) awarded to L.K. by the Dutch Research Council (NWO). The authors would like to thank Savannah Boele (Tilburg University) for providing her pilot ESM results.

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I.B., J.L.P., I.I.v.D., L.K., and P.M.V. designed the study; I.B., J.L.P., and I.I.v.D. collected the data; I.B., J.L.P., and L.K. analyzed the data; and I.B., J.L.P., I.I.v.D., L.K., and P.M.V. contributed to writing and reviewing the manuscript.

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Beyens, I., Pouwels, J.L., van Driel, I.I. et al. The effect of social media on well-being differs from adolescent to adolescent. Sci Rep 10 , 10763 (2020). https://doi.org/10.1038/s41598-020-67727-7

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Research Article

Social impact in social media: A new method to evaluate the social impact of research

Roles Investigation, Writing – original draft

* E-mail: [email protected]

Affiliation Department of Journalism and Communication Studies, Universitat Autonoma de Barcelona, Barcelona, Spain

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Affiliation Department of Psychology and Sociology, Universidad de Zaragoza, Zaragoza, Spain

Roles Conceptualization, Investigation, Methodology, Supervision, Writing – review & editing

Affiliation Department of Sociology, Universitat Autonoma de Barcelona, Barcelona, Spain

Affiliation Department of Sociology, Universitat de Barcelona (UB), Barcelona, Spain

  • Cristina M. Pulido, 
  • Gisela Redondo-Sama, 
  • Teresa Sordé-Martí, 
  • Ramon Flecha

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  • Published: August 29, 2018
  • https://doi.org/10.1371/journal.pone.0203117
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Table 1

The social impact of research has usually been analysed through the scientific outcomes produced under the auspices of the research. The growth of scholarly content in social media and the use of altmetrics by researchers to track their work facilitate the advancement in evaluating the impact of research. However, there is a gap in the identification of evidence of the social impact in terms of what citizens are sharing on their social media platforms. This article applies a social impact in social media methodology (SISM) to identify quantitative and qualitative evidence of the potential or real social impact of research shared on social media, specifically on Twitter and Facebook. We define the social impact coverage ratio (SICOR) to identify the percentage of tweets and Facebook posts providing information about potential or actual social impact in relation to the total amount of social media data found related to specific research projects. We selected 10 projects in different fields of knowledge to calculate the SICOR, and the results indicate that 0.43% of the tweets and Facebook posts collected provide linkages with information about social impact. However, our analysis indicates that some projects have a high percentage (4.98%) and others have no evidence of social impact shared in social media. Examples of quantitative and qualitative evidence of social impact are provided to illustrate these results. A general finding is that novel evidences of social impact of research can be found in social media, becoming relevant platforms for scientists to spread quantitative and qualitative evidence of social impact in social media to capture the interest of citizens. Thus, social media users are showed to be intermediaries making visible and assessing evidence of social impact.

Citation: Pulido CM, Redondo-Sama G, Sordé-Martí T, Flecha R (2018) Social impact in social media: A new method to evaluate the social impact of research. PLoS ONE 13(8): e0203117. https://doi.org/10.1371/journal.pone.0203117

Editor: Sergi Lozano, Institut Català de Paleoecologia Humana i Evolució Social (IPHES), SPAIN

Received: November 8, 2017; Accepted: August 15, 2018; Published: August 29, 2018

Copyright: © 2018 Pulido et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the paper and its Supporting Information files.

Funding: The research leading to these results has received funding from the 7th Framework Programme of the European Commission under the Grant Agreement n° 613202 P.I. Ramon Flecha, https://ec.europa.eu/research/fp7/index_en.cfm . The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

The social impact of research is at the core of some of the debates influencing how scientists develop their studies and how useful results for citizens and societies may be obtained. Concrete strategies to achieve social impact in particular research projects are related to a broader understanding of the role of science in contemporary society. There is a need to explore dialogues between science and society not only to communicate and disseminate science but also to achieve social improvements generated by science. Thus, the social impact of research emerges as an increasing concern within the scientific community [ 1 ]. As Bornmann [ 2 ] said, the assessment of this type of impact is badly needed and is more difficult than the measurement of scientific impact; for this reason, it is urgent to advance in the methodologies and approaches to measuring the social impact of research.

Several authors have approached the conceptualization of social impact, observing a lack of generally accepted conceptual and instrumental frameworks [ 3 ]. It is common to find a wide range of topics included in the contributions about social impact. In their analysis of the policies affecting land use, Hemling et al. [ 4 ] considered various domains in social impact, for instance, agricultural employment or health risk. Moving to the field of flora and fauna, Wilder and Walpole [ 5 ] studied the social impact of conservation projects, focusing on qualitative stories that provided information about changes in attitudes, behaviour, wellbeing and livelihoods. In an extensive study by Godin and Dore [ 6 ], the authors provided an overview and framework for the assessment of the contribution of science to society. They identified indicators of the impact of science, mentioning some of the most relevant weaknesses and developing a typology of impact that includes eleven dimensions, with one of them being the impact on society. The subdimensions of the impact of science on society focus on individuals (wellbeing and quality of life, social implication and practices) and organizations (speeches, interventions and actions). For the authors, social impact “refers to the impact knowledge has on welfare, and on the behaviours, practices and activities of people and groups” (p. 7).

In addition, the terms “social impact” and “societal impact” are sometimes used interchangeably. For instance, Bornmann [ 2 ] said that due to the difficulty of distinguishing social benefits from the superior term of societal benefits, “in much literature the term ‘social impact’ is used instead of ‘societal impact’”(p. 218). However, in other cases, the distinction is made [ 3 ], as in the present research. Similar to the definition used by the European Commission [ 7 ], social impact is used to refer to economic impact, societal impact, environmental impact and, additionally, human rights impact. Therefore, we use the term social impact as the broader concept that includes social improvements in all the above mentioned areas obtained from the transference of research results and representing positive steps towards the fulfilment of those officially defined social goals, including the UN Sustainable Development Goals, the EU 2020 Agenda, or similar official targets. For instance, the Europe 2020 strategy defines five priority targets with concrete indicators (employment, research and development, climate change and energy, education and poverty and social exclusion) [ 8 ], and we consider the targets addressed by objectives defined in the specific call that funds the research project.

This understanding of the social impact of research is connected to the creation of the Social Impact Open Repository (SIOR), which constitutes the first open repository worldwide that displays, cites and stores the social impact of research results [ 9 ]. The SIOR has linked to ORCID and Wikipedia to allow the synergies of spreading information about the social impact of research through diverse channels and audiences. It is relevant to mention that currently, SIOR includes evidence of real social impact, which implies that the research results have led to actual improvements in society. However, it is common to find evidence of potential social impact in research projects. The potential social impact implies that in the development of the research, there has been some evidence of the effectiveness of the research results in terms of social impact, but the results have not yet been transferred.

Additionally, a common confusion is found among the uses of dissemination, transference (policy impact) and social impact. While dissemination means to disseminate the knowledge created by research to citizens, companies and institutions, transference refers to the use of this knowledge by these different actors (or others), and finally, as already mentioned, social impact refers to the actual improvements resulting from the use of this knowledge in relation to the goals motivating the research project (such as the United Nations Sustainable Development Goals). In the present research [ 3 ], it is argued that “social impact can be understood as the culmination of the prior three stages of the research” (p.3). Therefore, this study builds on previous contributions measuring the dissemination and transference of research and goes beyond to propose a novel methodological approach to track social impact evidences.

In fact, the contribution that we develop in this article is based on the creation of a new method to evaluate the evidence of social impact shared in social media. The evaluation proposed is to measure the social impact coverage ratio (SICOR), focusing on the presence of evidence of social impact shared in social media. Then, the article first presents some of the contributions from the literature review focused on the research on social media as a source for obtaining key data for monitoring or evaluating different research purposes. Second, the SISM (social impact through social media) methodology[ 10 ] developed is introduced in detail. This methodology identifies quantitative and qualitative evidence of the social impact of the research shared on social media, specifically on Twitter and Facebook, and defines the SICOR, the social impact coverage ratio. Next, the results are discussed, and lastly, the main conclusions and further steps are presented.

Literature review

Social media research includes the analysis of citizens’ voices on a wide range of topics [ 11 ]. According to quantitative data from April 2017 published by Statista [ 12 ], Twitter and Facebook are included in the top ten leading social networks worldwide, as ranked by the number of active users. Facebook is at the top of the list, with 1,968 million active users, and Twitter ranks 10 th , with 319 million active users. Between them are the following social networks: WhatsApp, YouTube, Facebook Messenger, WeChat, QQ, Instagram,Qzone and Tumblr. If we look at altmetrics, the tracking of social networks for mentions of research outputs includes Facebook, Twitter, Google+,LinkedIn, Sina Weibo and Pinterest. The social networks common to both sources are Facebook and Twitter. These are also popular platforms that have a relevant coverage of scientific content and easy access to data, and therefore, the research projects selected here for application of the SISM methodology were chosen on these platforms.

Chew and Eysenbach [ 13 ] studied the presence of selected keywords in Twitter related to public health issues, particularly during the 2009 H1N1 pandemic, identifying the potential for health authorities to use social media to respond to the concerns and needs of society. Crooks et al.[ 14 ] investigated Twitter activity in the context of a 5.8 magnitude earthquake in 2011 on the East Coast of the United States, concluding that social media content can be useful for event monitoring and can complement other sources of data to improve the understanding of people’s responses to such events. Conversations among young Canadians posted on Facebook and analysed by Martinello and Donelle [ 15 ] revealed housing and transportation as main environmental concerns, and the project FoodRisc examined the role of social media to illustrate consumers’ quick responses during food crisis situations [ 16 ]. These types of contributions illustrate that social media research implies the understanding of citizens’ concerns in different fields, including in relation to science.

Research on the synergies between science and citizens has increased over the years, according to Fresco [ 17 ], and there is a growing interest among researchers and funding agencies in how to facilitate communication channels to spread scientific results. For instance, in 1998, Lubchenco [ 18 ] advocated for a social contract that “represents a commitment on the part of all scientists to devote their energies and talents to the most pressing problems of the day, in proportion to their importance, in exchange for public funding”(p.491).

In this framework, the recent debates on how to increase the impact of research have acquired relevance in all fields of knowledge, and major developments address the methods for measuring it. As highlighted by Feng Xia et al. [ 19 ], social media constitute an emerging approach to evaluating the impact of scholarly publications, and it is relevant to consider the influence of the journal, discipline, publication year and user type. The authors revealed that people’s concerns differ by discipline and observed more interest in papers related to everyday life, biology, and earth and environmental sciences. In the field of biomedical sciences, Haustein et al. [ 20 ] analysed the dissemination of journal articles on Twitter to explore the correlations between tweets and citations and proposed a framework to evaluate social media-based metrics. In fact, different studies address the relationship between the presence of articles on social networks and citations [ 21 ]. Bornmann [ 22 ] conducted a case study using a sample of 1,082 PLOS journal articles recommended in F1000 to explore the usefulness of altmetrics for measuring the broader impact of research. The author presents evidence about Facebook and Twitter as social networks that may indicate which papers in the biomedical sciences can be of interest to broader audiences, not just to specialists in the area. One aspect of particular interest resulting from this contribution is the potential to use altmetrics to measure the broader impacts of research, including the societal impact. However, most of the studies investigating social or societal impact lack a conceptualization underlying its measurement.

To the best of our knowledge, the assessment of social impact in social media (SISM) has developed according to this gap. At the core of this study, we present and discuss the results obtained through the application of the SICOR (social impact coverage ratio) with examples of evidence of social impact shared in social media, particularly on Twitter and Facebook, and the implications for further research.

Following these previous contributions, our research questions were as follows: Is there evidence of social impact of research shared by citizens in social media? If so, is there quantitative or qualitative evidence? How can social media contribute to identifying the social impact of research?

Methods and data presentation

A group of new methodologies related to the analysis of online data has recently emerged. One of these emerging methodologies is social media analytics [ 23 ], which was initially used most in the marketing research field but also came to be used in other domains due to the multiple possibilities opened up by the availability and richness of the data for different research purposes. Likewise, the concern of how to evaluate the social impact of research as well as the development of methodologies for addressing this concern has occupied central attention. The development of SISM (Social Impact in Social Media) and the application of the SICOR (Social Impact Coverage Ratio) is a contribution to advancement in the evaluation of the social impact of research through the analysis of the social media selected (in this case, Twitter and Facebook). Thus, SISM is novel in both social media analytics and among the methodologies used to evaluate the social impact of research. This development has been made under IMPACT-EV, a research project funded under the Framework Program FP7 of the Directorate-General for Research and Innovation of the European Commission. The main difference from other methodologies for measuring the social impact of research is the disentanglement between dissemination and social impact. While altmetrics is aimed at measuring research results disseminated beyond academic and specialized spheres, SISM contribute to advancing this measurement by shedding light on to what extent evidence of the social impact of research is found in social media data. This involves the need to differentiate between tweets or Facebook posts (Fb/posts) used to disseminate research findings from those used to share the social impact of research. We focus on the latter, investigating whether there is evidence of social impact, including both potential and real social impact. In fact, the question is whether research contributes and/or has the potential to contribute to improve the society or living conditions considering one of these goals defined. What is the evidence? Next, we detail the application of the methodology.

Data collection

To develop this study, the first step was to select research projects with social media data to be analysed. The selection of research projects for application of the SISM methodology was performed according to three criteria.

Criteria 1. Selection of success projects in FP7. The projects were success stories of the 7 th Framework Programme (FP7) highlighted by the European Commission [ 24 ] in the fields of knowledge of medicine, public health, biology and genomics. The FP7 published calls for project proposals from 2007 to 2013. This implies that most of the projects funded in the last period of the FP7 (2012 and 2013) are finalized or in the last phase of implementation.

Criteria 2. Period of implementation. We selected projects in the 2012–2013 period because they combine recent research results with higher possibilities of having Twitter and Facebook accounts compared with projects of previous years, as the presence of social accounts in research increased over this period.

Criteria 3. Twitter and Facebook accounts. It was crucial that the selected projects had active Twitter and Facebook accounts.

Table 1 summarizes the criteria and the final number of projects identified. As shown, 10 projects met the defined criteria. Projects in medical research and public health had higher presence.

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After the selection of projects, we defined the timeframe of social media data extraction on Twitter and Facebook from the starting date of the project until the day of the search, as presented in Table 2 .

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The second step was to define the search strategies for extracting social media data related to the research projects selected. In this line, we defined three search strategies.

Strategy 1. To extract messages published on the Twitter account and the Facebook page of the selected projects. We listed the Twitter accounts and Facebook pages related to each project in order to look at the available information. In this case, it is important to clarify that the tweets published under the corresponding Twitter project account are original tweets or retweets made from this account. It is relevant to mention that in one case, the Twitter account and Facebook page were linked to the website of the research group leading the project. In this case, we selected tweets and Facebook posts related to the project. For instance, in the case of the Twitter account, the research group created a specific hashtag to publish messages related to the project; therefore, we selected only the tweets published under this hashtag. In the analysis, we prioritized the analysis of the tweets and Facebook posts that received some type of interaction (likes, retweets or shares) because such interaction is a proxy for citizens’ interest. In doing so, we used the R program and NVivoto extract the data and proceed with the analysis. Once we obtained the data from Twitter and Facebook, we were able to have an overview of the information to be further analysed, as shown in Table 3 .

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We focused the second and third strategies on Twitter data. In both strategies, we extracted Twitter data directly from the Twitter Advanced Search tool, as the API connected to NVivo and the R program covers only a specific period of time limited to 7/9 days. Therefore, the use of the Twitter Advanced Search tool made it possible to obtain historic data without a period limitation. We downloaded the results in PDF and then uploaded them to NVivo.

Strategy 2. To use the project acronym combined with other keywords, such as FP7 or EU. This strategy made it possible to obtain tweets mentioning the project. Table 4 presents the number of tweets obtained with this strategy.

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Strategy 3. To use searchable research results of projects to obtain Twitter data. We defined a list of research results, one for each project, and converted them into keywords. We selected one searchable keyword for each project from its website or other relevant sources, for instance, the brief presentations prepared by the European Commission and published in CORDIS. Once we had the searchable research results, we used the Twitter Advanced Search tool to obtain tweets, as presented in Table 5 .

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The sum of the data obtained from these three strategies allowed us to obtain a total of 3,425 tweets and 1,925 posts on public Facebook pages. Table 6 presents a summary of the results.

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We imported the data obtained from the three search strategies into NVivo to analyse. Next, we select tweets and Facebook posts providing linkages with quantitative or qualitative evidence of social impact, and we complied with the terms of service for the social media from which the data were collected. By quantitative and qualitative evidence, we mean data or information that shows how the implementation of research results has led to improvements towards the fulfilment of the objectives defined in the EU2020 strategy of the European Commission or other official targets. For instance, in the case of quantitative evidence, we searched tweets and Facebook posts providing linkages with quantitative information about improvements obtained through the implementation of the research results of the project. In relation to qualitative evidence, for example, we searched for testimonies that show a positive evaluation of the improvement due to the implementation of research results. In relation to this step, it is important to highlight that social media users are intermediaries making visible evidence of social impact. Users often share evidence, sometimes sharing a link to an external resource (e.g., a video, an official report, a scientific article, news published on media). We identified evidence of social impact in these sources.

Data analysis

impact of social media research paper

γ i is the total number of messages obtained about project i with evidence of social impact on social media platforms (Twitter, Facebook, Instagram, etc.);

T i is the total number of messages from project i on social media platforms (Twitter, Facebook, Instagram, etc.); and

n is the number of projects selected.

impact of social media research paper

Analytical categories and codebook

The researchers who carried out the analysis of the social media data collected are specialists in the social impact of research and research on social media. Before conducting the full analysis, two aspects were guaranteed. First, how to identify evidence of social impact relating to the targets defined by the EU2020 strategy or to specific goals defined by the call addressed was clarified. Second, we held a pilot to test the methodology with one research project that we know has led to considerable social impact, which allowed us to clarify whether or not it was possible to detect evidence of social impact shared in social media. Once the pilot showed positive results, the next step was to extend the analysis to another set of projects and finally to the whole sample. The construction of the analytical categories was defined a priori, revised accordingly and lastly applied to the full sample.

Different observations should be made. First, in this previous analysis, we found that the tweets and Facebook users play a key role as “intermediaries,” serving as bridges between the larger public and the evidence of social impact. Social media users usually share a quote or paragraph introducing evidence of social impact and/or link to an external resource, for instance, a video, official report, scientific article, news story published on media, etc., where evidence of the social impact is available. This fact has implications for our study, as our unit of analysis is all the information included in the tweets or Facebook posts. This means that our analysis reaches the external resources linked to find evidence of social impact, and for this reason, we defined tweets or Facebook posts providing linkages with information about social impact.

Second, the other important aspect is the analysis of the users’ profile descriptions, which requires much more development in future research given the existing limitations. For instance, some profiles are users’ restricted due to privacy reasons, so the information is not available; other accounts have only the name of the user with no description of their profile available. Therefore, we gave priority to the identification of evidence of social impact including whether a post obtained interaction (retweets, likes or shares) or was published on accounts other than that of the research project itself. In the case of the profile analysis, we added only an exploratory preliminary result because this requires further development. Considering all these previous details, the codebook (see Table 7 ) that we present as follows is a result of this previous research.

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https://doi.org/10.1371/journal.pone.0203117.t007

How to analyse Twitter and Facebook data

To illustrate how we analysed data from Twitter and Facebook, we provide one example of each type of evidence of social impact defined, considering both real and potential social impact, with the type of interaction obtained and the profiles of those who have interacted.

QUANESISM. Tweet by ZeroHunger Challenge @ZeroHunger published on 3 May 2016. Text: How re-using food waste for animal feed cuts carbon emissions.-NOSHAN project hubs.ly/H02SmrP0. 7 retweets and 5 likes.

The unit of analysis is all the content of the tweet, including the external link. If we limited our analysis to the tweet itself, it would not be evidence. Examining the external link is necessary to find whether there is evidence of social impact. The aim of this project was to investigate the process and technologies needed to use food waste for feed production at low cost, with low energy consumption and with a maximal evaluation of the starting wastes. This tweet provides a link to news published in the PHYS.org portal [ 25 ], which specializes in science news. The news story includes an interview with the main researcher that provides the following quotation with quantitative evidence:

'Our results demonstrated that with a NOSHAN 10 percent mix diet, for every kilogram of broiler chicken feed, carbon dioxide emissions were reduced by 0.3 kg compared to a non-food waste diet,' explains Montse Jorba, NOSHAN project coordinator. 'If 1 percent of total chicken broiler feed in Europe was switched to the 10 percent NOSHAN mix diet, the total amount of CO2 emissions avoided would be 0.62 million tons each year.'[ 25 ]

This quantitative evidence “a NOSHAN 10 percent mix diet, for every kilogram of broiler chicken feed, carbon dioxide emissions carbon dioxide emissions were reduced by 0.3 kg to a non-food waste diet” is linked directly with the Europe 2020 target of Climate Change & Energy, specifically with the target of reducing greenhouse gas emissions by 20% compared to the levels in 1990 [ 8 ]. The illustrative extrapolation the coordinator mentioned in the news is also an example of quantitative evidence, although is an extrapolation based on the specific research result.

This tweet was captured by the Acronym search strategy. It is a message tweeted by an account that is not related to the research project. The twitter account is that of the Zero Hunger Challenge movement, which supports the goals of the UN. The interaction obtained is 7 retweets and 5 likes. Regarding the profiles of those who retweeted and clicked “like”, there were activists, a journalist, an eco-friendly citizen, a global news service, restricted profiles (no information is available on those who have retweeted) and one account with no information in its profile.

The following example illustrates the analysis of QUALESISM: Tweet by @eurofitFP7 published on4 October 2016. Text: See our great new EuroFIT video on youtube! https://t.co/TocQwMiW3c 9 retweets and 5 likes.

The aim of this project is to improve health through the implementation of two novel technologies to achieve a healthier lifestyle. The tweet provides a link to a video on YouTube on the project’s results. In this video, we found qualitative evidence from people who tested the EuroFit programme; there are quotes from men who said that they have experienced improved health results using this method and that they are more aware of how to manage their health:

One end-user said: I have really amazing results from the start, because I managed to change a lot of things in my life. And other one: I was more conscious of what I ate, I was more conscious of taking more steps throughout the day and also standing up a little more. [ 26 ]

The research applies the well researched scientific evidence to the management of health issues in daily life. The video presents the research but also includes a section where end-users talk about the health improvements they experienced. The quotes extracted are some examples of the testimonies collected. All agree that they have improved their health and learned healthy habits for their daily lives. These are examples of qualitative evidence linked with the target of the call HEALTH.2013.3.3–1—Social innovation for health promotion [ 27 ] that has the objectives of reducing sedentary habits in the population and promoting healthy habits. This research contributes to this target, as we see in the video testimonies. Regarding the interaction obtained, this tweet achieved 9 retweets and 5 likes. In this case, the profiles of the interacting citizens show involvement in sport issues, including sport trainers, sport enthusiasts and some researchers.

To summarize the analysis, in Table 8 below, we provide a summary with examples illustrating the evidence found.

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Quantitative evidence of social impact in social media

There is a greater presence of tweets/Fb posts with quantitative evidence (14) than with qualitative evidence (9) in the total number of tweets/Fb posts identified with evidence of social impact. Most of the tweets/Fb posts with quantitative evidence of social impact are from scientific articles published in peer-reviewed international journals and show potential social impact. In Table 8 , we introduce 3 examples of this type of tweets/Fb posts with quantitative evidence:

The first tweet with quantitative social impact selected is from project 7. The aim of this project was to provide high-quality scientific evidence for preventing vitamin D deficiency in European citizens. The tweet highlighted the main contribution of the published study, that is, “Weekly consumption of 7 vitamin D-enhanced eggs has an important impact on winter vitamin D status in adults” [ 28 ]. The quantitative evidence shared in social media was extracted from a news publication in a blog on health news. This blog collects scientific articles of research results. In this case, the blog disseminated the research result focused on how vitamin D-enhanced eggs improve vitamin D deficiency in wintertime, with the published results obtained by the research team of the project selected. The quantitative evidence illustrates that the group of adults who consumed vitamin D-enhanced eggs did not suffer from vitamin D deficiency, as opposed to the control group, which showed a significant decrease in vitamin D over the winter. The specific evidence is the following extracted from the article [ 28 ]:

With the use of a within-group analysis, it was shown that, although serum 25(OH) D in the control group significantly decreased over winter (mean ± SD: -6.4 ± 6.7 nmol/L; P = 0.001), there was no change in the 2 groups who consumed vitamin D-enhanced eggs (P>0.1 for both. (p. 629)

This evidence contributes to achievement of the target defined in the call addressed that is KBBE.2013.2.2–03—Food-based solutions for the eradication of vitamin D deficiency and health promotion throughout the life cycle [ 29 ]. The quantitative evidence shows how the consumption of vitamin D-enhanced eggs reduces vitamin D deficiency.

The second example of this table corresponds to the example of quantitative evidence of social impact provided in the previous section.

The third example is a Facebook post from project 3 that is also tweeted. Therefore, this evidence was published in both social media sources analysed. The aim of this project was to measure a range of chemical and physical environmental hazards in food, consumer products, water, air, noise, and the built environment in the pre- and postnatal early-life periods. This Facebook post and tweet links directly to a scientific article [ 30 ] that shows the precision of the spectroscopic platform:

Using 1H NMR spectroscopy we characterized short-term variability in urinary metabolites measured from 20 children aged 8–9 years old. Daily spot morning, night-time and pooled (50:50 morning and night-time) urine samples across six days (18 samples per child) were analysed, and 44 metabolites quantified. Intraclass correlation coefficients (ICC) and mixed effect models were applied to assess the reproducibility and biological variance of metabolic phenotypes. Excellent analytical reproducibility and precision was demonstrated for the 1H NMR spectroscopic platform (median CV 7.2%) . (p.1)

This evidence is linked to the target defined in the call “ENV.2012.6.4–3—Integrating environmental and health data to advance knowledge of the role of environment in human health and well-being in support of a European exposome initiative” [ 31 ]. The evidence provided shows how the project’s results have contributed to building technology for improving the data collection to advance in the knowledge of the role of the environment in human health, especially in early life. The interaction obtained is one retweet from a citizen from Nigeria interested in health issues, according to the information available in his profile.

Qualitative evidence of social impact in social media

We found qualitative evidence of the social impact of different projects, as shown in Table 9 . Similarly to the quantitative evidence, the qualitative cases also demonstrate potential social impact. The three examples provided have in common that they are tweets or Facebook posts that link to videos where the end users of the research project explain their improvements once they have implemented the research results.

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The first tweet with qualitative evidence selected is from project 4. The aim of this project is to produce a system that helps in the prevention of obesity and eating disorders, targeting young people and adults [ 32 ]. The twitter account that published this tweet is that of the Future and Emerging Technologies Programme of the European Commission, and a link to a Euronews video is provided. This video shows how the patients using the technology developed in the research achieved control of their eating disorders, through the testimonies of patients commenting on the positive results they have obtained. These testimonies are included in the news article that complements the video. An example of these testimonies is as follows:

Pierre Vial has lost 43 kilos over the past nine and a half months. He and other patients at the eating disorder clinic explain the effects obesity and anorexia have had on their lives. Another patient, Karin Borell, still has some months to go at the clinic but, after decades of battling anorexia, is beginning to be able to visualise life without the illness: “On a good day I see myself living a normal life without an eating disorder, without problems with food. That’s really all I wish right now”.[ 32 ]

This qualitative evidence shows how the research results contribute to the achievement of the target goals of the call addressed:“ICT-2013.5.1—Personalised health, active ageing, and independent living”. [ 33 ] In this case, the results are robust, particularly for people suffering chronic diseases and desiring to improve their health; people who have applied the research findings are improving their eating disorders and better managing their health. The value of this evidence is the inclusion of the patients’ voices stating the impact of the research results on their health.

The second example is a Facebook post from project 9, which provides a link to a Euronews video. The aim of this project is to bring some tools from the lab to the farm in order to guarantee a better management of the farm and animal welfare. In this video [ 34 ], there are quotes from farmers using the new system developed through the research results of the project. These quotes show how use of the new system is improving the management of the farm and the health of the animals; some examples are provided:

Cameras and microphones help me detect in real time when the animals are stressed for whatever reason,” explained farmer Twan Colberts. “So I can find solutions faster and in more efficient ways, without me being constantly here, checking each animal.”

This evidence shows how the research results contribute to addressing the objectives specified in the call “KBBE.2012.1.1–02—Animal and farm-centric approach to precision livestock farming in Europe” [ 29 ], particularly, to improve the precision of livestock farming in Europe. The interaction obtained is composed of6 likes and 1 share. The profiles are diverse, but some of them do not disclose personal information; others have not added a profile description, and only their name and photo are available.

Interrater reliability (kappa)

The analysis of tweets and Facebook posts providing linkages with information about social impact was conducted following a content analysis method in which reliability was based on a peer review process. This sample is composed of 3,425 tweets and 1,925 Fb/posts. Each tweet and Facebook post was analysed to identify whether or not it contains evidence of social impact. Each researcher has the codebook a priori. We used interrater reliability in examining the agreement between the two raters on the assignment of the categories defined through Cohen’s kappa. We used SPSS to calculate this coefficient. We exported an excel sheet with the sample coded by the two researchers being 1 (is evidence of social impact, either potential or real) and 0 (is not evidence of social impact) to SPSS. The cases where agreement was not achieved were not considered as containing evidence of social impact. The result obtained is 0.979; considering the interpretation of this number according to Landis & Koch [ 35 ], our level of agreement is almost perfect, and thus, our analysis is reliable. To sum up the data analysis, the description of the steps followed is explained:

Step 1. Data analysis I. We included all data collected in an excel sheet to proceed with the analysis. Prior to the analysis, researchers read the codebook to keep in mind the information that should be identified.

Step 2. Each researcher involved reviewed case by case the tweets and Facebook posts to identify whether they provide links with evidence of social impact or not. If the researcher considers there to be evidence of social impact, he or she introduces the value of 1into the column, and if not, the value of 0.

Step 3. Once all the researchers have finished this step, the next step is to export the excel sheet to SPSS to extract the kappa coefficient.

Step 4. Data Analysis II. The following step was to analyse case by case the tweets and Facebook posts identified as providing linkages with information of social impact and classify them as quantitative or qualitative evidence of social impact.

Step 5. The interaction received was analysed because this determines to which extent this evidence of social impact has captured the attention of citizens (in the form of how many likes, shares, or retweets the post has).

Step 6. Finally, if available, the profile descriptions of the citizens interacting through retweeting or sharing the Facebook post were considered.

Step 7. SICOR was calculated. It could be applied to the complete sample (all data projects) or to each project, as we will see in the next section.

The total number of tweets and Fb/posts collected from the 10 projects is 5,350. After the content analysis, we identified 23 tweets and Facebook posts providing linkages to information about social impact. To respond to the research question, which considered whether there is evidence of social impact shared by citizens in social media, the answer was affirmative, although the coverage ratio is low. Both Twitter and Facebook users retweeted or shared evidence of social impact, and therefore, these two social media networks are valid sources for expanding knowledge on the assessment of social impact. Table 10 shows the social impact coverage ratio in relation to the total number of messages analysed.

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The analysis of each of the projects selected revealed some results to consider. Of the 10 projects, 7 had evidence, but those projects did not necessarily have more Tweets and Facebook posts. In fact, some projects with fewer than 70 tweets and 50 Facebook posts have more evidence of social impact than other projects with more than 400 tweets and 400 Facebook posts. This result indicates that the number of tweets and Facebook posts does not determine the existence of evidence of social impact in social media. For example, project 2 has 403 tweets and 423 Facebooks posts, but it has no evidence of social impact on social media. In contrast, project 9 has 62 tweets, 43 Facebook posts, and 2 pieces of evidence of social impact in social media, as shown in Table 11 .

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The ratio of tweets/Fb posts to evidence is 0.43%, and it differs depending on the project, as shown below in Table 12 . There is one project (P7) with a ratio of 4.98%, which is a social impact coverage ratio higher than that of the other projects. Next, a group of projects (P3, P9, P10) has a social impact coverage ratio between 1.41% and 2,99%.The next slot has three projects (P1, P4, P5), with a ratio between 0.13% and 0.46%. Finally, there are three projects (P2, P6, P8) without any tweets/Fb posts evidence of social impact.

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Considering the three strategies for obtaining data, each is related differently to the evidence of social impact. In terms of the social impact coverage ratio, as shown in Table 13 , the most successful strategy is number 3 (searchable research results), as it has a relation of 17.86%, which is much higher than the ratios for the other 2 strategies. The second strategy (acronym search) is more effective than the first (profile accounts),with 1.77% for the former as opposed to 0.27% for the latter.

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https://doi.org/10.1371/journal.pone.0203117.t013

Once tweets and Facebook posts providing linkages with information about social impact(ESISM)were identified, we classified them in terms of quantitative (QUANESISM) or qualitative evidence (QUALESISM)to determine which type of evidence was shared in social media. Table 14 indicates the amount of quantitative and qualitative evidence identified for each search strategy.

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https://doi.org/10.1371/journal.pone.0203117.t014

First, the results obtained indicated that the SISM methodology aids in calculating the social impact coverage ratio of the research projects selected and evaluating whether the social impact of the corresponding research is shared by citizens in social media. The social impact coverage ratio applied to the sample selected is low, but when we analyse the SICOR of each project separately, we can observe that some projects have a higher social impact coverage ratio than others. Complementary to altmetrics measuring the extent to which research results reach out society, the SICOR considers the question whether this process includes evidence of potential or real social impact. In this sense, the overall methodology of SISM contributes to advancement in the evaluation of the social impact of research by providing a more precise approach to what we are evaluating.

This contribution complements current evaluation methodologies of social impact that consider which improvements are shared by citizens in social media. Exploring the results in more depth, it is relevant to highlight that of the ten projects selected, there is one research project with a social impact coverage ratio higher than those of the others, which include projects without any tweets or Facebook posts with evidence of social impact. This project has a higher ratio of evidence than the others because evidence of its social impact is shared more than is that of other projects. This also means that the researchers produced evidence of social impact and shared it during the project. Another relevant result is that the quantity of tweets and Fb/posts collected did not determine the number of tweets and Fb/posts found with evidence of social impact. Moreover, the analysis of the research projects selected showed that there are projects with less social media interaction but with more tweets and Fb/posts containing evidence of social media impact. Thus, the number of tweets and Fb/posts with evidence of social impact is not determined by the number of publication messages collected; it is determined by the type of messages published and shared, that is, whether they contain evidence of social impact or not.

The second main finding is related to the effectiveness of the search strategies defined. Related to the strategies carried out under this methodology, one of the results found is that the most effective search strategy is the searchable research results, which reveals a higher percentage of evidence of social impact than the own account and acronym search strategies. However, the use of these three search strategies is highly recommended because the combination of all of them makes it possible to identify more tweets and Facebook posts with evidence of social impact.

Another result is related to the type of evidence of social impact found. There is both quantitative and qualitative evidence. Both types are useful for understanding the type of social impact achieved by the corresponding research project. In this sense, quantitative evidence allows us to understand the improvements obtained by the implementation of the research results and capture their impact. In contrast, qualitative evidence allows us to deeply understand how the resultant improvements obtained from the implementation of the research results are evaluated by the end users by capturing their corresponding direct quotes. The social impact includes the identification of both real and potential social impact.

Conclusions

After discussing the main results obtained, we conclude with the following points. Our study indicates that there is incipient evidence of social impact, both potential and real, in social media. This demonstrates that researchers from different fields, in the present case involved in medical research, public health, animal welfare and genomics, are sharing the improvements generated by their research and opening up new venues for citizens to interact with their work. This would imply that scientists are promoting not only the dissemination of their research results but also the evidence on how their results may lead to the improvement of societies. Considering the increasing relevance and presence of the dissemination of research, the results indicate that scientists still need to include in their dissemination and communication strategies the aim of sharing the social impact of their results. This implies the publication of concrete qualitative or quantitative evidence of the social impact obtained. Because of the inclusion of this strategy, citizens will pay more attention to the content published in social media because they are interested in knowing how science can contribute to improving their living conditions and in accessing crucial information. Sharing social impact in social media facilitates access to citizens of different ages, genders, cultural backgrounds and education levels. However, what is most relevant for our argument here is how citizens should also be able to participate in the evaluation of the social impact of research, with social media a great source to reinforce this democratization process. This contributes not only to greatly improving the social impact assessment, as in addition to experts, policy makers and scientific publications, citizens through social media contribute to making this assessment much more accurate. Thus, citizens’ contribution to the dissemination of evidence of the social impact of research yields access to more diverse sectors of society and information that might be unknown by the research or political community. Two future steps are opened here. On the one hand, it is necessary to further examine the profiles of users who interact with this evidence of social impact considering the limitations of the privacy and availability of profile information. A second future task is to advance in the articulation of the role played by citizens’ participation in social impact assessment, as citizens can contribute to current worldwide efforts by shedding new light on this process of social impact assessment and contributing to making science more relevant and useful for the most urgent and poignant social needs.

Supporting information

S1 file. interrater reliability (kappa) result..

This file contains the SPSS file with the result of the calculation of Cohen’s Kappa regards the interrater reliability. The word document exported with the obtained result is also included.

https://doi.org/10.1371/journal.pone.0203117.s001

S2 File. Data collected and SICOR calculation.

This excel contains four sheets, the first one titled “data collected” contains the number of tweets and Facebook posts collected through the three defined search strategies; the second sheet titled “sample” contains the sample classified by project indicating the ID of the message or code assigned, the type of message (tweet or Facebook post) and the codification done by researchers being 1 (is evidence of social impact, either potential or real) and 0 (is not evidence of social impact); the third sheet titled “evidence found” contains the number of type of evidences of social impact founded by project (ESISM-QUANESIM or ESISM-QUALESIM), search strategy and type of message (tweet or Facebook posts); and the last sheet titled “SICOR” contains the Social Impact Coverage Ratio calculation by projects in one table and type of search strategy done in another one.

https://doi.org/10.1371/journal.pone.0203117.s002

Acknowledgments

The research leading to these results received funding from the 7 th Framework Programme of the European Commission under Grant Agreement n° 613202. The extraction of available data using the list of searchable keywords on Twitter and Facebook followed the ethical guidelines for social media research supported by the Economic and Social Research Council (UK) [ 36 ] and the University of Aberdeen [ 37 ]. Furthermore, the research results have already been published and made public, and hence, there are no ethical issues.

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Social Media Use and Mental Health and Well-Being Among Adolescents – A Scoping Review

Viktor schønning.

1 Department of Health Promotion, Norwegian Institute of Public Health, Bergen, Norway

Gunnhild Johnsen Hjetland

Leif edvard aarø, jens christoffer skogen.

2 Alcohol and Drug Research Western Norway, Stavanger University Hospital, Stavanger, Norway

3 Faculty of Health Sciences, University of Stavanger, Stavanger, Norway

Associated Data

Introduction: Social media has become an integrated part of daily life, with an estimated 3 billion social media users worldwide. Adolescents and young adults are the most active users of social media. Research on social media has grown rapidly, with the potential association of social media use and mental health and well-being becoming a polarized and much-studied subject. The current body of knowledge on this theme is complex and difficult-to-follow. The current paper presents a scoping review of the published literature in the research field of social media use and its association with mental health and well-being among adolescents.

Methods and Analysis: First, relevant databases were searched for eligible studies with a vast range of relevant search terms for social media use and mental health and well-being over the past five years. Identified studies were screened thoroughly and included or excluded based on prior established criteria. Data from the included studies were extracted and summarized according to the previously published study protocol.

Results: Among the 79 studies that met our inclusion criteria, the vast majority (94%) were quantitative, with a cross-sectional design (57%) being the most common study design. Several studies focused on different aspects of mental health, with depression (29%) being the most studied aspect. Almost half of the included studies focused on use of non-specified social network sites (43%). Of specified social media, Facebook (39%) was the most studied social network site. The most used approach to measuring social media use was frequency and duration (56%). Participants of both genders were included in most studies (92%) but seldom examined as an explanatory variable. 77% of the included studies had social media use as the independent variable.

Conclusion: The findings from the current scoping review revealed that about 3/4 of the included studies focused on social media and some aspect of pathology. Focus on the potential association between social media use and positive outcomes seems to be rarer in the current literature. Amongst the included studies, few separated between different forms of (inter)actions on social media, which are likely to be differentially associated with mental health and well-being outcomes.

In just a few decades, the use of social media have permeated most areas of our society. For adolescents, social media play a particularly large part in their lives as indicated by their extensive use of several different social media platforms ( Ofcom, 2018 ). Furthermore, the use of social media and types of platforms offered have increased at such a speed that there is reason to believe that scientific knowledge about social media in relation to adolescents’ health and well-being is scattered and incomplete ( Orben, 2020 ). Nevertheless, research findings indicating the potential negative effects of social media on mental health and well-being are frequently reported in traditional media (newspapers, radio, TV) ( Bell et al., 2015 ). Within the scientific community, however, there are ongoing debates regarding the impact and relevance of social media in relation to mental health and well-being. For instance, Twenge and Campbell (2019) stated that use of digital technology and social media have a negative impact on well-being, while Orben and Przybylski (2019) argued that the association between digital technology use and adolescent well-being is so small that it is more or less inconsequential. Research on social media use is a new focus area, and it is therefore important to get an overview of the studies performed to date, and describe the subject matter studies have investigated in relation to the effect of social media use on adolescents mental health and well-being. Also, research gaps in this emerging research field is important to highlight as it may guide future research in new and meritorious directions. A scoping review is therefore deemed necessary to provide a foundation for further research, which in time will provide a knowledge base for policymaking and service delivery.

This scoping review will help provide an overall understanding of the main foci of research within the field of social media and mental health and well-being among adolescents, as well as the type of data sources and research instruments used so far. Furthermore, we aim to highlight potential gaps in the research literature ( Arksey and O’Malley, 2005 ). Even though a large number of studies on social media use and mental health with different vantage points has been conducted over the last decade, we are not aware of any broad-sweeping scoping review covering this area.

This scoping review aims to give an overview of the main research questions that have been focused on with regard to use of social media among adolescents in relation to mental health and well-being. Both quantitative and qualitative studies are of interest. Three specific secondary research questions will be addressed and together with the main research question serve as a template for organizing the results:

  • • Which aspects of mental health and well-being have been the focus or foci of research so far?
  • • Has the research focused on different research aims across gender, ethnicity, socio-economic status, geographic location? What kind of findings are reported across these groups?
  • • Organize and describe the main sources of evidence related to social media that have been used in the studies identified.

Defining Adolescence and Social Media

In the present review, adolescents are defined as those between 13 and 19 years of age. We chose the mean age of 13 as our lower limit as nearly all social media services require users to be at least 13 years of age to access and use their services ( Childnet International, 2018 ). All pertinent studies which present results relevant for this age range is within the scope of this review. For social media we used the following definition by Kietzmann et al. (2011 , p. 1): “Social media employ mobile and web-based technologies to create highly interactive platforms via which individuals and communities share, co-create, discuss, and modify user-generated content.” We also employed the typology described by Kaplan and Haenlein’s classification scheme across two axes: level of self-presentation and social presence/media richness ( Kaplan and Haenlein, 2010 ). The current scoping review adheres to guidelines and recommendations stated by Tricco et al. (2018) .

See protocol for further details about the definitions used ( Schønning et al., 2020 ).

Data Sources and Search Strategy

A literature search was performed in OVID Medline, OVID Embase, OVID PsycINFO, Sociological Abstracts (proquest), Social Services Abstracts (proquest), ERIC (proquest), and CINAHL. The search strategy combined search terms for adolescents, social media and mental health or wellbeing. The database-controlled vocabulary was used for searching subject headings, and a large spectrum of synonyms with appropriate truncations was used for searching title, abstract, and author keywords. A filter for observational studies was applied to limit the results. The search was also limited to publications from 2014 to current. The search strategy was translated between each database. An example of full strategy for Embase is attached as Supplementary Material .

Study Selection: Exclusion and Inclusion Criteria

The exclusion and inclusion criteria are detailed in the protocol ( Schønning et al., 2020 ). Briefly, we included English language peer-reviewed quantitative- or qualitative papers or systematic reviews published within the last 5 years with an explicit focus on mental health/well-being and social media. Non-empirical studies, intervention studies, clinical studies and publications not peer-reviewed were excluded. Intervention studies and clinical studies were excluded as we sought to not introduce too much heterogeneity in design and our focus was on observational studies. The criteria used for study selection was part of an iterative process which was described in detail in the protocol ( Schønning et al., 2020 ). As per the study protocol ( Schønning et al., 2020 ), and in line with scoping review guidelines ( Peters et al., 2015 , 2017 ; Tricco et al., 2018 ), we did not assess methodological quality or risk of bias of the included studies.

The selection process is illustrated by a flow-chart indicating the stages from unsorted search results to the number of included studies (see Figure 1 ). Study selection was accomplished and organized using the Rayyan QCRI software 1 . The inclusion and exclusion process was performed independently by VS and JCS. The interrater agreement was κ = 0.87, indicating satisfactory agreement.

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Object name is fpsyg-11-01949-g001.jpg

Flowchart of exclusion process from unsorted results to included studies.

Data Extraction and Organization

Details of the data extracted is described in the protocol. Three types of information were extracted, bibliographic information, information about study design and subject matter information. Subject matter information included aim of study, how social media and mental health/well-being was measured, and main findings of the study.

Visualization of Words From the Titles of the Included Studies

The most frequently occurring words and bigrams in the titles of the included studies are presented in Figures 2 , ​ ,3. 3 . The following procedure was used to generate Figure 1 : First, a text file containing all titles were imported into R as a data frame ( R Core Team, 2014 ). The data frame was processed using the “tidy text”-package with required additional packages ( Silge and Robinson, 2016 ). Second, numbers and commonly used words with little inherent meaning (so called “stop words,” such as “and,” “of,” and “in”), were removed from the data frame using the three available lexicons in the “tidy-text”-package ( Silge and Robinson, 2016 ). Furthermore, variations of “adolescents” (e.g., “adolescent,” “adolescence,” and “adolescents”) and “social media” (e.g., “social media,” “social networking,” “online social networks”) were removed from the data frame. Third, the resulting data frame was sorted based on frequency of unique words, and words occurring only once were removed. The final data frame is presented as a word cloud in Figure 1 ( N = 113). The same procedure as described above was employed to generate commonly occurring bigrams (two words occurring adjacent to each other), but without removing bigrams occurring only once ( N = 231). The word clouds were generated using the “wordcloud2”-package in R ( Lang and Chien, 2018 ). For Figure 1 , shades of blue indicate word frequencies >2 and green a frequency of 2. For Figure 2 , shades of blue indicate bigram frequencies of >1 and green a frequency of 1.

An external file that holds a picture, illustration, etc.
Object name is fpsyg-11-01949-g002.jpg

Word cloud from the titles of the included studies. Most frequent words, excluding variations of “adolescence” and “social media.” N = 113. Shades of blue indicate word frequencies >2 and green a frequency of 2. The size of each word is indicative of its relative frequency of occurrence.

An external file that holds a picture, illustration, etc.
Object name is fpsyg-11-01949-g003.jpg

Word cloud from the titles of the included studies. Bigrams from the titles of the included studies, excluding variations of “adolescence” and “social media.” N = 231. Shades of blue indicate bigram frequencies of >1 and green a frequency of 1. The size of each bigram is indicative of its relative frequency of occurrence.

Characteristics of the Included Studies

Of 7927 unique studies, 79 (1%) met our inclusion criteria ( Aboujaoude et al., 2015 ; Banjanin et al., 2015 ; Banyai et al., 2017 ; Barry et al., 2017 ; Best et al., 2014 , 2015 ; Booker et al., 2018 ; Bourgeois et al., 2014 ; Boyle et al., 2016 ; Brunborg et al., 2017 ; Burnette et al., 2017 ; Colder Carras et al., 2017 ; Critchlow et al., 2019 ; Cross et al., 2015 ; Curtis et al., 2018 ; de Lenne et al., 2018 ; de Vries et al., 2016 ; Erfani and Abedin, 2018 ; Erreygers et al., 2018 ; Fahy et al., 2016 ; Ferguson et al., 2014 ; Fisher et al., 2016 ; Foerster and Roosli, 2017 ; Foody et al., 2017 ; Fredrick and Demaray, 2018 ; Frison and Eggermont, 2016 , 2017 ; Geusens and Beullens, 2017 , 2018 ; Hamm et al., 2015 ; Hanprathet et al., 2015 ; Harbard et al., 2016 ; Hase et al., 2015 ; Holfeld and Mishna, 2019 ; Houghton et al., 2018 ; Jafarpour et al., 2017 ; John et al., 2018 ; Kim et al., 2019 ; Kim, 2017 ; Koo et al., 2015 ; Lai et al., 2018 ; Larm et al., 2017 , 2019 ; Marchant et al., 2017 ; Marengo et al., 2018 ; Marques et al., 2018 ; Meier and Gray, 2014 ; Memon et al., 2018 ; Merelle et al., 2017 ; Neira and Barber, 2014 ; Nesi et al., 2017a , b ; Niu et al., 2018 ; Nursalam et al., 2018 ; Oberst et al., 2017 ; O’Connor et al., 2014 ; O’Reilly et al., 2018 ; Przybylski and Bowes, 2017 ; Przybylski and Weinstein, 2017 ; Richards et al., 2015 ; Rousseau et al., 2017 ; Salmela-Aro et al., 2017 ; Sampasa-Kanyinga and Chaput, 2016 ; Sampasa-Kanyinga and Lewis, 2015 ; Sampasa-Kanyinga et al., 2018 ; Scott and Woods, 2018 ; Settanni et al., 2018 ; Spears et al., 2015 ; Throuvala et al., 2019 ; Tiggemann and Slater, 2017 ; Tseng and Yang, 2015 ; Twenge and Campbell, 2019 ; Twenge et al., 2018 ; van den Eijnden et al., 2018 ; Wang et al., 2018 ; Wartberg et al., 2018 ; Wolke et al., 2017 ; Woods and Scott, 2016 ; Yan et al., 2017 ). Among the included studies, 74 (94%) are quantitative ( Aboujaoude et al., 2015 ; Banjanin et al., 2015 ; Banyai et al., 2017 ; Barry et al., 2017 ; Best et al., 2014 ; Booker et al., 2018 ; Bourgeois et al., 2014 ; Boyle et al., 2016 ; Brunborg et al., 2017 ; Colder Carras et al., 2017 ; Critchlow et al., 2019 ; Cross et al., 2015 ; Curtis et al., 2018 ; de Lenne et al., 2018 ; de Vries et al., 2016 ; Erfani and Abedin, 2018 ; Erreygers et al., 2018 ; Fahy et al., 2016 ; Ferguson et al., 2014 ; Fisher et al., 2016 ; Foerster and Roosli, 2017 ; Foody et al., 2017 ; Fredrick and Demaray, 2018 ; Frison and Eggermont, 2016 , 2017 ; Geusens and Beullens, 2017 , 2018 ; Hamm et al., 2015 ; Hanprathet et al., 2015 ; Harbard et al., 2016 ; Hase et al., 2015 ; Houghton et al., 2018 ; Jafarpour et al., 2017 ; John et al., 2018 ; Kim et al., 2019 ; Kim, 2017 ; Koo et al., 2015 ; Lai et al., 2018 ; Larm et al., 2017 , 2019 ; Marchant et al., 2017 ; Marengo et al., 2018 ; Marques et al., 2018 ; Meier and Gray, 2014 ; Memon et al., 2018 ; Merelle et al., 2017 ; Neira and Barber, 2014 ; Nesi et al., 2017a , b ; Niu et al., 2018 ; Nursalam et al., 2018 ; Oberst et al., 2017 ; O’Connor et al., 2014 ; Przybylski and Bowes, 2017 ; Przybylski and Weinstein, 2017 ; Richards et al., 2015 ; Rousseau et al., 2017 ; Salmela-Aro et al., 2017 ; Sampasa-Kanyinga and Chaput, 2016 ; Sampasa-Kanyinga and Lewis, 2015 ; Sampasa-Kanyinga et al., 2018 ; Scott and Woods, 2018 ; Settanni et al., 2018 ; Spears et al., 2015 ; Tiggemann and Slater, 2017 ; Tseng and Yang, 2015 ; Twenge and Campbell, 2019 ; Twenge et al., 2018 ; van den Eijnden et al., 2018 ; Wang et al., 2018 ; Wartberg et al., 2018 ; Wolke et al., 2017 ; Woods and Scott, 2016 ; Yan et al., 2017 ), three are qualitative ( O’Reilly et al., 2018 ; Burnette et al., 2017 ; Throuvala et al., 2019 ), and two use mixed methods ( Best et al., 2015 ; Holfeld and Mishna, 2019 ) (see Supplementary Tables 1 , 2 in the Supplementary Material for additional details extracted from all included studies). In relation to study design, 45 (57%) used a cross-sectional design ( Bourgeois et al., 2014 ; Ferguson et al., 2014 ; Meier and Gray, 2014 ; Neira and Barber, 2014 ; O’Connor et al., 2014 ; Banjanin et al., 2015 ; Hanprathet et al., 2015 ; Hase et al., 2015 ; Koo et al., 2015 ; Sampasa-Kanyinga and Lewis, 2015 ; Spears et al., 2015 ; Tseng and Yang, 2015 ; Frison and Eggermont, 2016 ; Sampasa-Kanyinga and Chaput, 2016 ; Woods and Scott, 2016 ; Banyai et al., 2017 ; Barry et al., 2017 ; Brunborg et al., 2017 ; Colder Carras et al., 2017 ; Larm et al., 2017 , 2019 ; Merelle et al., 2017 ; Oberst et al., 2017 ; Przybylski and Bowes, 2017 ; Przybylski and Weinstein, 2017 ; Tiggemann and Slater, 2017 ; Wolke et al., 2017 ; Yan et al., 2017 ; de Lenne et al., 2018 ; Erreygers et al., 2018 ; Fredrick and Demaray, 2018 ; Geusens and Beullens, 2018 ; Lai et al., 2018 ; Marengo et al., 2018 ; Marques et al., 2018 ; Niu et al., 2018 ; Nursalam et al., 2018 ; Sampasa-Kanyinga et al., 2018 ; Scott and Woods, 2018 ; Settanni et al., 2018 ; Wang et al., 2018 ; Wartberg et al., 2018 ; Critchlow et al., 2019 ; Kim et al., 2019 ; Twenge and Campbell, 2019 ), 17 used a longitudinal design ( Cross et al., 2015 ; Boyle et al., 2016 ; de Vries et al., 2016 ; Fahy et al., 2016 ; Frison and Eggermont, 2016 ; Harbard et al., 2016 ; Foerster and Roosli, 2017 ; Geusens and Beullens, 2017 ; Kim, 2017 ; Nesi et al., 2017a , b ; Rousseau et al., 2017 ; Salmela-Aro et al., 2017 ; Booker et al., 2018 ; Houghton et al., 2018 ; van den Eijnden et al., 2018 ; Holfeld and Mishna, 2019 ), seven were systematic reviews ( Aboujaoude et al., 2015 ; Best et al., 2015 ; Fisher et al., 2016 ; Marchant et al., 2017 ; Erfani and Abedin, 2018 ; John et al., 2018 ; Memon et al., 2018 ), two were meta-analyses ( Foody et al., 2017 : Curtis et al., 2018 ), one was a causal-comparative study ( Jafarpour et al., 2017 ), one was a review article ( Richards et al., 2015 ), one used a time-lag design ( Twenge et al., 2018 ), one was a scoping review ( Hamm et al., 2015 ), three used a focus-group interview design ( Burnette et al., 2017 ; O’Reilly et al., 2018 ; Throuvala et al., 2019 ), and one study used a combined survey and focus-group design ( Best et al., 2014 ).

The most common study settings were schools [ N = 42 (54%)] ( Best et al., 2014 ; Bourgeois et al., 2014 ; Meier and Gray, 2014 ; Neira and Barber, 2014 ; O’Connor et al., 2014 ; Banjanin et al., 2015 ; Hanprathet et al., 2015 ; Hase et al., 2015 ; Sampasa-Kanyinga and Lewis, 2015 ; Frison and Eggermont, 2016 ; Sampasa-Kanyinga and Chaput, 2016 ; Woods and Scott, 2016 ; Banyai et al., 2017 ; Brunborg et al., 2017 ; Colder Carras et al., 2017 ; Foerster and Roosli, 2017 ; Geusens and Beullens, 2017 , 2018 ; Kim, 2017 ; Larm et al., 2017 , 2019 ; Merelle et al., 2017 ; Nesi et al., 2017a , b ; Przybylski and Bowes, 2017 ; Rousseau et al., 2017 ; Salmela-Aro et al., 2017 ; Tiggemann and Slater, 2017 ; de Lenne et al., 2018 ; Fredrick and Demaray, 2018 ; Houghton et al., 2018 ; Lai et al., 2018 ; Marengo et al., 2018 ; Niu et al., 2018 ; Nursalam et al., 2018 ; Sampasa-Kanyinga et al., 2018 ; Scott and Woods, 2018 ; Settanni et al., 2018 ; van den Eijnden et al., 2018 ; Wang et al., 2018 ; Holfeld and Mishna, 2019 ; Kim et al., 2019 ). Fourteen of the included studies were based on data from a home setting ( Cross et al., 2015 ; Koo et al., 2015 ; Spears et al., 2015 ; Boyle et al., 2016 ; de Vries et al., 2016 ; Harbard et al., 2016 ; Barry et al., 2017 ; Frison and Eggermont, 2017 ; Oberst et al., 2017 ; Yan et al., 2017 ; Booker et al., 2018 ; Marques et al., 2018 ; Wartberg et al., 2018 ; Critchlow et al., 2019 ). Eleven publications were reviews or meta-analyses and included primary studies from different settings ( Aboujaoude et al., 2015 ; Best et al., 2015 ; Hamm et al., 2015 ; Richards et al., 2015 ; Fisher et al., 2016 ; Foody et al., 2017 ; Marchant et al., 2017 ; Curtis et al., 2018 ; Erfani and Abedin, 2018 ; John et al., 2018 ; Memon et al., 2018 ). One study used both a home and school setting ( Erreygers et al., 2018 ), and 11 (14%) of the included studies did not mention the study setting for data collection ( Ferguson et al., 2014 ; Tseng and Yang, 2015 ; Fahy et al., 2016 ; Burnette et al., 2017 ; Jafarpour et al., 2017 ; Przybylski and Weinstein, 2017 ; Wolke et al., 2017 ; O’Reilly et al., 2018 ; Twenge et al., 2018 ; Throuvala et al., 2019 ; Twenge and Campbell, 2019 ).

Mental Health Foci of Included Studies

For a visual overview of the mental health foci of the included studies see Figures 2 , ​ ,3. 3 . Most studies had a focus on different negative aspects of mental health, as evident from the frequently used terms in Figures 2 , ​ ,3. 3 . The most studied aspect was depression, with 23 (29%) studies examining the relationship between social media use and depressive symptoms ( Ferguson et al., 2014 ; Neira and Barber, 2014 ; O’Connor et al., 2014 ; Banjanin et al., 2015 ; Richards et al., 2015 ; Spears et al., 2015 ; Tseng and Yang, 2015 ; Fahy et al., 2016 ; Frison and Eggermont, 2016 , 2017 ; Woods and Scott, 2016 ; Banyai et al., 2017 ; Brunborg et al., 2017 ; Colder Carras et al., 2017 ; Larm et al., 2017 ; Nesi et al., 2017a ; Salmela-Aro et al., 2017 ; Fredrick and Demaray, 2018 ; Houghton et al., 2018 ; Niu et al., 2018 ; Twenge et al., 2018 ; Wang et al., 2018 ; Wartberg et al., 2018 ). Twenty of the included studies focused on different aspects of good mental health, such as well-being, happiness, or quality of life ( Best et al., 2014 , 2015 ; Bourgeois et al., 2014 ; Ferguson et al., 2014 ; Cross et al., 2015 ; Koo et al., 2015 ; Richards et al., 2015 ; Spears et al., 2015 ; Fahy et al., 2016 ; Foerster and Roosli, 2017 ; Przybylski and Bowes, 2017 ; Przybylski and Weinstein, 2017 ; Yan et al., 2017 ; Booker et al., 2018 ; de Lenne et al., 2018 ; Erfani and Abedin, 2018 ; Erreygers et al., 2018 ; Lai et al., 2018 ; van den Eijnden et al., 2018 ; Twenge and Campbell, 2019 ). Nineteen studies had a more broad-stroke approach, and covered general mental health or psychiatric problems ( Aboujaoude et al., 2015 ; Hanprathet et al., 2015 ; Hase et al., 2015 ; Sampasa-Kanyinga and Lewis, 2015 ; Spears et al., 2015 ; Fisher et al., 2016 ; Barry et al., 2017 ; Jafarpour et al., 2017 ; Kim, 2017 ; Merelle et al., 2017 ; Oberst et al., 2017 ; Wolke et al., 2017 ; Marengo et al., 2018 ; Marques et al., 2018 ; Sampasa-Kanyinga et al., 2018 ; Holfeld and Mishna, 2019 ; Kim et al., 2019 ; Larm et al., 2019 ). Eight studies examined the link between social media use and body dissatisfaction and eating disorder symptoms ( Ferguson et al., 2014 ; Meier and Gray, 2014 ; de Vries et al., 2016 ; Burnette et al., 2017 ; Rousseau et al., 2017 ; Tiggemann and Slater, 2017 ; Marengo et al., 2018 ; Wartberg et al., 2018 ). Anxiety was the focus of seven studies ( O’Connor et al., 2014 ; Koo et al., 2015 ; Spears et al., 2015 ; Fahy et al., 2016 ; Woods and Scott, 2016 ; Colder Carras et al., 2017 ; Yan et al., 2017 ), and 13 studies included a focus on the relationship between alcohol use and social media use ( O’Connor et al., 2014 ; Boyle et al., 2016 ; Sampasa-Kanyinga and Chaput, 2016 ; Brunborg et al., 2017 ; Geusens and Beullens, 2017 , 2018 ; Larm et al., 2017 ; Merelle et al., 2017 ; Nesi et al., 2017b ; Curtis et al., 2018 ; Sampasa-Kanyinga et al., 2018 ; Critchlow et al., 2019 ; Kim et al., 2019 ). Seven studies examined the effect of social media use on sleep ( Harbard et al., 2016 ; Woods and Scott, 2016 ; Yan et al., 2017 ; Nursalam et al., 2018 ; Sampasa-Kanyinga et al., 2018 ; Scott and Woods, 2018 ; Larm et al., 2019 ). Five studies saw how drug use and social media use affected each other ( O’Connor et al., 2014 ; Merelle et al., 2017 ; Sampasa-Kanyinga et al., 2018 ; Kim et al., 2019 ; Larm et al., 2019 ). Self-harm and suicidal behavior was the focus of eleven studies ( O’Connor et al., 2014 ; Sampasa-Kanyinga and Lewis, 2015 ; Tseng and Yang, 2015 ; Kim, 2017 ; Marchant et al., 2017 ; Merelle et al., 2017 ; Fredrick and Demaray, 2018 ; John et al., 2018 ; Memon et al., 2018 ; Twenge et al., 2018 ; Kim et al., 2019 ). Other areas of focus other than the aforementioned are loneliness, self-esteem, fear of missing out and other non-pathological measures ( Neira and Barber, 2014 ; Banyai et al., 2017 ; Barry et al., 2017 ; Colder Carras et al., 2017 ).

Social Media Metrics of Included Studies

The studies included in the current scoping review often focus on specific, widely used, social media and social networking services, such as 31 (39%) studies focusing on Facebook ( Bourgeois et al., 2014 ; Meier and Gray, 2014 ; Banjanin et al., 2015 ; Cross et al., 2015 ; Hanprathet et al., 2015 ; Richards et al., 2015 ; Sampasa-Kanyinga and Lewis, 2015 ; Spears et al., 2015 ; Boyle et al., 2016 ; de Vries et al., 2016 ; Frison and Eggermont, 2016 ; Harbard et al., 2016 ; Sampasa-Kanyinga and Chaput, 2016 ; Banyai et al., 2017 ; Barry et al., 2017 ; Brunborg et al., 2017 ; Larm et al., 2017 ; Merelle et al., 2017 ; Nesi et al., 2017a , b ; Rousseau et al., 2017 ; Tiggemann and Slater, 2017 ; Booker et al., 2018 ; de Lenne et al., 2018 ; Lai et al., 2018 ; Marengo et al., 2018 ; Marques et al., 2018 ; Memon et al., 2018 ; Sampasa-Kanyinga et al., 2018 ; Settanni et al., 2018 ; Twenge et al., 2018 ), 11 on Instagram ( Sampasa-Kanyinga and Lewis, 2015 ; Boyle et al., 2016 ; Sampasa-Kanyinga and Chaput, 2016 ; Barry et al., 2017 ; Brunborg et al., 2017 ; Frison and Eggermont, 2017 ; Nesi et al., 2017a ; Marengo et al., 2018 ; Memon et al., 2018 ; Sampasa-Kanyinga et al., 2018 ), 11 including Twitter ( Richards et al., 2015 ; Sampasa-Kanyinga and Lewis, 2015 ; Spears et al., 2015 ; Harbard et al., 2016 ; Sampasa-Kanyinga and Chaput, 2016 ; Barry et al., 2017 ; Brunborg et al., 2017 ; Merelle et al., 2017 ; Nesi et al., 2017a ; Memon et al., 2018 ; Sampasa-Kanyinga et al., 2018 ), and five studies asking about Snapchat ( Boyle et al., 2016 ; Barry et al., 2017 ; Brunborg et al., 2017 ; Nesi et al., 2017a ; Marengo et al., 2018 ). Eight studies mentioned Myspace ( Richards et al., 2015 ; Sampasa-Kanyinga and Lewis, 2015 ; de Vries et al., 2016 ; Harbard et al., 2016 ; Sampasa-Kanyinga and Chaput, 2016 ; Larm et al., 2017 ; Booker et al., 2018 ; Sampasa-Kanyinga et al., 2018 ) and two asked about Tumblr ( Barry et al., 2017 ; Nesi et al., 2017a ). Other media such as Skype ( Merelle et al., 2017 ), Youtube ( Richards et al., 2015 ), WhatsApp ( Brunborg et al., 2017 ), Ping ( Merelle et al., 2017 ), Bebo ( Booker et al., 2018 ), Hyves ( de Vries et al., 2016 ), Kik ( Brunborg et al., 2017 ), Ask ( Brunborg et al., 2017 ), and Qzone ( Niu et al., 2018 ) were only included in one study each.

Almost half ( n = 34, 43%) of the included studies focus on use of social network sites or online communication in general, without specifying particular social media sites, leaving this up to the study participants to decide ( Best et al., 2014 , 2015 ; Ferguson et al., 2014 ; Neira and Barber, 2014 ; O’Connor et al., 2014 ; Koo et al., 2015 ; Tseng and Yang, 2015 ; Fahy et al., 2016 ; Woods and Scott, 2016 ; Burnette et al., 2017 ; Colder Carras et al., 2017 ; Foerster and Roosli, 2017 ; Foody et al., 2017 ; Geusens and Beullens, 2017 , 2018 ; Jafarpour et al., 2017 ; Kim, 2017 ; Marchant et al., 2017 ; Oberst et al., 2017 ; Przybylski and Weinstein, 2017 ; Salmela-Aro et al., 2017 ; Yan et al., 2017 ; Curtis et al., 2018 ; Erfani and Abedin, 2018 ; Erreygers et al., 2018 ; Nursalam et al., 2018 ; Scott and Woods, 2018 ; van den Eijnden et al., 2018 ; Wartberg et al., 2018 ; Critchlow et al., 2019 ; Holfeld and Mishna, 2019 ; Larm et al., 2019 ; Throuvala et al., 2019 ; Twenge and Campbell, 2019 ). Seven of the included studies examined the relationship between virtual game worlds or socially oriented video games and mental health ( Ferguson et al., 2014 ; Best et al., 2015 ; Spears et al., 2015 ; Yan et al., 2017 ; van den Eijnden et al., 2018 ; Larm et al., 2019 ; Twenge and Campbell, 2019 ).

In the 79 studies included in this scoping review, several approaches to measuring social media use are utilized. The combination of frequency and duration of social media use is by far the most used measurement of social media use, and 44 (56%) of the included studies collected data on these parameters ( Ferguson et al., 2014 ; Meier and Gray, 2014 ; Neira and Barber, 2014 ; Banjanin et al., 2015 ; Best et al., 2015 ; Hanprathet et al., 2015 ; Sampasa-Kanyinga and Lewis, 2015 ; Tseng and Yang, 2015 ; Boyle et al., 2016 ; de Vries et al., 2016 ; Frison and Eggermont, 2016 , 2017 ; Harbard et al., 2016 ; Sampasa-Kanyinga and Chaput, 2016 ; Woods and Scott, 2016 ; Banyai et al., 2017 ; Brunborg et al., 2017 ; Colder Carras et al., 2017 ; Foerster and Roosli, 2017 ; Jafarpour et al., 2017 ; Kim, 2017 ; Larm et al., 2017 , 2019 ; Merelle et al., 2017 ; Nesi et al., 2017b ; Oberst et al., 2017 ; Rousseau et al., 2017 ; Tiggemann and Slater, 2017 ; Yan et al., 2017 ; Booker et al., 2018 ; de Lenne et al., 2018 ; Erreygers et al., 2018 ; Houghton et al., 2018 ; Lai et al., 2018 ; Marengo et al., 2018 ; Marques et al., 2018 ; Niu et al., 2018 ; Nursalam et al., 2018 ; Sampasa-Kanyinga et al., 2018 ; Scott and Woods, 2018 ; Settanni et al., 2018 ; Twenge et al., 2018 ; van den Eijnden et al., 2018 ; Twenge and Campbell, 2019 ). Eight studies focused on the relationship between social media addiction or excessive use and mental health ( Banjanin et al., 2015 ; Tseng and Yang, 2015 ; Banyai et al., 2017 ; Merelle et al., 2017 ; Nursalam et al., 2018 ; Settanni et al., 2018 ; Wang et al., 2018 ). Bergen Social Media Addiction Scale is a commonly used questionnaire amongst the included studies ( Hanprathet et al., 2015 ; Banyai et al., 2017 ; Settanni et al., 2018 ). Seven studies asked about various specific actions on social media, such as liking or commenting on photos, posting something or participating in a discussion ( Meier and Gray, 2014 ; Koo et al., 2015 ; Nesi et al., 2017b ; Geusens and Beullens, 2018 ; Marques et al., 2018 ; van den Eijnden et al., 2018 ; Critchlow et al., 2019 ).

Five studies had a specific and sole focus on the link between social media use and alcohol, and examined how various alcohol-related social media use affected alcohol intake ( Boyle et al., 2016 ; Geusens and Beullens, 2017 , 2018 ; Nesi et al., 2017b ; Critchlow et al., 2019 ). Some studies had a more theory-based focus and investigated themes such as peer comparison, social media intrusion or pro-social behavior on social media and its effect on mental health ( Bourgeois et al., 2014 ; Rousseau et al., 2017 ; de Lenne et al., 2018 ). One of the included studies looked into night-time specific social media use ( Scott and Woods, 2018 ) and one looked into pre-bedtime social media behavior ( Harbard et al., 2016 ) to study the link between this use and sleep.

Amongst the 79 included studies, only six (8%) studies had participants of one gender ( Ferguson et al., 2014 ; Meier and Gray, 2014 ; Best et al., 2015 ; Burnette et al., 2017 ; Jafarpour et al., 2017 ; Tiggemann and Slater, 2017 ). Sixteen studies (20%) did not mention the gender distribution of the participants ( Aboujaoude et al., 2015 ; Best et al., 2015 ; Hamm et al., 2015 ; Richards et al., 2015 ; Fisher et al., 2016 ; Woods and Scott, 2016 ; Foody et al., 2017 ; Marchant et al., 2017 ; Przybylski and Weinstein, 2017 ; Curtis et al., 2018 ; Erfani and Abedin, 2018 ; John et al., 2018 ; Memon et al., 2018 ; O’Reilly et al., 2018 ; Twenge et al., 2018 ; Twenge and Campbell, 2019 ). Several of these were meta-analyses or reviews ( Aboujaoude et al., 2015 ; Best et al., 2014 ; Curtis et al., 2018 ; Foody et al., 2017 ; John et al., 2018 ; Erfani and Abedin, 2018 ; Wallaroo, 2020 ). The studies that included both genders as participants generally had a well-balanced gender distribution with no gender below 40% of the participants. Eight of the studies did not report gender-specific results ( Harbard et al., 2016 ; Nesi et al., 2017b ; Curtis et al., 2018 ; de Lenne et al., 2018 ; Niu et al., 2018 ; Nursalam et al., 2018 ; Wang et al., 2018 ; Twenge and Campbell, 2019 ). Of the included studies, gender was seldom examined as an explanatory variable, and other sociodemographic variables (e.g., ethnicity, socioeconomic status) were not included at all.

Implicit Causation Based on Direction of Association

Sixty-one (77%) of the included studies has social media use as the independent variable and some of the mentioned measurements of mental health as the dependent variable ( Aboujaoude et al., 2015 ; Banjanin et al., 2015 ; Banyai et al., 2017 ; Barry et al., 2017 ; Best et al., 2014 ; Booker et al., 2018 ; Bourgeois et al., 2014 ; Boyle et al., 2016 ; Brunborg et al., 2017 ; Colder Carras et al., 2017 ; Critchlow et al., 2019 ; Cross et al., 2015 ; Curtis et al., 2018 ; de Lenne et al., 2018 ; de Vries et al., 2016 ; Erfani and Abedin, 2018 ; Fahy et al., 2016 ; Fisher et al., 2016 ; Foerster and Roosli, 2017 ; Fredrick and Demaray, 2018 ; Frison and Eggermont, 2016 ; Geusens and Beullens, 2018 ; Hamm et al., 2015 ; Hanprathet et al., 2015 ; Harbard et al., 2016 ; Hase et al., 2015 ; Holfeld and Mishna, 2019 ; Jafarpour et al., 2017 ; John et al., 2018 ; Kim et al., 2019 ; Kim, 2017 ; Lai et al., 2018 ; Larm et al., 2017 , 2019 ; Marengo et al., 2018 ; Marques et al., 2018 ; Meier and Gray, 2014 ; Memon et al., 2018 ; Neira and Barber, 2014 ; Nesi et al., 2017b ; Niu et al., 2018 ; Nursalam et al., 2018 ; O’Connor et al., 2014 ; O’Reilly et al., 2018 ; Przybylski and Bowes, 2017 ; Przybylski and Weinstein, 2017 ; Richards et al., 2015 ; Sampasa-Kanyinga and Chaput, 2016 ; Sampasa-Kanyinga and Lewis, 2015 ; Sampasa-Kanyinga et al., 2018 ; Scott and Woods, 2018 ; Spears et al., 2015 ; Tseng and Yang, 2015 ; Twenge and Campbell, 2019 ; Twenge et al., 2018 ; van den Eijnden et al., 2018 ; Wang et al., 2018 ; Wartberg et al., 2018 ; Wolke et al., 2017 ; Woods and Scott, 2016 ; Yan et al., 2017 ). Most of the included studies hypothesize social media use pattern will affect youth mental health in certain ways. The majority of the included studies tend to find a correlation between more frequent social media use and poor well-being and/or mental health (see Supplementary Table 2 ). The strength of this correlation is however heterogeneous as social media use is measured substantially different across studies. Four (5%) of the included studies focus explicitly on how mental health can affect social media use ( Merelle et al., 2017 ; Nesi et al., 2017a ; Erreygers et al., 2018 ; Settanni et al., 2018 ). Fourteen studies included a mediating factor or focus on reciprocal relationships between social media use and mental health ( Ferguson et al., 2014 ; Koo et al., 2015 ; Tseng and Yang, 2015 ; Frison and Eggermont, 2017 ; Geusens and Beullens, 2017 ; Marchant et al., 2017 ; Oberst et al., 2017 ; Rousseau et al., 2017 ; Salmela-Aro et al., 2017 ; Tiggemann and Slater, 2017 ; Houghton et al., 2018 ; Marques et al., 2018 ; Niu et al., 2018 ; Wang et al., 2018 ). An example is a cross-sectional study by Ferguson et al. (2014) suggesting that exposure to social media contribute to later peer competition which was found to be a predictor of negative mental health outcomes such as eating disorder symptoms.

Cyberbullying as a Nexus

Thirteen of the 79 (17%) included studies investigated cyberbullying as the measurement of social media use ( Aboujaoude et al., 2015 ; Cross et al., 2015 ; Hamm et al., 2015 ; Hase et al., 2015 ; Spears et al., 2015 ; Fahy et al., 2016 ; Fisher et al., 2016 ; Foody et al., 2017 ; Przybylski and Bowes, 2017 ; Wolke et al., 2017 ; Fredrick and Demaray, 2018 ; John et al., 2018 ; Holfeld and Mishna, 2019 ). Most of the systematic reviews and meta-analyses included focused on cyberbullying. A cross-sectional study from 2017 suggests that cyberbullying has similar negative effects as direct or relational bullying, and that cyberbullying is “mainly a new tool to harm victims already bullied by traditional means” ( Wolke et al., 2017 ). A meta-analysis from 2016 concludes that “peer cybervictimization is indeed associated with a variety of internalizing and externalizing problems among adolescents” ( Fisher et al., 2016 ). A systematic review from 2018 concludes that both victims and perpetrators of cyberbullying are at greater risk of suicidal behavior compared with non-victims and non-perpetrators ( John et al., 2018 ).

Strengths and Limitations of Present Study

The main strength of this scoping review lies in the effort to give a broad overview of published research related to use of social media, and mental health and well-being among adolescents. Although a range of reviews on screen-based activities in general and mental health and well-being exist ( Dickson et al., 2018 ; Orben, 2020 ), they do not necessarily discern between social media use and other types of technology-based media. Also, some previous reviews tend to be more particular regarding mental health outcome ( Best et al., 2014 ; Seabrook et al., 2016 ; Orben, 2020 ), or do not focus on adolescents per se ( Seabrook et al., 2016 ). The main limitation is that, despite efforts to make the search strategy as comprehensive and inclusive as possible, we probably have not been able to identify all relevant studies – this is perhaps especially true when studies do include relevant information about social media and mental health/well-being, but this information is part of sub-group analyses or otherwise not the main aim of the studies. In a similar manner, related to qualitative studies, we do not know if our search strategy were as efficient in identifying studies of relevance if this was not the main theme or focus of the study. Despite this, we believe that we were able to strike a balance between specificity and sensitivity in our search strategy.

Description of Central Themes and Core Concepts

The findings from the present scoping review on social media use and mental health and well-being among adolescents revealed that the majority (about 3/4) of the included studies focused on social media and pathology. The core concepts identified are social media use and its statistical association with symptoms of depression, general psychiatric symptoms and other symptoms of psychopathology. Similar findings were made by Keles et al. (2020) in a systematic review from 2019. Focus on the potential association between social media use and positive outcomes seems to be rarer in the current literature, even though some studies focused on well-being which also includes positive aspects of mental health. Studies focusing on screen-based media in general and well-being is more prevalent than studies linking social media specifically with well-being ( Orben, 2020 ). The notion that excessive social media use is associated with poor mental health is well established within mainstream media. Our observation that this preconception seems to be the starting point for much research is not conducive to increased knowledge, but also alluded to elsewhere ( Coyne et al., 2020 ).

Why the Focus on Poor Mental Health/Pathology?

The relationship between social media and mental health is likely to be complex, and social media use can be beneficial for maintaining friendships and enriching social life ( Seabrook et al., 2016 ; Birkjær and Kaats, 2019 ; Coyne et al., 2020 ; Orben, 2020 ). This scoping review reveals that the majority of studies focusing on effects of social media use has a clearly stated focus on pathology and detrimental results of social media use. Mainstream media and the public discourse has contributed in creating a culture of fear around social media, with a focus on its negative elements ( Ahn, 2012 ; O’Reilly et al., 2018 ). It is difficult to pin-point why the one-sided focus on the negative effects of social media has been established within the research literature. But likely reasons are elements of “moral panic,” and reports of increases in mental health problems among adolescents in the same period that social media were introduced and became wide-spread ( Birkjær and Kaats, 2019 ). The phenomenon of moral panic typically resurges with the introduction and increasing use of new technologies, as happened with video games, TV, and radio ( Mueller, 2019 ).

The Metrics of Social Media

Social media trends change rapidly, and it is challenging for the research field to keep up. The included studies covered some of the most frequently used social media, but the amount of studies focusing on each social media did not accurately reflect the contemporary distribution of users. Even though sites such as Instagram and Snapchat were covered in some studies, the coverage did not do justice to the amount of users these sites had. Newer social media sites such as TikTok were not mentioned in the included studies even though it has several hundred million daily users ( Mediakix, 2019 ; Wallaroo, 2020 ).

Across the included studies there was some variation in how social media were gauged, but the majority of studies focused on the mere frequency and duration of use. There were little focus on separating between different forms of (inter)actions on social media, as these can vary between being a victim of cyberbullying to participating in healthy community work. Also, few studies differentiated between types of actions (i.e., posting, scrolling, reading), active and passive modes of social media use (i.e., production versus consumption, and level of interactivity), a finding similar to other reports ( Seabrook et al., 2016 ; Verduyn et al., 2017 ; Orben, 2020 ). There is reason to believe that different modes of use on social media platforms are differentially associated with mental health, and a recent narrative review highlight the need to address this in future research ( Orben, 2020 ). One of the included studies found for instance that it is not the total time spent on Facebook or the internet, but the specific amount of time allocated to photo-related activities that is associated with greater symptoms of eating disorders such as thin ideal internalization, self-objectification, weight dissatisfaction, and drive for thinness ( Meier and Gray, 2014 ). This observation can possibly be explained by social comparison mechanisms ( Appel et al., 2016 ) and passive use of social media ( Verduyn et al., 2017 ). The lack of research differentiating social media use and its association with mental health is an important finding of this scoping review and will hopefully contribute to this being included in future studies.

Few studies examined the motivation behind choosing to use social media, or the mental health status of the users when beginning a social media session. It has been reported that young people sometimes choose to enter sites such as Facebook and Twitter as an escape from threats to their mental health such as experiencing overwhelming pressure in daily life ( Boyd, 2014 ). This kind of escapism can be explained through uses and gratifications theory [see for instance ( Coyne et al., 2020 )]. On the other hand, more recent research suggest that additional motivational factors may include the need to control relationships, content, presentation, and impressions ( Throuvala et al., 2019 ), and it is possible that social media use can act as an reinforcement of adolescents’ current moods and motivations ( Birkjær and Kaats, 2019 ). Regardless, it seems obvious that the interplay between online and offline use and underlying motivational mechanisms needs to be better understood.

There has also been some questions about the accuracy when it comes to deciding the amount and frequency of one’s personal social media use. Without measuring duration and frequency of use directly and objectively it is unlikely that subjective self-report of general use is reliable ( Kobayashi and Boase, 2012 ; Scharkow, 2016 , 2019 ; Naab et al., 2019 ). Especially since the potential for social media use is almost omnipresent and the use itself is diverse in nature. Also, due to processes such as social desirability, it is likely that some participants report lower amounts of social media use as excessive use is seen largely undesirable ( Krumpal, 2013 ). Inaccurate reporting of prior social media use could also be a threat to the validity of the reported numbers and thus bias the results reported. Real-time tracking of actual use and modes of use is therefore recommended in future studies to ensure higher accuracy of these aspects of social media use ( Coyne et al., 2020 ; Orben, 2020 ), despite obvious legal and ethical challenges. Another aspect of social media use which does not seem to be addressed is potential spill-over effects, where use of social media leads to potential interest in or thinking about use of – and events or contents on – social media when the individual is offline. When this aspect has been addressed, it seems to be in relation to preoccupations and with a focus on excessive use or addictive behaviors ( Griffiths et al., 2014 ). Conversely, given the ubiquitous and important role of social media, experiences on social media – for better or for worse – are likely to be interconnected with the rest of an individual’s lived experience ( Birkjær and Kaats, 2019 ).

The Studies Seem to Implicitly Think That the Use of Social Media “Causes”/“Affects” Mental Health (Problems)

Most of the included studies establish an implicit causation between social media and mental health. It is assumed that social media use has an impact on mental health. The majority of studies included establish some correlation between more frequent use of social media and poor well-being/mental health, as evident from Supplementary Table 2 . As formerly mentioned, most of the included studies are cross-sectional and cannot shed light into temporality or cause-and-effect. In total, only 16 studies had a longitudinal design, using different types of regression models, latent growth curve models and cross-lagged models. Yet there seems to be an unspoken expectation that the direction of the association is social media use affecting mental health. The reason for this supposition is unclear, but again it is likely that the mainstream media discourse dominated by mostly negative stories and reports of social media use has some impact together with the observed moral panic.

With the increased popularity of social media and internet arrived a reduction of face-to-face contact and supposed increased social isolation ( Kraut et al., 1998 ; Espinoza and Juvonen, 2011 ). This view is described as the displacement hypothesis [see for instance ( Coyne et al., 2020 )]. Having a thriving social life and community with meaningful relations are for many considered vital for well-being and good mental health, and the supposed reduction of sociality were undoubtedly met with skepticism by some. Social media use has increased rapidly among young people over the last two decades along with reports that mental health problems are increasing. Several studies report that there is a rising prevalence of symptom of anxiety and depression among our adolescents ( Bor et al., 2014 ; Olfson et al., 2015 ). The observation that increases in social media use and mental health issues happened in more or less the same time period can have contributed to focus on how use of social media affects mental health problems.

The existence of an implicit causation is supported by the study variables chosen and the lack of positively worded outcomes. Depression, anxiety, alcohol use, psychiatric problems, suicidal behavior and eating disorders are amongst the most studied outcome-variables. On the other side of the spectrum we have well-being, which can oscillate from positive to negative, whilst the measures of pathology only vary from “ill” to “not ill” with positive outcomes not possible.

What Is the Gap in the Literature?

The current literature on social media and mental health among youth is still developing and has several gaps and shortcomings, as evident from this scoping review and other publications ( Seabrook et al., 2016 ; Coyne et al., 2020 ; Keles et al., 2020 ; Orben, 2020 ). Some of the gaps and shortcomings in the field we propose solutions for has been identified in a systematic review from 2019 by Keles et al. (2020) . The majority of the included studies in the current scoping review were cross-sectional, were limited in their inclusion of potential confounders and 3rd variables such as sociodemographics and personality, preventing knowledge about possible cause-and-effect between social media and mental health. There is a lack of longitudinal studies examining the effects of social media over extended periods of time, as well as investigations longitudinally of how mental health impacts social media use. However, since the formal search was ended for this scoping review, some innovative studies have emerged using longitudinal data ( Brunborg and Andreas, 2019 ; Orben et al., 2019 ; Coyne et al., 2020 ). More high quality longitudinal studies of social media use and mental health could help us identify the patterns over time and help us learn about possible cause-and-effect relationships, as well as disentangling between- and within-person associations ( Coyne et al., 2020 ; Orben, 2020 ). Furthermore, both social media use and mental health are complex phenomena in themselves, and future studies need to consider which aspects they want to investigate when trying to understand their relationship. Mechanisms linking social media use and eating disorders are for instance likely to be different than mechanisms linking social media use and symptoms of ADHD.

Our literature search also revealed a paucity of qualitative studies exploring the why’s and how’s of social media use in relation to mental health among adolescents. Few studies examine how youth themselves experience and perceive the relationship between social media and mental health, and the reasons for their continued and frequent use. Qualitatively oriented studies would contribute to a deeper understanding of adolescent’s social media sphere, and their thoughts about the relationship between social media use and mental health [see for instance ( Burnette et al., 2017 )]. For instance, O’Reilly et al. (2018) found that adolescents viewed social media as a threat to mental well-being, and concluded that they buy into the idea that “inherently social media has negative effects on mental wellbeing” and seem to “reify the moral panic that has become endemic to contemporary discourses.” On the other hand, Weinstein found using both quantitative and qualitative data that adolescents’ perceptions of the relationship between social media use and well-being probably is more nuanced, and mostly positive. Another clear gap in the research literature is the lack of focus on potentially positive aspects of social media use. It is obvious that there are some positive sides of the use of social media, and these also need to be investigated further ( Weinstein, 2018 ; Birkjær and Kaats, 2019 ). Gender-specific analyses are also lacking in the research literature, and there is reason to believe that social media use have different characteristics between the genders with different relationships to mental health. In fact, recent findings indicate that not only gender should be considered an important factor when investigating the role of social media in adolescents’ lives, but individual characteristics in general ( Orben et al., 2019 ; Orben, 2020 ). Analyses of socioeconomic status and geographic location are also lacking and it is likely that these factors might play a role the potential association between social media use and mental health. And finally, several studies point to the fact that social media potentially could be a fruitful arena for promoting mental well-being among youth, and developing mental health literacy to better equip our adolescents for the challenges that will surely arise ( O’Reilly et al., 2018 ; Teesson et al., 2020 ).

Research into the association between social media use and mental health and well-being among adolescents is rapidly emerging. The field is characterized by a focus on the association between social media use and negative aspects of mental health and well-being, and where studies focusing on the potentially positive aspects of social media use are lacking. Presently, the majority of studies in the field are quantitatively oriented, with most utilizing a cross-sectional design. An increase in qualitatively oriented studies would add to the field of research by increasing the understanding of adolescents’ social-media life and their own experiences of its association with mental health and well-being. More studies using a longitudinal design would contribute to examining the effects of social media over extended periods of time and help us learn about possible cause-and-effect relationships. Few studies look into individual factors, which may be important for our understanding of the association. Social media use and mental health and well-being are complex phenomena, and future studies could benefit from specifying the type of social media use they focus on when trying to understand its link to mental health. In conclusion, studies including more specific aspects of social media, individual differences and potential intermediate variables, and more studies using a longitudinal design are needed as the research field matures.

Author Contributions

JS conceptualized the review approach and provided general guidance to the research team. VS and JS drafted the first version of this manuscript. JS, GH, and LA developed the draft further based on feedback from the author group. All authors reviewed and approved the final version of the manuscript and have made substantive intellectual contributions to the development of this manuscript.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

We would like to thank Bergen municipality, Hordaland County Council and Western Norway University of Applied Sciences for their collaboration and help with the review. We would also like to thank Senior Librarian Marita Heinz at the Norwegian Institute for Public Health for vital help conducting the literature search.

Funding. This review was partly funded by Regional Research Funds in Norway, funding #RFF297031. No other specific funding was received for the present project. The present project is associated with a larger innovation-project lead by Bergen municipality in Western Norway related to the use of social media and mental health and well-being. The innovation-project is funded by a program initiated by the Norwegian Directorate of Health, and in Vestland county coordinated by the County Council (County Authority). The project aims to explore social media as platform for health-promotion among adolescents.

1 https://rayyan.qcri.org/welcome

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpsyg.2020.01949/full#supplementary-material

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CONCEPTUAL ANALYSIS article

The effect of social media on the development of students’ affective variables.

\r\nMiao Chen,*

  • 1 Science and Technology Department, Nanjing University of Posts and Telecommunications, Nanjing, China
  • 2 School of Marxism, Hohai University, Nanjing, Jiangsu, China
  • 3 Government Enterprise Customer Center, China Mobile Group Jiangsu Co., Ltd., Nanjing, China

The use of social media is incomparably on the rise among students, influenced by the globalized forms of communication and the post-pandemic rush to use multiple social media platforms for education in different fields of study. Though social media has created tremendous chances for sharing ideas and emotions, the kind of social support it provides might fail to meet students’ emotional needs, or the alleged positive effects might be short-lasting. In recent years, several studies have been conducted to explore the potential effects of social media on students’ affective traits, such as stress, anxiety, depression, and so on. The present paper reviews the findings of the exemplary published works of research to shed light on the positive and negative potential effects of the massive use of social media on students’ emotional well-being. This review can be insightful for teachers who tend to take the potential psychological effects of social media for granted. They may want to know more about the actual effects of the over-reliance on and the excessive (and actually obsessive) use of social media on students’ developing certain images of self and certain emotions which are not necessarily positive. There will be implications for pre- and in-service teacher training and professional development programs and all those involved in student affairs.

Introduction

Social media has turned into an essential element of individuals’ lives including students in today’s world of communication. Its use is growing significantly more than ever before especially in the post-pandemic era, marked by a great revolution happening to the educational systems. Recent investigations of using social media show that approximately 3 billion individuals worldwide are now communicating via social media ( Iwamoto and Chun, 2020 ). This growing population of social media users is spending more and more time on social network groupings, as facts and figures show that individuals spend 2 h a day, on average, on a variety of social media applications, exchanging pictures and messages, updating status, tweeting, favoring, and commenting on many updated socially shared information ( Abbott, 2017 ).

Researchers have begun to investigate the psychological effects of using social media on students’ lives. Chukwuere and Chukwuere (2017) maintained that social media platforms can be considered the most important source of changing individuals’ mood, because when someone is passively using a social media platform seemingly with no special purpose, s/he can finally feel that his/her mood has changed as a function of the nature of content overviewed. Therefore, positive and negative moods can easily be transferred among the population using social media networks ( Chukwuere and Chukwuere, 2017 ). This may become increasingly important as students are seen to be using social media platforms more than before and social networking is becoming an integral aspect of their lives. As described by Iwamoto and Chun (2020) , when students are affected by social media posts, especially due to the increasing reliance on social media use in life, they may be encouraged to begin comparing themselves to others or develop great unrealistic expectations of themselves or others, which can have several affective consequences.

Considering the increasing influence of social media on education, the present paper aims to focus on the affective variables such as depression, stress, and anxiety, and how social media can possibly increase or decrease these emotions in student life. The exemplary works of research on this topic in recent years will be reviewed here, hoping to shed light on the positive and negative effects of these ever-growing influential platforms on the psychology of students.

Significance of the study

Though social media, as the name suggests, is expected to keep people connected, probably this social connection is only superficial, and not adequately deep and meaningful to help individuals feel emotionally attached to others. The psychological effects of social media on student life need to be studied in more depth to see whether social media really acts as a social support for students and whether students can use social media to cope with negative emotions and develop positive feelings or not. In other words, knowledge of the potential effects of the growing use of social media on students’ emotional well-being can bridge the gap between the alleged promises of social media and what it actually has to offer to students in terms of self-concept, self-respect, social role, and coping strategies (for stress, anxiety, etc.).

Exemplary general literature on psychological effects of social media

Before getting down to the effects of social media on students’ emotional well-being, some exemplary works of research in recent years on the topic among general populations are reviewed. For one, Aalbers et al. (2018) reported that individuals who spent more time passively working with social media suffered from more intense levels of hopelessness, loneliness, depression, and perceived inferiority. For another, Tang et al. (2013) observed that the procedures of sharing information, commenting, showing likes and dislikes, posting messages, and doing other common activities on social media are correlated with higher stress. Similarly, Ley et al. (2014) described that people who spend 2 h, on average, on social media applications will face many tragic news, posts, and stories which can raise the total intensity of their stress. This stress-provoking effect of social media has been also pinpointed by Weng and Menczer (2015) , who contended that social media becomes a main source of stress because people often share all kinds of posts, comments, and stories ranging from politics and economics, to personal and social affairs. According to Iwamoto and Chun (2020) , anxiety and depression are the negative emotions that an individual may develop when some source of stress is present. In other words, when social media sources become stress-inducing, there are high chances that anxiety and depression also develop.

Charoensukmongkol (2018) reckoned that the mental health and well-being of the global population can be at a great risk through the uncontrolled massive use of social media. These researchers also showed that social media sources can exert negative affective impacts on teenagers, as they can induce more envy and social comparison. According to Fleck and Johnson-Migalski (2015) , though social media, at first, plays the role of a stress-coping strategy, when individuals continue to see stressful conditions (probably experienced and shared by others in media), they begin to develop stress through the passage of time. Chukwuere and Chukwuere (2017) maintained that social media platforms continue to be the major source of changing mood among general populations. For example, someone might be passively using a social media sphere, and s/he may finally find him/herself with a changed mood depending on the nature of the content faced. Then, this good or bad mood is easily shared with others in a flash through the social media. Finally, as Alahmar (2016) described, social media exposes people especially the young generation to new exciting activities and events that may attract them and keep them engaged in different media contexts for hours just passing their time. It usually leads to reduced productivity, reduced academic achievement, and addiction to constant media use ( Alahmar, 2016 ).

The number of studies on the potential psychological effects of social media on people in general is higher than those selectively addressed here. For further insights into this issue, some other suggested works of research include Chang (2012) , Sriwilai and Charoensukmongkol (2016) , and Zareen et al. (2016) . Now, we move to the studies that more specifically explored the effects of social media on students’ affective states.

Review of the affective influences of social media on students

Vygotsky’s mediational theory (see Fernyhough, 2008 ) can be regarded as a main theoretical background for the support of social media on learners’ affective states. Based on this theory, social media can play the role of a mediational means between learners and the real environment. Learners’ understanding of this environment can be mediated by the image shaped via social media. This image can be either close to or different from the reality. In the case of the former, learners can develop their self-image and self-esteem. In the case of the latter, learners might develop unrealistic expectations of themselves by comparing themselves to others. As it will be reviewed below among the affective variables increased or decreased in students under the influence of the massive use of social media are anxiety, stress, depression, distress, rumination, and self-esteem. These effects have been explored more among school students in the age range of 13–18 than university students (above 18), but some studies were investigated among college students as well. Exemplary works of research on these affective variables are reviewed here.

In a cross-sectional study, O’Dea and Campbell (2011) explored the impact of online interactions of social networks on the psychological distress of adolescent students. These researchers found a negative correlation between the time spent on social networking and mental distress. Dumitrache et al. (2012) explored the relations between depression and the identity associated with the use of the popular social media, the Facebook. This study showed significant associations between depression and the number of identity-related information pieces shared on this social network. Neira and Barber (2014) explored the relationship between students’ social media use and depressed mood at teenage. No significant correlation was found between these two variables. In the same year, Tsitsika et al. (2014) explored the associations between excessive use of social media and internalizing emotions. These researchers found a positive correlation between more than 2-h a day use of social media and anxiety and depression.

Hanprathet et al. (2015) reported a statistically significant positive correlation between addiction to Facebook and depression among about a thousand high school students in wealthy populations of Thailand and warned against this psychological threat. Sampasa-Kanyinga and Lewis (2015) examined the relationship between social media use and psychological distress. These researchers found that the use of social media for more than 2 h a day was correlated with a higher intensity of psychological distress. Banjanin et al. (2015) tested the relationship between too much use of social networking and depression, yet found no statistically significant correlation between these two variables. Frison and Eggermont (2016) examined the relationships between different forms of Facebook use, perceived social support of social media, and male and female students’ depressed mood. These researchers found a positive association between the passive use of the Facebook and depression and also between the active use of the social media and depression. Furthermore, the perceived social support of the social media was found to mediate this association. Besides, gender was found as the other factor to mediate this relationship.

Vernon et al. (2017) explored change in negative investment in social networking in relation to change in depression and externalizing behavior. These researchers found that increased investment in social media predicted higher depression in adolescent students, which was a function of the effect of higher levels of disrupted sleep. Barry et al. (2017) explored the associations between the use of social media by adolescents and their psychosocial adjustment. Social media activity showed to be positively and moderately associated with depression and anxiety. Another investigation was focused on secondary school students in China conducted by Li et al. (2017) . The findings showed a mediating role of insomnia on the significant correlation between depression and addiction to social media. In the same year, Yan et al. (2017) aimed to explore the time spent on social networks and its correlation with anxiety among middle school students. They found a significant positive correlation between more than 2-h use of social networks and the intensity of anxiety.

Also in China, Wang et al. (2018) showed that addiction to social networking sites was correlated positively with depression, and this correlation was mediated by rumination. These researchers also found that this mediating effect was moderated by self-esteem. It means that the effect of addiction on depression was compounded by low self-esteem through rumination. In another work of research, Drouin et al. (2018) showed that though social media is expected to act as a form of social support for the majority of university students, it can adversely affect students’ mental well-being, especially for those who already have high levels of anxiety and depression. In their research, the social media resources were found to be stress-inducing for half of the participants, all university students. The higher education population was also studied by Iwamoto and Chun (2020) . These researchers investigated the emotional effects of social media in higher education and found that the socially supportive role of social media was overshadowed in the long run in university students’ lives and, instead, fed into their perceived depression, anxiety, and stress.

Keles et al. (2020) provided a systematic review of the effect of social media on young and teenage students’ depression, psychological distress, and anxiety. They found that depression acted as the most frequent affective variable measured. The most salient risk factors of psychological distress, anxiety, and depression based on the systematic review were activities such as repeated checking for messages, personal investment, the time spent on social media, and problematic or addictive use. Similarly, Mathewson (2020) investigated the effect of using social media on college students’ mental health. The participants stated the experience of anxiety, depression, and suicidality (thoughts of suicide or attempts to suicide). The findings showed that the types and frequency of using social media and the students’ perceived mental health were significantly correlated with each other.

The body of research on the effect of social media on students’ affective and emotional states has led to mixed results. The existing literature shows that there are some positive and some negative affective impacts. Yet, it seems that the latter is pre-dominant. Mathewson (2020) attributed these divergent positive and negative effects to the different theoretical frameworks adopted in different studies and also the different contexts (different countries with whole different educational systems). According to Fredrickson’s broaden-and-build theory of positive emotions ( Fredrickson, 2001 ), the mental repertoires of learners can be built and broadened by how they feel. For instance, some external stimuli might provoke negative emotions such as anxiety and depression in learners. Having experienced these negative emotions, students might repeatedly check their messages on social media or get addicted to them. As a result, their cognitive repertoire and mental capacity might become limited and they might lose their concentration during their learning process. On the other hand, it should be noted that by feeling positive, learners might take full advantage of the affordances of the social media and; thus, be able to follow their learning goals strategically. This point should be highlighted that the link between the use of social media and affective states is bi-directional. Therefore, strategic use of social media or its addictive use by students can direct them toward either positive experiences like enjoyment or negative ones such as anxiety and depression. Also, these mixed positive and negative effects are similar to the findings of several other relevant studies on general populations’ psychological and emotional health. A number of studies (with general research populations not necessarily students) showed that social networks have facilitated the way of staying in touch with family and friends living far away as well as an increased social support ( Zhang, 2017 ). Given the positive and negative emotional effects of social media, social media can either scaffold the emotional repertoire of students, which can develop positive emotions in learners, or induce negative provokers in them, based on which learners might feel negative emotions such as anxiety and depression. However, admittedly, social media has also generated a domain that encourages the act of comparing lives, and striving for approval; therefore, it establishes and internalizes unrealistic perceptions ( Virden et al., 2014 ; Radovic et al., 2017 ).

It should be mentioned that the susceptibility of affective variables to social media should be interpreted from a dynamic lens. This means that the ecology of the social media can make changes in the emotional experiences of learners. More specifically, students’ affective variables might self-organize into different states under the influence of social media. As for the positive correlation found in many studies between the use of social media and such negative effects as anxiety, depression, and stress, it can be hypothesized that this correlation is induced by the continuous comparison the individual makes and the perception that others are doing better than him/her influenced by the posts that appear on social media. Using social media can play a major role in university students’ psychological well-being than expected. Though most of these studies were correlational, and correlation is not the same as causation, as the studies show that the number of participants experiencing these negative emotions under the influence of social media is significantly high, more extensive research is highly suggested to explore causal effects ( Mathewson, 2020 ).

As the review of exemplary studies showed, some believed that social media increased comparisons that students made between themselves and others. This finding ratifies the relevance of the Interpretation Comparison Model ( Stapel and Koomen, 2000 ; Stapel, 2007 ) and Festinger’s (1954) Social Comparison Theory. Concerning the negative effects of social media on students’ psychology, it can be argued that individuals may fail to understand that the content presented in social media is usually changed to only represent the attractive aspects of people’s lives, showing an unrealistic image of things. We can add that this argument also supports the relevance of the Social Comparison Theory and the Interpretation Comparison Model ( Stapel and Koomen, 2000 ; Stapel, 2007 ), because social media sets standards that students think they should compare themselves with. A constant observation of how other students or peers are showing their instances of achievement leads to higher self-evaluation ( Stapel and Koomen, 2000 ). It is conjectured that the ubiquitous role of social media in student life establishes unrealistic expectations and promotes continuous comparison as also pinpointed in the Interpretation Comparison Model ( Stapel and Koomen, 2000 ; Stapel, 2007 ).

Implications of the study

The use of social media is ever increasing among students, both at school and university, which is partly because of the promises of technological advances in communication services and partly because of the increased use of social networks for educational purposes in recent years after the pandemic. This consistent use of social media is not expected to leave students’ psychological, affective and emotional states untouched. Thus, it is necessary to know how the growing usage of social networks is associated with students’ affective health on different aspects. Therefore, we found it useful to summarize the research findings in recent years in this respect. If those somehow in charge of student affairs in educational settings are aware of the potential positive or negative effects of social media usage on students, they can better understand the complexities of students’ needs and are better capable of meeting them.

Psychological counseling programs can be initiated at schools or universities to check upon the latest state of students’ mental and emotional health influenced by the pervasive use of social media. The counselors can be made aware of the potential adverse effects of social networking and can adapt the content of their inquiries accordingly. Knowledge of the potential reasons for student anxiety, depression, and stress can help school or university counselors to find individualized coping strategies when they diagnose any symptom of distress in students influenced by an excessive use of social networking.

Admittedly, it is neither possible to discard the use of social media in today’s academic life, nor to keep students’ use of social networks fully controlled. Certainly, the educational space in today’s world cannot do without the social media, which has turned into an integral part of everybody’s life. Yet, probably students need to be instructed on how to take advantage of the media and to be the least affected negatively by its occasional superficial and unrepresentative content. Compensatory programs might be needed at schools or universities to encourage students to avoid making unrealistic and impartial comparisons of themselves and the flamboyant images of others displayed on social media. Students can be taught to develop self-appreciation and self-care while continuing to use the media to their benefit.

The teachers’ role as well as the curriculum developers’ role are becoming more important than ever, as they can significantly help to moderate the adverse effects of the pervasive social media use on students’ mental and emotional health. The kind of groupings formed for instructional purposes, for example, in social media can be done with greater care by teachers to make sure that the members of the groups are homogeneous and the tasks and activities shared in the groups are quite relevant and realistic. The teachers cannot always be in a full control of students’ use of social media, and the other fact is that students do not always and only use social media for educational purposes. They spend more time on social media for communicating with friends or strangers or possibly they just passively receive the content produced out of any educational scope just for entertainment. This uncontrolled and unrealistic content may give them a false image of life events and can threaten their mental and emotional health. Thus, teachers can try to make students aware of the potential hazards of investing too much of their time on following pages or people that publish false and misleading information about their personal or social identities. As students, logically expected, spend more time with their teachers than counselors, they may be better and more receptive to the advice given by the former than the latter.

Teachers may not be in full control of their students’ use of social media, but they have always played an active role in motivating or demotivating students to take particular measures in their academic lives. If teachers are informed of the recent research findings about the potential effects of massively using social media on students, they may find ways to reduce students’ distraction or confusion in class due to the excessive or over-reliant use of these networks. Educators may more often be mesmerized by the promises of technology-, computer- and mobile-assisted learning. They may tend to encourage the use of social media hoping to benefit students’ social and interpersonal skills, self-confidence, stress-managing and the like. Yet, they may be unaware of the potential adverse effects on students’ emotional well-being and, thus, may find the review of the recent relevant research findings insightful. Also, teachers can mediate between learners and social media to manipulate the time learners spend on social media. Research has mainly indicated that students’ emotional experiences are mainly dependent on teachers’ pedagogical approach. They should refrain learners from excessive use of, or overreliance on, social media. Raising learners’ awareness of this fact that individuals should develop their own path of development for learning, and not build their development based on unrealistic comparison of their competences with those of others, can help them consider positive values for their activities on social media and, thus, experience positive emotions.

At higher education, students’ needs are more life-like. For example, their employment-seeking spirits might lead them to create accounts in many social networks, hoping for a better future. However, membership in many of these networks may end in the mere waste of the time that could otherwise be spent on actual on-campus cooperative projects. Universities can provide more on-campus resources both for research and work experience purposes from which the students can benefit more than the cyberspace that can be tricky on many occasions. Two main theories underlying some negative emotions like boredom and anxiety are over-stimulation and under-stimulation. Thus, what learners feel out of their involvement in social media might be directed toward negative emotions due to the stimulating environment of social media. This stimulating environment makes learners rely too much, and spend too much time, on social media or use them obsessively. As a result, they might feel anxious or depressed. Given the ubiquity of social media, these negative emotions can be replaced with positive emotions if learners become aware of the psychological effects of social media. Regarding the affordances of social media for learners, they can take advantage of the potential affordances of these media such as improving their literacy, broadening their communication skills, or enhancing their distance learning opportunities.

A review of the research findings on the relationship between social media and students’ affective traits revealed both positive and negative findings. Yet, the instances of the latter were more salient and the negative psychological symptoms such as depression, anxiety, and stress have been far from negligible. These findings were discussed in relation to some more relevant theories such as the social comparison theory, which predicted that most of the potential issues with the young generation’s excessive use of social media were induced by the unfair comparisons they made between their own lives and the unrealistic portrayal of others’ on social media. Teachers, education policymakers, curriculum developers, and all those in charge of the student affairs at schools and universities should be made aware of the psychological effects of the pervasive use of social media on students, and the potential threats.

It should be reminded that the alleged socially supportive and communicative promises of the prevalent use of social networking in student life might not be fully realized in practice. Students may lose self-appreciation and gratitude when they compare their current state of life with the snapshots of others’ or peers’. A depressed or stressed-out mood can follow. Students at schools or universities need to learn self-worth to resist the adverse effects of the superficial support they receive from social media. Along this way, they should be assisted by the family and those in charge at schools or universities, most importantly the teachers. As already suggested, counseling programs might help with raising students’ awareness of the potential psychological threats of social media to their health. Considering the ubiquity of social media in everybody’ life including student life worldwide, it seems that more coping and compensatory strategies should be contrived to moderate the adverse psychological effects of the pervasive use of social media on students. Also, the affective influences of social media should not be generalized but they need to be interpreted from an ecological or contextual perspective. This means that learners might have different emotions at different times or different contexts while being involved in social media. More specifically, given the stative approach to learners’ emotions, what learners emotionally experience in their application of social media can be bound to their intra-personal and interpersonal experiences. This means that the same learner at different time points might go through different emotions Also, learners’ emotional states as a result of their engagement in social media cannot be necessarily generalized to all learners in a class.

As the majority of studies on the psychological effects of social media on student life have been conducted on school students than in higher education, it seems it is too soon to make any conclusive remark on this population exclusively. Probably, in future, further studies of the psychological complexities of students at higher education and a better knowledge of their needs can pave the way for making more insightful conclusions about the effects of social media on their affective states.

Suggestions for further research

The majority of studies on the potential effects of social media usage on students’ psychological well-being are either quantitative or qualitative in type, each with many limitations. Presumably, mixed approaches in near future can better provide a comprehensive assessment of these potential associations. Moreover, most studies on this topic have been cross-sectional in type. There is a significant dearth of longitudinal investigation on the effect of social media on developing positive or negative emotions in students. This seems to be essential as different affective factors such as anxiety, stress, self-esteem, and the like have a developmental nature. Traditional research methods with single-shot designs for data collection fail to capture the nuances of changes in these affective variables. It can be expected that more longitudinal studies in future can show how the continuous use of social media can affect the fluctuations of any of these affective variables during the different academic courses students pass at school or university.

As already raised in some works of research reviewed, the different patterns of impacts of social media on student life depend largely on the educational context. Thus, the same research designs with the same academic grade students and even the same age groups can lead to different findings concerning the effects of social media on student psychology in different countries. In other words, the potential positive and negative effects of popular social media like Facebook, Snapchat, Twitter, etc., on students’ affective conditions can differ across different educational settings in different host countries. Thus, significantly more research is needed in different contexts and cultures to compare the results.

There is also a need for further research on the higher education students and how their affective conditions are positively and negatively affected by the prevalent use of social media. University students’ psychological needs might be different from other academic grades and, thus, the patterns of changes that the overall use of social networking can create in their emotions can be also different. Their main reasons for using social media might be different from school students as well, which need to be investigated more thoroughly. The sorts of interventions needed to moderate the potential negative effects of social networking on them can be different too, all requiring a new line of research in education domain.

Finally, there are hopes that considering the ever-increasing popularity of social networking in education, the potential psychological effects of social media on teachers be explored as well. Though teacher psychology has only recently been considered for research, the literature has provided profound insights into teachers developing stress, motivation, self-esteem, and many other emotions. In today’s world driven by global communications in the cyberspace, teachers like everyone else are affecting and being affected by social networking. The comparison theory can hold true for teachers too. Thus, similar threats (of social media) to self-esteem and self-worth can be there for teachers too besides students, which are worth investigating qualitatively and quantitatively.

Probably a new line of research can be initiated to explore the co-development of teacher and learner psychological traits under the influence of social media use in longitudinal studies. These will certainly entail sophisticated research methods to be capable of unraveling the nuances of variation in these traits and their mutual effects, for example, stress, motivation, and self-esteem. If these are incorporated within mixed-approach works of research, more comprehensive and better insightful findings can be expected to emerge. Correlational studies need to be followed by causal studies in educational settings. As many conditions of the educational settings do not allow for having control groups or randomization, probably, experimental studies do not help with this. Innovative research methods, case studies or else, can be used to further explore the causal relations among the different features of social media use and the development of different affective variables in teachers or learners. Examples of such innovative research methods can be process tracing, qualitative comparative analysis, and longitudinal latent factor modeling (for a more comprehensive view, see Hiver and Al-Hoorie, 2019 ).

Author contributions

Both authors listed have made a substantial, direct, and intellectual contribution to the work, and approved it for publication.

This study was sponsored by Wuxi Philosophy and Social Sciences bidding project—“Special Project for Safeguarding the Rights and Interests of Workers in the New Form of Employment” (Grant No. WXSK22-GH-13). This study was sponsored by the Key Project of Party Building and Ideological and Political Education Research of Nanjing University of Posts and Telecommunications—“Research on the Guidance and Countermeasures of Network Public Opinion in Colleges and Universities in the Modern Times” (Grant No. XC 2021002).

Conflict of interest

Author XX was employed by China Mobile Group Jiangsu Co., Ltd.

The remaining author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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Keywords : affective variables, education, emotions, social media, post-pandemic, emotional needs

Citation: Chen M and Xiao X (2022) The effect of social media on the development of students’ affective variables. Front. Psychol. 13:1010766. doi: 10.3389/fpsyg.2022.1010766

Received: 03 August 2022; Accepted: 25 August 2022; Published: 15 September 2022.

Reviewed by:

Copyright © 2022 Chen and Xiao. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Miao Chen, [email protected] ; Xin Xiao, [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

  • Systematic review
  • Open access
  • Published: 19 February 2024

‘It depends’: what 86 systematic reviews tell us about what strategies to use to support the use of research in clinical practice

  • Annette Boaz   ORCID: orcid.org/0000-0003-0557-1294 1 ,
  • Juan Baeza 2 ,
  • Alec Fraser   ORCID: orcid.org/0000-0003-1121-1551 2 &
  • Erik Persson 3  

Implementation Science volume  19 , Article number:  15 ( 2024 ) Cite this article

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The gap between research findings and clinical practice is well documented and a range of strategies have been developed to support the implementation of research into clinical practice. The objective of this study was to update and extend two previous reviews of systematic reviews of strategies designed to implement research evidence into clinical practice.

We developed a comprehensive systematic literature search strategy based on the terms used in the previous reviews to identify studies that looked explicitly at interventions designed to turn research evidence into practice. The search was performed in June 2022 in four electronic databases: Medline, Embase, Cochrane and Epistemonikos. We searched from January 2010 up to June 2022 and applied no language restrictions. Two independent reviewers appraised the quality of included studies using a quality assessment checklist. To reduce the risk of bias, papers were excluded following discussion between all members of the team. Data were synthesised using descriptive and narrative techniques to identify themes and patterns linked to intervention strategies, targeted behaviours, study settings and study outcomes.

We identified 32 reviews conducted between 2010 and 2022. The reviews are mainly of multi-faceted interventions ( n  = 20) although there are reviews focusing on single strategies (ICT, educational, reminders, local opinion leaders, audit and feedback, social media and toolkits). The majority of reviews report strategies achieving small impacts (normally on processes of care). There is much less evidence that these strategies have shifted patient outcomes. Furthermore, a lot of nuance lies behind these headline findings, and this is increasingly commented upon in the reviews themselves.

Combined with the two previous reviews, 86 systematic reviews of strategies to increase the implementation of research into clinical practice have been identified. We need to shift the emphasis away from isolating individual and multi-faceted interventions to better understanding and building more situated, relational and organisational capability to support the use of research in clinical practice. This will involve drawing on a wider range of research perspectives (including social science) in primary studies and diversifying the types of synthesis undertaken to include approaches such as realist synthesis which facilitate exploration of the context in which strategies are employed.

Peer Review reports

Contribution to the literature

Considerable time and money is invested in implementing and evaluating strategies to increase the implementation of research into clinical practice.

The growing body of evidence is not providing the anticipated clear lessons to support improved implementation.

Instead what is needed is better understanding and building more situated, relational and organisational capability to support the use of research in clinical practice.

This would involve a more central role in implementation science for a wider range of perspectives, especially from the social, economic, political and behavioural sciences and for greater use of different types of synthesis, such as realist synthesis.

Introduction

The gap between research findings and clinical practice is well documented and a range of interventions has been developed to increase the implementation of research into clinical practice [ 1 , 2 ]. In recent years researchers have worked to improve the consistency in the ways in which these interventions (often called strategies) are described to support their evaluation. One notable development has been the emergence of Implementation Science as a field focusing explicitly on “the scientific study of methods to promote the systematic uptake of research findings and other evidence-based practices into routine practice” ([ 3 ] p. 1). The work of implementation science focuses on closing, or at least narrowing, the gap between research and practice. One contribution has been to map existing interventions, identifying 73 discreet strategies to support research implementation [ 4 ] which have been grouped into 9 clusters [ 5 ]. The authors note that they have not considered the evidence of effectiveness of the individual strategies and that a next step is to understand better which strategies perform best in which combinations and for what purposes [ 4 ]. Other authors have noted that there is also scope to learn more from other related fields of study such as policy implementation [ 6 ] and to draw on methods designed to support the evaluation of complex interventions [ 7 ].

The increase in activity designed to support the implementation of research into practice and improvements in reporting provided the impetus for an update of a review of systematic reviews of the effectiveness of interventions designed to support the use of research in clinical practice [ 8 ] which was itself an update of the review conducted by Grimshaw and colleagues in 2001. The 2001 review [ 9 ] identified 41 reviews considering a range of strategies including educational interventions, audit and feedback, computerised decision support to financial incentives and combined interventions. The authors concluded that all the interventions had the potential to promote the uptake of evidence in practice, although no one intervention seemed to be more effective than the others in all settings. They concluded that combined interventions were more likely to be effective than single interventions. The 2011 review identified a further 13 systematic reviews containing 313 discrete primary studies. Consistent with the previous review, four main strategy types were identified: audit and feedback; computerised decision support; opinion leaders; and multi-faceted interventions (MFIs). Nine of the reviews reported on MFIs. The review highlighted the small effects of single interventions such as audit and feedback, computerised decision support and opinion leaders. MFIs claimed an improvement in effectiveness over single interventions, although effect sizes remained small to moderate and this improvement in effectiveness relating to MFIs has been questioned in a subsequent review [ 10 ]. In updating the review, we anticipated a larger pool of reviews and an opportunity to consolidate learning from more recent systematic reviews of interventions.

This review updates and extends our previous review of systematic reviews of interventions designed to implement research evidence into clinical practice. To identify potentially relevant peer-reviewed research papers, we developed a comprehensive systematic literature search strategy based on the terms used in the Grimshaw et al. [ 9 ] and Boaz, Baeza and Fraser [ 8 ] overview articles. To ensure optimal retrieval, our search strategy was refined with support from an expert university librarian, considering the ongoing improvements in the development of search filters for systematic reviews since our first review [ 11 ]. We also wanted to include technology-related terms (e.g. apps, algorithms, machine learning, artificial intelligence) to find studies that explored interventions based on the use of technological innovations as mechanistic tools for increasing the use of evidence into practice (see Additional file 1 : Appendix A for full search strategy).

The search was performed in June 2022 in the following electronic databases: Medline, Embase, Cochrane and Epistemonikos. We searched for articles published since the 2011 review. We searched from January 2010 up to June 2022 and applied no language restrictions. Reference lists of relevant papers were also examined.

We uploaded the results using EPPI-Reviewer, a web-based tool that facilitated semi-automation of the screening process and removal of duplicate studies. We made particular use of a priority screening function to reduce screening workload and avoid ‘data deluge’ [ 12 ]. Through machine learning, one reviewer screened a smaller number of records ( n  = 1200) to train the software to predict whether a given record was more likely to be relevant or irrelevant, thus pulling the relevant studies towards the beginning of the screening process. This automation did not replace manual work but helped the reviewer to identify eligible studies more quickly. During the selection process, we included studies that looked explicitly at interventions designed to turn research evidence into practice. Studies were included if they met the following pre-determined inclusion criteria:

The study was a systematic review

Search terms were included

Focused on the implementation of research evidence into practice

The methodological quality of the included studies was assessed as part of the review

Study populations included healthcare providers and patients. The EPOC taxonomy [ 13 ] was used to categorise the strategies. The EPOC taxonomy has four domains: delivery arrangements, financial arrangements, governance arrangements and implementation strategies. The implementation strategies domain includes 20 strategies targeted at healthcare workers. Numerous EPOC strategies were assessed in the review including educational strategies, local opinion leaders, reminders, ICT-focused approaches and audit and feedback. Some strategies that did not fit easily within the EPOC categories were also included. These were social media strategies and toolkits, and multi-faceted interventions (MFIs) (see Table  2 ). Some systematic reviews included comparisons of different interventions while other reviews compared one type of intervention against a control group. Outcomes related to improvements in health care processes or patient well-being. Numerous individual study types (RCT, CCT, BA, ITS) were included within the systematic reviews.

We excluded papers that:

Focused on changing patient rather than provider behaviour

Had no demonstrable outcomes

Made unclear or no reference to research evidence

The last of these criteria was sometimes difficult to judge, and there was considerable discussion amongst the research team as to whether the link between research evidence and practice was sufficiently explicit in the interventions analysed. As we discussed in the previous review [ 8 ] in the field of healthcare, the principle of evidence-based practice is widely acknowledged and tools to change behaviour such as guidelines are often seen to be an implicit codification of evidence, despite the fact that this is not always the case.

Reviewers employed a two-stage process to select papers for inclusion. First, all titles and abstracts were screened by one reviewer to determine whether the study met the inclusion criteria. Two papers [ 14 , 15 ] were identified that fell just before the 2010 cut-off. As they were not identified in the searches for the first review [ 8 ] they were included and progressed to assessment. Each paper was rated as include, exclude or maybe. The full texts of 111 relevant papers were assessed independently by at least two authors. To reduce the risk of bias, papers were excluded following discussion between all members of the team. 32 papers met the inclusion criteria and proceeded to data extraction. The study selection procedure is documented in a PRISMA literature flow diagram (see Fig.  1 ). We were able to include French, Spanish and Portuguese papers in the selection reflecting the language skills in the study team, but none of the papers identified met the inclusion criteria. Other non- English language papers were excluded.

figure 1

PRISMA flow diagram. Source: authors

One reviewer extracted data on strategy type, number of included studies, local, target population, effectiveness and scope of impact from the included studies. Two reviewers then independently read each paper and noted key findings and broad themes of interest which were then discussed amongst the wider authorial team. Two independent reviewers appraised the quality of included studies using a Quality Assessment Checklist based on Oxman and Guyatt [ 16 ] and Francke et al. [ 17 ]. Each study was rated a quality score ranging from 1 (extensive flaws) to 7 (minimal flaws) (see Additional file 2 : Appendix B). All disagreements were resolved through discussion. Studies were not excluded in this updated overview based on methodological quality as we aimed to reflect the full extent of current research into this topic.

The extracted data were synthesised using descriptive and narrative techniques to identify themes and patterns in the data linked to intervention strategies, targeted behaviours, study settings and study outcomes.

Thirty-two studies were included in the systematic review. Table 1. provides a detailed overview of the included systematic reviews comprising reference, strategy type, quality score, number of included studies, local, target population, effectiveness and scope of impact (see Table  1. at the end of the manuscript). Overall, the quality of the studies was high. Twenty-three studies scored 7, six studies scored 6, one study scored 5, one study scored 4 and one study scored 3. The primary focus of the review was on reviews of effectiveness studies, but a small number of reviews did include data from a wider range of methods including qualitative studies which added to the analysis in the papers [ 18 , 19 , 20 , 21 ]. The majority of reviews report strategies achieving small impacts (normally on processes of care). There is much less evidence that these strategies have shifted patient outcomes. In this section, we discuss the different EPOC-defined implementation strategies in turn. Interestingly, we found only two ‘new’ approaches in this review that did not fit into the existing EPOC approaches. These are a review focused on the use of social media and a review considering toolkits. In addition to single interventions, we also discuss multi-faceted interventions. These were the most common intervention approach overall. A summary is provided in Table  2 .

Educational strategies

The overview identified three systematic reviews focusing on educational strategies. Grudniewicz et al. [ 22 ] explored the effectiveness of printed educational materials on primary care physician knowledge, behaviour and patient outcomes and concluded they were not effective in any of these aspects. Koota, Kääriäinen and Melender [ 23 ] focused on educational interventions promoting evidence-based practice among emergency room/accident and emergency nurses and found that interventions involving face-to-face contact led to significant or highly significant effects on patient benefits and emergency nurses’ knowledge, skills and behaviour. Interventions using written self-directed learning materials also led to significant improvements in nurses’ knowledge of evidence-based practice. Although the quality of the studies was high, the review primarily included small studies with low response rates, and many of them relied on self-assessed outcomes; consequently, the strength of the evidence for these outcomes is modest. Wu et al. [ 20 ] questioned if educational interventions aimed at nurses to support the implementation of evidence-based practice improve patient outcomes. Although based on evaluation projects and qualitative data, their results also suggest that positive changes on patient outcomes can be made following the implementation of specific evidence-based approaches (or projects). The differing positive outcomes for educational strategies aimed at nurses might indicate that the target audience is important.

Local opinion leaders

Flodgren et al. [ 24 ] was the only systemic review focusing solely on opinion leaders. The review found that local opinion leaders alone, or in combination with other interventions, can be effective in promoting evidence‐based practice, but this varies both within and between studies and the effect on patient outcomes is uncertain. The review found that, overall, any intervention involving opinion leaders probably improves healthcare professionals’ compliance with evidence-based practice but varies within and across studies. However, how opinion leaders had an impact could not be determined because of insufficient details were provided, illustrating that reporting specific details in published studies is important if diffusion of effective methods of increasing evidence-based practice is to be spread across a system. The usefulness of this review is questionable because it cannot provide evidence of what is an effective opinion leader, whether teams of opinion leaders or a single opinion leader are most effective, or the most effective methods used by opinion leaders.

Pantoja et al. [ 26 ] was the only systemic review focusing solely on manually generated reminders delivered on paper included in the overview. The review explored how these affected professional practice and patient outcomes. The review concluded that manually generated reminders delivered on paper as a single intervention probably led to small to moderate increases in adherence to clinical recommendations, and they could be used as a single quality improvement intervention. However, the authors indicated that this intervention would make little or no difference to patient outcomes. The authors state that such a low-tech intervention may be useful in low- and middle-income countries where paper records are more likely to be the norm.

ICT-focused approaches

The three ICT-focused reviews [ 14 , 27 , 28 ] showed mixed results. Jamal, McKenzie and Clark [ 14 ] explored the impact of health information technology on the quality of medical and health care. They examined the impact of electronic health record, computerised provider order-entry, or decision support system. This showed a positive improvement in adherence to evidence-based guidelines but not to patient outcomes. The number of studies included in the review was low and so a conclusive recommendation could not be reached based on this review. Similarly, Brown et al. [ 28 ] found that technology-enabled knowledge translation interventions may improve knowledge of health professionals, but all eight studies raised concerns of bias. The De Angelis et al. [ 27 ] review was more promising, reporting that ICT can be a good way of disseminating clinical practice guidelines but conclude that it is unclear which type of ICT method is the most effective.

Audit and feedback

Sykes, McAnuff and Kolehmainen [ 29 ] examined whether audit and feedback were effective in dementia care and concluded that it remains unclear which ingredients of audit and feedback are successful as the reviewed papers illustrated large variations in the effectiveness of interventions using audit and feedback.

Non-EPOC listed strategies: social media, toolkits

There were two new (non-EPOC listed) intervention types identified in this review compared to the 2011 review — fewer than anticipated. We categorised a third — ‘care bundles’ [ 36 ] as a multi-faceted intervention due to its description in practice and a fourth — ‘Technology Enhanced Knowledge Transfer’ [ 28 ] was classified as an ICT-focused approach. The first new strategy was identified in Bhatt et al.’s [ 30 ] systematic review of the use of social media for the dissemination of clinical practice guidelines. They reported that the use of social media resulted in a significant improvement in knowledge and compliance with evidence-based guidelines compared with more traditional methods. They noted that a wide selection of different healthcare professionals and patients engaged with this type of social media and its global reach may be significant for low- and middle-income countries. This review was also noteworthy for developing a simple stepwise method for using social media for the dissemination of clinical practice guidelines. However, it is debatable whether social media can be classified as an intervention or just a different way of delivering an intervention. For example, the review discussed involving opinion leaders and patient advocates through social media. However, this was a small review that included only five studies, so further research in this new area is needed. Yamada et al. [ 31 ] draw on 39 studies to explore the application of toolkits, 18 of which had toolkits embedded within larger KT interventions, and 21 of which evaluated toolkits as standalone interventions. The individual component strategies of the toolkits were highly variable though the authors suggest that they align most closely with educational strategies. The authors conclude that toolkits as either standalone strategies or as part of MFIs hold some promise for facilitating evidence use in practice but caution that the quality of many of the primary studies included is considered weak limiting these findings.

Multi-faceted interventions

The majority of the systematic reviews ( n  = 20) reported on more than one intervention type. Some of these systematic reviews focus exclusively on multi-faceted interventions, whilst others compare different single or combined interventions aimed at achieving similar outcomes in particular settings. While these two approaches are often described in a similar way, they are actually quite distinct from each other as the former report how multiple strategies may be strategically combined in pursuance of an agreed goal, whilst the latter report how different strategies may be incidentally used in sometimes contrasting settings in the pursuance of similar goals. Ariyo et al. [ 35 ] helpfully summarise five key elements often found in effective MFI strategies in LMICs — but which may also be transferrable to HICs. First, effective MFIs encourage a multi-disciplinary approach acknowledging the roles played by different professional groups to collectively incorporate evidence-informed practice. Second, they utilise leadership drawing on a wide set of clinical and non-clinical actors including managers and even government officials. Third, multiple types of educational practices are utilised — including input from patients as stakeholders in some cases. Fourth, protocols, checklists and bundles are used — most effectively when local ownership is encouraged. Finally, most MFIs included an emphasis on monitoring and evaluation [ 35 ]. In contrast, other studies offer little information about the nature of the different MFI components of included studies which makes it difficult to extrapolate much learning from them in relation to why or how MFIs might affect practice (e.g. [ 28 , 38 ]). Ultimately, context matters, which some review authors argue makes it difficult to say with real certainty whether single or MFI strategies are superior (e.g. [ 21 , 27 ]). Taking all the systematic reviews together we may conclude that MFIs appear to be more likely to generate positive results than single interventions (e.g. [ 34 , 45 ]) though other reviews should make us cautious (e.g. [ 32 , 43 ]).

While multi-faceted interventions still seem to be more effective than single-strategy interventions, there were important distinctions between how the results of reviews of MFIs are interpreted in this review as compared to the previous reviews [ 8 , 9 ], reflecting greater nuance and debate in the literature. This was particularly noticeable where the effectiveness of MFIs was compared to single strategies, reflecting developments widely discussed in previous studies [ 10 ]. We found that most systematic reviews are bounded by their clinical, professional, spatial, system, or setting criteria and often seek to draw out implications for the implementation of evidence in their areas of specific interest (such as nursing or acute care). Frequently this means combining all relevant studies to explore the respective foci of each systematic review. Therefore, most reviews we categorised as MFIs actually include highly variable numbers and combinations of intervention strategies and highly heterogeneous original study designs. This makes statistical analyses of the type used by Squires et al. [ 10 ] on the three reviews in their paper not possible. Further, it also makes extrapolating findings and commenting on broad themes complex and difficult. This may suggest that future research should shift its focus from merely examining ‘what works’ to ‘what works where and what works for whom’ — perhaps pointing to the value of realist approaches to these complex review topics [ 48 , 49 ] and other more theory-informed approaches [ 50 ].

Some reviews have a relatively small number of studies (i.e. fewer than 10) and the authors are often understandably reluctant to engage with wider debates about the implications of their findings. Other larger studies do engage in deeper discussions about internal comparisons of findings across included studies and also contextualise these in wider debates. Some of the most informative studies (e.g. [ 35 , 40 ]) move beyond EPOC categories and contextualise MFIs within wider systems thinking and implementation theory. This distinction between MFIs and single interventions can actually be very useful as it offers lessons about the contexts in which individual interventions might have bounded effectiveness (i.e. educational interventions for individual change). Taken as a whole, this may also then help in terms of how and when to conjoin single interventions into effective MFIs.

In the two previous reviews, a consistent finding was that MFIs were more effective than single interventions [ 8 , 9 ]. However, like Squires et al. [ 10 ] this overview is more equivocal on this important issue. There are four points which may help account for the differences in findings in this regard. Firstly, the diversity of the systematic reviews in terms of clinical topic or setting is an important factor. Secondly, there is heterogeneity of the studies within the included systematic reviews themselves. Thirdly, there is a lack of consistency with regards to the definition and strategies included within of MFIs. Finally, there are epistemological differences across the papers and the reviews. This means that the results that are presented depend on the methods used to measure, report, and synthesise them. For instance, some reviews highlight that education strategies can be useful to improve provider understanding — but without wider organisational or system-level change, they may struggle to deliver sustained transformation [ 19 , 44 ].

It is also worth highlighting the importance of the theory of change underlying the different interventions. Where authors of the systematic reviews draw on theory, there is space to discuss/explain findings. We note a distinction between theoretical and atheoretical systematic review discussion sections. Atheoretical reviews tend to present acontextual findings (for instance, one study found very positive results for one intervention, and this gets highlighted in the abstract) whilst theoretically informed reviews attempt to contextualise and explain patterns within the included studies. Theory-informed systematic reviews seem more likely to offer more profound and useful insights (see [ 19 , 35 , 40 , 43 , 45 ]). We find that the most insightful systematic reviews of MFIs engage in theoretical generalisation — they attempt to go beyond the data of individual studies and discuss the wider implications of the findings of the studies within their reviews drawing on implementation theory. At the same time, they highlight the active role of context and the wider relational and system-wide issues linked to implementation. It is these types of investigations that can help providers further develop evidence-based practice.

This overview has identified a small, but insightful set of papers that interrogate and help theorise why, how, for whom, and in which circumstances it might be the case that MFIs are superior (see [ 19 , 35 , 40 ] once more). At the level of this overview — and in most of the systematic reviews included — it appears to be the case that MFIs struggle with the question of attribution. In addition, there are other important elements that are often unmeasured, or unreported (e.g. costs of the intervention — see [ 40 ]). Finally, the stronger systematic reviews [ 19 , 35 , 40 , 43 , 45 ] engage with systems issues, human agency and context [ 18 ] in a way that was not evident in the systematic reviews identified in the previous reviews [ 8 , 9 ]. The earlier reviews lacked any theory of change that might explain why MFIs might be more effective than single ones — whereas now some systematic reviews do this, which enables them to conclude that sometimes single interventions can still be more effective.

As Nilsen et al. ([ 6 ] p. 7) note ‘Study findings concerning the effectiveness of various approaches are continuously synthesized and assembled in systematic reviews’. We may have gone as far as we can in understanding the implementation of evidence through systematic reviews of single and multi-faceted interventions and the next step would be to conduct more research exploring the complex and situated nature of evidence used in clinical practice and by particular professional groups. This would further build on the nuanced discussion and conclusion sections in a subset of the papers we reviewed. This might also support the field to move away from isolating individual implementation strategies [ 6 ] to explore the complex processes involving a range of actors with differing capacities [ 51 ] working in diverse organisational cultures. Taxonomies of implementation strategies do not fully account for the complex process of implementation, which involves a range of different actors with different capacities and skills across multiple system levels. There is plenty of work to build on, particularly in the social sciences, which currently sits at the margins of debates about evidence implementation (see for example, Normalisation Process Theory [ 52 ]).

There are several changes that we have identified in this overview of systematic reviews in comparison to the review we published in 2011 [ 8 ]. A consistent and welcome finding is that the overall quality of the systematic reviews themselves appears to have improved between the two reviews, although this is not reflected upon in the papers. This is exhibited through better, clearer reporting mechanisms in relation to the mechanics of the reviews, alongside a greater attention to, and deeper description of, how potential biases in included papers are discussed. Additionally, there is an increased, but still limited, inclusion of original studies conducted in low- and middle-income countries as opposed to just high-income countries. Importantly, we found that many of these systematic reviews are attuned to, and comment upon the contextual distinctions of pursuing evidence-informed interventions in health care settings in different economic settings. Furthermore, systematic reviews included in this updated article cover a wider set of clinical specialities (both within and beyond hospital settings) and have a focus on a wider set of healthcare professions — discussing both similarities, differences and inter-professional challenges faced therein, compared to the earlier reviews. These wider ranges of studies highlight that a particular intervention or group of interventions may work well for one professional group but be ineffective for another. This diversity of study settings allows us to consider the important role context (in its many forms) plays on implementing evidence into practice. Examining the complex and varied context of health care will help us address what Nilsen et al. ([ 6 ] p. 1) described as, ‘society’s health problems [that] require research-based knowledge acted on by healthcare practitioners together with implementation of political measures from governmental agencies’. This will help us shift implementation science to move, ‘beyond a success or failure perspective towards improved analysis of variables that could explain the impact of the implementation process’ ([ 6 ] p. 2).

This review brings together 32 papers considering individual and multi-faceted interventions designed to support the use of evidence in clinical practice. The majority of reviews report strategies achieving small impacts (normally on processes of care). There is much less evidence that these strategies have shifted patient outcomes. Combined with the two previous reviews, 86 systematic reviews of strategies to increase the implementation of research into clinical practice have been conducted. As a whole, this substantial body of knowledge struggles to tell us more about the use of individual and MFIs than: ‘it depends’. To really move forwards in addressing the gap between research evidence and practice, we may need to shift the emphasis away from isolating individual and multi-faceted interventions to better understanding and building more situated, relational and organisational capability to support the use of research in clinical practice. This will involve drawing on a wider range of perspectives, especially from the social, economic, political and behavioural sciences in primary studies and diversifying the types of synthesis undertaken to include approaches such as realist synthesis which facilitate exploration of the context in which strategies are employed. Harvey et al. [ 53 ] suggest that when context is likely to be critical to implementation success there are a range of primary research approaches (participatory research, realist evaluation, developmental evaluation, ethnography, quality/ rapid cycle improvement) that are likely to be appropriate and insightful. While these approaches often form part of implementation studies in the form of process evaluations, they are usually relatively small scale in relation to implementation research as a whole. As a result, the findings often do not make it into the subsequent systematic reviews. This review provides further evidence that we need to bring qualitative approaches in from the periphery to play a central role in many implementation studies and subsequent evidence syntheses. It would be helpful for systematic reviews, at the very least, to include more detail about the interventions and their implementation in terms of how and why they worked.

Availability of data and materials

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Abbreviations

Before and after study

Controlled clinical trial

Effective Practice and Organisation of Care

High-income countries

Information and Communications Technology

Interrupted time series

Knowledge translation

Low- and middle-income countries

Randomised controlled trial

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Acknowledgements

The authors would like to thank Professor Kathryn Oliver for her support in the planning the review, Professor Steve Hanney for reading and commenting on the final manuscript and the staff at LSHTM library for their support in planning and conducting the literature search.

This study was supported by LSHTM’s Research England QR strategic priorities funding allocation and the National Institute for Health and Care Research (NIHR) Applied Research Collaboration South London (NIHR ARC South London) at King’s College Hospital NHS Foundation Trust. Grant number NIHR200152. The views expressed are those of the author(s) and not necessarily those of the NIHR, the Department of Health and Social Care or Research England.

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Boaz, A., Baeza, J., Fraser, A. et al. ‘It depends’: what 86 systematic reviews tell us about what strategies to use to support the use of research in clinical practice. Implementation Sci 19 , 15 (2024). https://doi.org/10.1186/s13012-024-01337-z

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“Pictures provide a point around which other pieces of evidence collect.” — American Film Director Errol Morris  

Morris’s sentiment emphasizes the role of images—especially photographs and videos—to help galvanize evidentiary claims and to help create the foundations from which societies can construct notions of truth or fact. Although this role is socially constituted rather than being an inherent quality of the medium (Tagg, 1988), it has been instrumental in both emancipatory and oppressive projects since its inception. Yet, with the surge in visual misinformation, disinformation, and evolving artificial intelligence (AI), the epistemic role of still and moving images to convey truth has reached a crucible. This prompts a critical inquiry: What theoretical and interpretive frameworks, technologies, and practices can identify, categorize, and preserve images’ evidentiary value? To delve deeper, what precisely is worth preserving?   

Thus far, the literature on visual misinformation, disinformation, and AI-generated visuals has primarily explored the social impact of deceptive or augmented visuals. This includes individuals’ ability to detect deception (meleers et al., 2023; Köbis et al., 2021; Korshunov & Marcel, 2021; Shahid et al., 2022), public perceptions of deepfakes and their engagement (Ahmed, 2023; Ahmed et al., 2023; Ahmed & Chua, 2023), their impact on news credibility (Jin et al., 2023; Shin & Lee, 2022; Vaccari & Chadwick, 2020), detection methods (Sohrawardi et al., 2020), and the construction of deepfakes through meta-journalistic discourse (Gosse & Burkell, 2020; Yadlin-Segal & Oppenheim, 2021).

One component this literature lacks, however, is theoretical and interpretive frameworks that can redefine and reorient the epistemological role of image-based news in a networked society. To fill this gap, we invite contributors to critically evaluate the challenges, opportunities, and dynamics surrounding visual representations influenced by misinformation, disinformation, and/or evolving AI technology. The special issue welcomes empirical studies utilizing diverse approaches—qualitative, quantitative, computational, and mixed-methods—and theoretical contributions assessing the contemporary state of visual evidence.  It considers topics such as:

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Dr. Sang Jung Kim ([email protected]) is an assistant professor at the University of Iowa School of Journalism and Mass Communication specializing in the computational examination of visual content. Her research focuses on how multi-modality (visual, text, audio, video) exacerbates the devastating consequences of mis- and disinformation.   

Dr. Bingbing Zhang ([email protected]) is an assistant professor at the University of Iowa School of Journalism and Mass Communication specializing in studying the effects of visual content on audiences’ perceptions, attitudes, and behaviors. Her research also investigates the impact of algorithms and AI-related technology on these message effects.   

The special issue editors are affiliated with the  Visual Media Lab , which aims to examine the (1) attributes, (2) prevalence, and (3) impact of visuals in journalism and social media in the digital age. Additionally, they are organizers of the symposium ‘State of Visual Evidence,’ addressing the challenges and opportunities that synthetic media pose for the contemporary media environment, which is highly relevant to the VCQ special issue.   

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Ahmed, S. (2023). Examining public perception and cognitive biases in the presumed influence of deepfakes threat: empirical evidence of third person perception from three studies. Asian Journal of Communication , 33 (3), 308–331. https://doi.org/10.1080/01292986.2023.2194886

Ahmed, S., & Chua, H. W. (2023). Perception and deception: Exploring individual responses to deepfakes across different modalities. Heliyon , 9 (10), e20383. https://doi.org/10.1016/j.heliyon.2023.e20383

Ahmed, S., Ng, S. W. T., & Bee, A. W. T. (2023). Understanding the role of fear of missing out and deficient self-regulation in sharing of deepfakes on social media: Evidence from eight countries. Frontiers in Psychology , 14 (March), 1–7. https://doi.org/10.3389/fpsyg.2023.1127507

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The future of social media in marketing

  • Conceptual/Theoretical Paper
  • Open access
  • Published: 12 October 2019
  • Volume 48 , pages 79–95, ( 2020 )

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  • Gil Appel 1 ,
  • Lauren Grewal 2 ,
  • Rhonda Hadi 3 &
  • Andrew T. Stephen 3 , 4  

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Social media allows people to freely interact with others and offers multiple ways for marketers to reach and engage with consumers. Considering the numerous ways social media affects individuals and businesses alike, in this article, the authors focus on where they believe the future of social media lies when considering marketing-related topics and issues. Drawing on academic research, discussions with industry leaders, and popular discourse, the authors identify nine themes, organized by predicted imminence (i.e., the immediate, near, and far futures), that they believe will meaningfully shape the future of social media through three lenses: consumer, industry, and public policy. Within each theme, the authors describe the digital landscape, present and discuss their predictions, and identify relevant future research directions for academics and practitioners.

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Introduction

Social media is used by billions of people around the world and has fast become one of the defining technologies of our time. Facebook, for example, reported having 2.38 billion monthly active users and 1.56 billion daily active users as of March 31, 2019 (Facebook 2019 ). Globally, the total number of social media users is estimated to grow to 3.29 billion users in 2022, which will be 42.3% of the world’s population (eMarketer 2018 ). Given the massive potential audience available who are spending many hours a day using social media across the various platforms, it is not surprising that marketers have embraced social media as a marketing channel. Academically, social media has also been embraced, and an extensive body of research on social media marketing and related topics, such as online word of mouth (WOM) and online networks, has been developed. Despite what academics and practitioners have studied and learned over the last 15–20 years on this topic, due to the fast-paced and ever-changing nature of social media—and how consumers use it—the future of social media in marketing might not be merely a continuation of what we have already seen. Therefore, we ask a pertinent question, what is the future of social media in marketing?

Addressing this question is the goal of this article. It is important to consider the future of social media in the context of consumer behavior and marketing, since social media has become a vital marketing and communications channel for businesses, organizations and institutions alike, including those in the political sphere. Moreover, social media is culturally significant since it has become, for many, the primary domain in which they receive vast amounts of information, share content and aspects of their lives with others, and receive information about the world around them (even though that information might be of questionable accuracy). Vitally, social media is always changing. Social media as we know it today is different than even a year ago (let alone a decade ago), and social media a year from now will likely be different than now. This is due to constant innovation taking place on both the technology side (e.g., by the major platforms constantly adding new features and services) and the user/consumer side (e.g., people finding new uses for social media) of social media.

What is social media?

Definitionally, social media can be thought of in a few different ways. In a practical sense, it is a collection of software-based digital technologies—usually presented as apps and websites—that provide users with digital environments in which they can send and receive digital content or information over some type of online social network. In this sense, we can think of social media as the major platforms and their features, such as Facebook, Instagram, and Twitter. We can also in practical terms of social media as another type of digital marketing channel that marketers can use to communicate with consumers through advertising. But we can also think of social media more broadly, seeing it less as digital media and specific technology services, and more as digital places where people conduct significant parts of their lives. From this perspective, it means that social media becomes less about the specific technologies or platforms, and more about what people do in these environments. To date, this has tended to be largely about information sharing, and, in marketing, often thought of as a form of (online) word of mouth (WOM).

Building on these definitional perspectives, and thinking about the future, we consider social media to be a technology-centric—but not entirely technological—ecosystem in which a diverse and complex set of behaviors, interactions, and exchanges involving various kinds of interconnected actors (individuals and firms, organizations, and institutions) can occur. Social media is pervasive, widely used, and culturally relevant. This definitional perspective is deliberately broad because we believe that social media has essentially become almost anything—content, information, behaviors, people, organizations, institutions—that can exist in an interconnected, networked digital environment where interactivity is possible. It has evolved from being simply an online instantiation of WOM behaviors and content/information creation and sharing. It is pervasive across societies (and geographic borders) and culturally prominent at both local and global levels.

Throughout the paper we consider many of the definitional and phenomenological aspects described above and explore their implications for consumers and marketing in order to address our question about the future of marketing-related social media. By drawing on academic research, discussions with industry leaders, popular discourse, and our own expertise, we present and discuss a framework featuring nine themes that we believe will meaningfully shape the future of social media in marketing. These themes by no means represent a comprehensive list of all emerging trends in the social media domain and include aspects that are both familiar in extant social media marketing literature (e.g., online WOM, engagement, and user-generated content) and emergent (e.g., sensory considerations in human-computer interaction and new types of unstructured data, including text, audio, images, and video). The themes we present were chosen because they capture important changes in the social media space through the lenses of important stakeholders, including consumers, industry/practice, and public policy.

In addition to describing the nature and consequences of each theme, we identify research directions that academics and practitioners may wish to explore. While it is infeasible to forecast precisely what the future has in store or to project these on a specific timeline, we have organized the emergent themes into three time-progressive waves, according to imminence of impact (i.e., the immediate, near, and far future). Before presenting our framework for the future of social media in marketing and its implications for research (and practice and policy), we provide a brief overview of where social media currently stands as a major media and marketing channel.

Social media at present

The current social media landscape has two key aspects to it. First are the platforms—major and minor, established and emerging—that provide the underlying technologies and business models making up the industry and ecosystem. Second are the use cases; i.e., how various kinds of people and organizations are using these technologies and for what purposes.

The rise of social media, and the manner in which it has impacted both consumer behavior and marketing practice, has largely been driven by the platforms themselves. Some readers might recall the “early days” of social media where social networking sites such as MySpace and Friendster were popular. These sites were precursors to Facebook and everything else that has developed over the last decade. Alongside these platforms, we continue to have other forms of social media such as messaging (which started with basic Internet Relay Chat services in the 1990s and the SMS text messaging built into early digital mobile telephone standards in the 2000s), and asynchronous online conversations arranged around specific topics of interest (e.g., threaded discussion forums, subreddits on Reddit). More recently, we have seen the rise of social media platforms where images and videos replace text, such as Instagram and Snapchat.

Across platforms, historically and to the present day, the dominant business model has involved monetization of users (audiences) by offering advertising services to anyone wishing to reach those audiences with digital content and marketing communications. Prior research has examined the usefulness of social media (in its various forms) for marketing purposes. For example, work by Trusov et al. ( 2009 ) and Stephen and Galak ( 2012 ) demonstrated that certain kinds of social interactions that now happen on social media (e.g., “refer a friend” features and discussions in online communities) can positively affect important marketing outcomes such as new customer acquisition and sales. More recently, the value of advertising on social media continues to be explored (e.g., Gordon et al. 2019 ), as well as how it interacts with other forms of media such as television (e.g., Fossen and Schweidel 2016 , 2019 ) and affects new product adoption through diffusion of information mechanisms (e.g., Hennig-Thurau et al. 2015 ).

Although the rise (and fall) of various kinds of social media platforms has been important for understanding the social media landscape, our contention is that understanding the current situation of social media, at least from a marketing perspective, lies more in what the users do on these platforms than the technologies or services offered by these platforms. Presently, people around the world use social media in its various forms (e.g., news feeds on Facebook and Twitter, private messaging on WhatsApp and WeChat, and discussion forums on Reddit) for a number of purposes. These can generally be categorized as (1) digitally communicating and socializing with known others, such as family and friends, (2) doing the same but with unknown others but who share common interests, and (3) accessing and contributing to digital content such as news, gossip, and user-generated product reviews.

All of these use cases are essentially WOM in one form or another. This, at least, is how marketing scholars have mainly characterized social media, as discussed by Lamberton and Stephen ( 2016 ). Indeed, online WOM has been—and, we contend, will continue to be—important in marketing (e.g., in the meta-analysis by Babić Rosario et al. 2016 the authors found, on average, a positive correlation between online WOM and sales). The present perspective on social media is that people use it for creating, accessing, and spreading information via WOM to various types of others, be it known “strong ties” or “weak ties” in their networks or unknown “strangers.” Some extant research has looked at social media from the WOM perspective of the consequences of the transmission of WOM (e.g., creating a Facebook post or tweeting) on others (e.g., Herhausen et al. 2019 ; Stephen and Lehmann 2016 ), the impact of the type of WOM content shared on others’ behavior (e.g., Villarroel Ordenes et al. 2017 ; Villarroel Ordenes et al. 2018 ), and on the motivations that drive consumer posting on social media, including considerations of status and self-presentation (e.g., Grewal et al. 2019 ; Hennig-Thurau et al. 2004 ; Hollenbeck and Kaikati 2012 ; Toubia and Stephen 2013 ; Wallace et al. 2014 ).

While this current characterization of WOM appears reasonable, it considers social media only from a communications perspective (and as a type of media channel). However, as social media matures, broader social implications emerge. To appropriately consider the future, we must expand our perspective beyond the narrow communicative aspects of social media and consider instead how consumers might use it. Hence, in our vision for the future of social media in marketing in the following sections, we attempt to present a more expansive perspective of what social media is (and will become) and explain why this perspective is relevant to marketing research and practice.

Overview of framework for the future of social media in marketing

In the following sections we present a framework for the immediate, near, and far future of social media in marketing when considering various relevant stakeholders. Themes in the immediate future represent those which already exist in the current marketplace, and that we believe will continue shaping the social media landscape. The near future section examines trends that have shown early signs of manifesting, and that we believe will meaningfully alter the social media landscape in the imminent future. Finally, themes designated as being in the far future represent more speculative projections that we deem capable of long-term influence on the future of social media. The next sections delve into each of the themes in Table 1 , organized around the predicted imminence of these theme’s importance to marketing (i.e., the immediate, near, and far futures).

The immediate future

To begin our discussion on the direction of social media, in this section, we highlight three themes that have surfaced in the current environment that we believe will continue to shape the social media landscape in the immediate future. These themes—omni-social presence, the rise of influencers, and trust and privacy concerns—reflect the ever-changing digital and social media landscape that we presently face. We believe that these different areas will influence a number of stakeholders such as individual social media users, firms and brands that utilize social media, and public policymakers (e.g., governments, regulators).

Omni-social presence

In its early days, social media activity was mostly confined to designated social media platforms such as Facebook and Twitter (or their now-defunct precursors). However, a proliferation of websites and applications that primarily serve separate purposes have capitalized on the opportunity to embed social media functionality into their interfaces. Similarly, all major mobile and desktop operating systems have in-built social media integration (e.g., sharing functions built into Apple’s iOS). This has made social media pervasive and ubiquitous—and perhaps even omnipotent—and has extended the ecosystem beyond dedicated platforms.

Accordingly, consumers live in a world in which social media intersects with most aspects of their lives through digitally enabled social interactivity in such domains as travel (e.g., TripAdvisor), work (e.g., LinkedIn), food (e.g., Yelp), music (e.g., Spotify), and more. At the same time, traditional social media companies have augmented their platforms to provide a broader array of functionalities and services (e.g., Facebook’s marketplace, Chowdry 2018 ; WeChat’s payment system, Cheng 2017 ). These bidirectional trends suggest that the modern-day consumer is living in an increasingly “omni-social” world.

From a marketing perspective, the “omni-social” nature of the present environment suggests that virtually every part of a consumer’s decision-making process is prone to social media influence. Need recognition might be activated when a consumer watches their favorite beauty influencer trying a new product on YouTube. A consumer shopping for a car might search for information by asking their Facebook friends what models they recommend. A hungry employee might sift through Yelp reviews to evaluate different lunch options. A traveler might use Airbnb to book future accommodation. Finally, a highly dissatisfied (or delighted) airline passenger might rant (rave) about their experience on Twitter. While the decision-making funnel is arguably growing flatter than the aforementioned examples would imply (Cortizo-Burgess 2014 ), these independent scenarios illustrate that social media has the propensity to influence the entire consumer-decision making process, from beginning to end.

Finally, perhaps the greatest indication of an “omni-social” phenomenon is the manner in which social media appears to be shaping culture itself. YouTube influencers are now cultural icons, with their own TV shows (Comm 2016 ) and product lines (McClure 2015 ). Creative content in television and movies is often deliberately designed to be “gifable” and meme-friendly (Bereznak 2018 ). “Made-for-Instagram museums” are encouraging artistic content and experiences that are optimized for selfie-taking and posting (Pardes 2017 ). These examples suggest that social media’s influence is hardly restricted to the “online” world (we discuss the potential obsolescence of this term later in this paper), but is rather consistently shaping cultural artifacts (television, film, the arts) that transcend its traditional boundaries. We believe this trend will continue to manifest, perhaps making the term “social media” itself out-of-date, as it’s omni-presence will be the default assumption for consumers, businesses, and artists in various domains.

This omni-social trend generates many questions to probe in future research. For example, how will social interactivity influence consumer behavior in areas that had traditionally been non-social? From a practitioner lens, it might also be interesting to explore how marketers can strategically address the flatter decision-making funnel that social media has enabled, and to examine how service providers can best alter experiential consumption when anticipating social media sharing behavior.

The rise of new forms of social influence (and influencers)

The idea of using celebrities (in consumer markets) or well-known opinion leaders (in business markets), who have a high social value, to influence others is a well-known marketing strategy (Knoll and Matthes 2017 ). However, the omnipresence of social media has tremendously increased the accessibility and appeal of this approach. For example, Selena Gomez has over 144 million followers on Instagram that she engages with each of her posts. In 2018, the exposure of a single photo shared by her was valued at $3.4 million (Maxim 2018 ). However, she comes at a high price: one post that Selena sponsors for a brand can cost upwards of $800,000 (Mejia 2018 ). However, putting high valuations on mere online exposures or collecting “likes” for specific posts can be somewhat speculative, as academic research shows that acquiring “likes” on social media might have no effect on consumers’ attitudes or behaviors (John et al. 2017 ; Mochon et al. 2017 ). Moreover, Hennig-Thurau et al. ( 2015 ), show that while garnering positive WOM has little to no effect on consumer preferences, negative WOM can have a negative effect on consumer preferences.

While celebrities like Selena Gomez are possible influencers for major brands, these traditional celebrities are so expensive that smaller brands have begun, and will continue to, capitalize on the popularity and success of what are referred to as “micro-influencers,” representing a new form of influencers. Micro-influencers are influencers who are not as well-known as celebrities, but who have strong and enthusiastic followings that are usually more targeted, amounting anywhere between a few thousand to hundreds of thousands of followers (Main 2017 ). In general, these types of influencers are considered to be more trustworthy and authentic than traditional celebrities, which is a major reason influencer marketing has grown increasingly appealing to brands (Enberg 2018 ). These individuals are often seen as credible “experts” in what they post about, encouraging others to want to view the content they create and engage with them. Furthermore, using these influencers allows the brand via first person narration (compared to ads), which is considered warmer and more personal, and was shown to be more effective in engaging consumers (Chang et al. 2019 ).

Considering the possible reach and engagement influencers command on social media, companies have either begun embracing influencers on social media, or plan to expand their efforts in this domain even more. For example, in recent conversations we had with social media executives, several of them stated the growing importance of influencers and mentioned how brands generally are looking to incorporate influencer marketing into their marketing strategies. Further, recent conversations with executives at some globally leading brands suggest that influencer marketing spending by big brands continues to rise.

While influencer marketing on social media is not new, we believe it has a lot of potential to develop further as an industry. In a recent working paper, Duani et al. ( 2018 ) show that consumers enjoy watching a live experience much more and for longer time periods than watching a prerecorded one. Hence, we think live streaming by influencers will continue to grow, in broad domains as well as niche ones. For example, streaming of video game playing on Twitch, a platform owned by Amazon, may still be niche but shows no signs of slowing down. However, live platforms are limited by the fact that the influencers, being human, need to sleep and do other activities offline. Virtual influencers (i.e., “CGI” influencers that look human but are not), on the other hand, have no such limitations. They never get tired or sick, they do not even eat (unless it is needed for a campaign). Some brands have started exploring the use of virtual influencers (Nolan 2018 ), and we believe that in coming years, along with stronger computing power and artificial intelligence algorithms, virtual influencers will become much more prominent on social media, being able to invariably represent and act on brand values and engage with followers anytime.

There are many interesting future research avenues to consider when thinking about the role of influencers on social media. First, determining what traits and qualities (e.g., authenticity, trust, credibility, and likability) make sponsored posts by a traditional celebrity influencer, versus a micro-influencer, or even compared to a CGI influencer, more or less successful is important to determine for marketers. Understanding whether success has to do with the actual influencer’s characteristics, the type of content being posted, whether content is sponsored or not, and so on, are all relevant concerns for companies and social media platforms when determining partnerships and where to invest effort in influencers. In addition, research can focus on understanding the appeal of live influencer content, and how to successfully blend influencer content with more traditional marketing mix approaches.

Privacy concerns on social media

Consumer concerns regarding data privacy, and their ability to trust brands and platforms are not new (for a review on data privacy see Martin and Murphy 2017 ). Research in marketing and related disciplines has examined privacy and trust concerns from multiple angles and using different definitions of privacy. For example, research has focused on the connections between personalization and privacy (e.g., Aguirre et al. 2015 ; White et al. 2008 ), the relationship of privacy as it relates to consumer trust and firm performance (e.g., Martin 2018 ; Martin et al. 2017 ), and the legal and ethical aspects of data and digital privacy (e.g., Culnan and Williams 2009 ; Nill and Aalberts 2014 ). Despite this topic not seeming novel, the way consumers, brands, policy makers, and social media platforms are all adjusting and adapting to these concerns are still in flux and without clear resolution.

Making our understanding of privacy concerns even less straightforward is the fact that, across extant literature, a clear definition of privacy is hard to come by. In one commentary on privacy, Stewart ( 2017 ), defined privacy as “being left alone,” as this allows an individual to determine invasions of privacy. We build from this definition of privacy to speculate on a major issue in privacy and trust moving forward. Specifically, how consumers are adapting and responding to the digital world, where “being left alone” isn’t possible. For example, while research has shown benefits to personalization tactics (e.g., Chung et al. 2016 ), with eroding trust in social platforms and brands that advertise through them, many consumers would rather not share data and privacy for a more personalized experiences, are uncomfortable with their purchases being tracked and think it should be illegal for brands to be able to buy their data (Edelman 2018 ). These recent findings seem to be in conflict with previously established work on consumer privacy expectations. Therefore, understanding if previously studied factors that mitigated the negative effects of personalization (e.g., perceived utility; White et al. 2008 ) are still valued by consumers in an ever-changing digital landscape is essential for future work.

In line with rising privacy concerns, the way consumers view brands and social media is becoming increasingly negative. Consumers are deleting their social media presence, where research has shown that nearly 40% of digitally connected individuals admitted to deleting at least one social media account due to fears of their personal data being mishandled (Edelman 2018 ). This is a negative trend not only for social media platforms, but for the brands and advertisers who have grown dependent on these avenues for reaching consumers. Edelman found that nearly half of the surveyed consumers believed brands to be complicit in negative aspects of content on social media such as hate speech, inappropriate content, or fake news (Edelman 2018 ). Considering that social media has become one of the best places for brands to engage with consumers, build relationships, and provide customer service, it’s not only in the best interest of social media platforms to “do better” in terms of policing content, but the onus of responsibility has been placed on brands to advocate for privacy, trust, and the removal of fake or hateful content.

Therefore, to combat these negative consumer beliefs, changes will need to be made by everyone who benefits from consumer engagement on social media. Social media platforms and brands need to consider three major concerns that are eroding consumer trust: personal information, intellectual property and information security (Information Technology Faculty 2018 ). Considering each of these concerns, specific actions and initiatives need to be taken for greater transparency and subsequent trust. We believe that brands and agencies need to hold social media accountable for their actions regarding consumer data (e.g., GDPR in the European Union) for consumers to feel “safe” and “in control,” two factors shown necessary in cases of privacy concerns (e.g., Tucker 2014 ; Xu et al. 2012 ). As well, brands need to establish transparent policies regarding consumer data in a way that recognizes the laws, advertising restrictions, and a consumer’s right to privacy (a view shared by others; e.g., Martin et al. 2017 ). All of this is managerially essential for brands to engender feelings of trust in the increasingly murky domain of social media.

Future research can be conducted to determine consumer reactions to different types of changes and policies regarding data and privacy. As well, another related and important direction for future research, will be to ascertain the spillover effects of distrust on social media. Specifically, is all content shared on social media seen as less trustworthy if the platform itself is distrusted? Does this extend to brand messages displayed online? Is there a negative spillover effect to other user-generated content shared through these platforms?

The near future

In the previous section, we discussed three areas where we believe social media is immediately in flux. In this section, we identify three trends that have shown early signs of manifesting, and which we believe will meaningfully alter the social media landscape in the near, or not-too-distant, future. Each of these topics impact the stakeholders we mentioned when discussing the immediate social media landscape.

Combatting loneliness and isolation

Social media has made it easier to reach people. When Facebook was founded in 2004, their mission was “to give people the power to build community and bring the world closer together... use Facebook to stay connected with friends and family, to discover what’s going on in the world, and to share and express what matters to them” (Facebook 2019 ). Despite this mission, and the reality that users are more “connected” to other people than ever before, loneliness and isolation are on the rise. Over the last fifty years in the U.S., loneliness and isolation rates have doubled, with Generation Z considered to be the loneliest generation (Cigna 2018 ). Considering these findings with the rise of social media, is the fear that Facebook is interfering with real friendships and ironically spreading the isolation it was designed to conquer something to be considered about (Marche 2012 )?

The role of social media in this “loneliness epidemic” is being hotly debated. Some research has shown that social media negatively impacts consumer well-being. Specifically, heavy social media use has been associated with higher perceived social isolation, loneliness, and depression (Kross et al. 2013 ; Primack et al. 2017 ; Steers et al. 2014 ). Additionally, Facebook use has been shown to be negatively correlated with consumer well-being (Shakya and Christakis 2017 ) and correlational research has shown that limiting social media use to 10 min can decrease feelings of loneliness and depression due to less FOMO (e.g., “fear of missing out;” Hunt et al. 2018 ).

On the other hand, research has shown that social media use alone is not a predictor of loneliness as other factors have to be considered (Cigna 2018 ; Kim et al. 2009 ). In fact, while some research has shown no effect of social media on well-being (Orben et al. 2019 ), other research has shown that social media can benefit individuals through a number of different avenues such as teaching and developing socialization skills, allowing greater communication and access to a greater wealth of resources, and helping with connection and belonging (American Psychological Association 2011 ; Baker and Algorta 2016 ; Marker et al. 2018 ). As well, a working paper by Crolic et al. ( 2019 ) argues that much of the evidence of social media use on consumer well-being is of questionable quality (e.g., small and non-representative samples, reliance on self-reported social media use), and show that some types of social media use are positively associated with psychological well-being over time.

Managerially speaking, companies are beginning to respond as a repercussion of studies highlighting a negative relationship between social media and negative wellbeing. For example, Facebook has created “time limit” tools (mobile operating systems, such as iOS, now also have these time-limiting features). Specifically, users can now check their daily times, set up reminder alerts that pop up when a self-imposed amount of time on the apps is hit, and there is the option to mute notifications for a set period of time (Priday 2018 ). These different features seem well-intentioned and are designed to try and give people a more positive social media experience. Whether these features will be used is unknown.

Future research can address whether or not consumers will use available “timing” tools on one of many devices in which their social media exists (i.e., fake self-policing) or on all of their devices to actually curb behavior. It could also be the case that users will actually spend less time on Facebook and Instagram, but possibly spend that extra time on other competing social media platforms, or attached to devices, which theoretically will not help combat loneliness. Understanding how (and which) consumers use these self-control tools and how impactful they are is a potentially valuable avenue for future research.

One aspect of social media that has yet to be considered in the loneliness discussion through empirical measures, is the quality of use (versus quantity). Facebook ads have begun saying, “The best part of Facebook isn’t on Facebook. It’s when it helps us get together” (Facebook 2019 ). There have been discussions around the authenticity of this type of message, but at its core, in addition to promoting quantity differences, it’s speaking to how consumers use the platform. Possibly, to facilitate this message, social media platforms will find new ways to create friend suggestions between individuals who not only share similar interests and mutual friends to facilitate in-person friendships (e.g., locational data from the mobile app service). Currently there are apps that allow people to search for friends that are physically close (e.g., Bumble Friends), and perhaps social media will go in this same direction to address the loneliness epidemic and stay current.

Future research can examine whether the quantity of use, types of social media platforms, or the way social media is used causally impacts perceived loneliness. Specifically, understanding if the negative correlations found between social media use and well-being are due to the demographics of individuals who use a lot of social media, the way social media works, or the way users choose to engage with the platform will be important for understanding social media’s role (or lack of role) in the loneliness epidemic.

Integrated customer care

Customer care via digital channels as we know it is going to change substantially in the near future. To date, many brands have used social media platforms as a place for providing customer care, addressing customers’ specific questions, and fixing problems. In the future, social media-based customer care is expected to become even more customized, personalized, and ubiquitous. Customers will be able to engage with firms anywhere and anytime, and solutions to customers’ problems will be more accessible and immediate, perhaps even pre-emptive using predictive approaches (i.e., before a customer even notices an issue or has a question pop into their mind).

Even today, we observe the benefits that companies gain from connecting with customers on social media for service- or care-related purposes. Customer care is implemented in dedicated smartphone apps and via direct messaging on social media platforms. However, it appears that firms want to make it even easier for customers to connect with them whenever and wherever they might need. Requiring a customer to download a brand specific app or to search through various social media platforms to connect with firms through the right branded account on a platform can be a cumbersome process. In those cases, customers might instead churn or engage in negative WOM, instead of connecting with the firm to bring up any troubles they might have.

The near future of customer care on social media appears to be more efficient and far-reaching. In a recent review on the future of customer relationship management, Haenlein ( 2017 ) describes “invisible CRM” as future systems that will make customer engagement simple and accessible for customers. New platforms have emerged to make the connection between customer and firm effortless. Much of this is via instant messaging applications for businesses, which several leading technology companies have recently launched as business-related features in existing platforms (e.g., contact business features in Facebook Messenger and WhatsApp or Apple’s Business Chat).

These technologies allow businesses to directly communicate via social media messaging services with their customers. Amazon, Apple, Facebook, and Google are in the process, or have already released early versions of such platforms (Dequier 2018 ). Customers can message a company, ask them questions, or even order products and services through the messaging system, which is often built around chatbots and virtual assistants. This practice is expected to become more widespread, especially because it puts brands and companies into the social media messaging platforms their customers already use to communicate with others, it provides quicker—even instantaneous—responses, is economically scalable through the use of AI-driven chatbots, and, despite the use of chatbots, can provide a more personalized level of customer service.

Another area that companies will greatly improve upon is data collection and analysis. While it is true that data collection on social media is already pervasive today, it is also heavily scrutinized. However, we believe that companies will adapt to the latest regulation changes (e.g., GDPR in Europe, CCPA in California) and improve on collecting and analyzing anonymized data (Kakatkar and Spann 2018 ). Furthermore, even under these new regulations, personalized data collection is still allowed, but severely limits firm’s abilities to exploit consumers’ data, and requires their consent for data collection.

We believe that in the future, companies will be able recognize early indications of problems within customer chatter, behavior, or even physiological data (e.g., monitoring the sensors in our smart watches) before customers themselves even realize they are experiencing a problem. For example, WeWork, the shared workspace company, collects data on how workers move and act in a workspace, building highly personalized workspaces based on trends in the data. Taking this type of approach to customer care will enable “seamless service,” where companies would be able to identify and address consumer problems when they are still small and scattered, and while only a small number of customers are experiencing problems. Customer healthcare is a pioneer in this area, where using twitter and review sites were shown to predict poor healthcare quality (Greaves et al. 2013 ), listen to patients to analyze trending terms (Baktha et al. 2017 ; Padrez et al. 2016 ), or even predict disease outbreaks (Schmidt 2012 ).

Companies, wanting to better understand and mimic human interactions, will invest a lot of R&D efforts into developing better Natural Language Processing, voice and image recognition, emotional analysis, and speech synthesis tools (Sheth 2017 ). For example, Duplex, Google’s latest AI assistant, can already call services on its own and seamlessly book reservations for their users (Welch 2018 ). In the future, AI systems will act as human ability augmenters, allowing us to accomplish more, in less time, and better results (Guszcza 2018 ).

For marketers, this will reduce the need for call centers and agents, reducing points of friction in service and increasing the convenience for customers (Kaplan and Haenlein 2019 ). However, some raise the question that the increased dependence on automation may result in a loss of compassion and empathy. In a recent study, Force (2018) shows that interacting with brands on social media lowered people’s empathy. In response to such concerns, and to educate and incentivize people to interact with machines in a similar way they do with people, Google programmed their AI assistant to respond in a nicer way if you use a polite, rather than a commanding approach (Kumparak 2018 ). While this might help, more research is needed to understand the effect of an AI rich world on human behavior. As well, future research can examine how consumer generated data can help companies preemptively predict consumer distress. Another interesting path for research would be to better understand the difference in consumer engagement between the various platforms, and the long-term effects of service communications with non-human AI and IoT.

Social media as a political tool

Social media is a platform to share thoughts and opinions. This is especially true in the case of disseminating political sentiments. Famously, President Barack Obama’s victory in the 2008 election was partially attributed to his ability to drive and engage voters on social media (Carr 2008 ). Indeed, Bond et al. ( 2012 ) have shown that with simple interventions, social media platforms can increase targeted audiences’ likelihood of voting. Social media is considered one of the major drivers of the 2010 wave of revolutions in Arab countries, also known as the Arab Spring (Brown et al. 2012 ).

While social media is not new to politics, we believe that social media is transitioning to take a much larger role as a political tool in the intermediate future. First evidence for this could be seen in the 2016 U.S. presidential election, as social media took on a different shape, with many purported attempts to influence voter’s opinions, thoughts, and actions. This is especially true for then-candidate and now-President Donald Trump. His use of Twitter attracted a lot of attention during the campaign and has continued to do so during his term in office. Yet, he is not alone, and many politicians changed the way they work and interact with constituents, with a recent example of Congresswoman Alexandria Ocasio-Cortez that even ran a workshop for fellow congress members on social media (Dwyer 2019 ).

While such platforms allow for a rapid dissemination of ideas and concepts (Bonilla and Rosa 2015 ; Bode 2016 ), there are some, both in academia and industry that have raised ethical concerns about using social media for political purposes. Given that people choose who to follow, this selective behavior is said to potentially create echo chambers, wherein, users are exposed only to ideas by like-minded people, exhibiting increased political homophily (Bakshy et al. 2015 ). People’s preference to group with like-minded people is not new. Social in-groups have been shown to promote social identification and promote in-group members to conform to similar ideas (Castano et al. 2002 ; Harton and Bourgeois 2004 ). Furthermore, it was also shown that group members strongly disassociate and distance themselves from outgroup members (Berger and Heath 2008 ; White and Dahl 2007 ). Thus, it is not surprising to find that customized newsfeeds within social media exacerbate this problem by generating news coverage that is unique to specific users, locking them in their purported echo chambers (Oremus 2016 ).

While social media platforms admit that echo chambers could pose a problem, a solution is not clear (Fiegerman 2018 ). One reason that echo chambers present such a problem, is their proneness to fake news. Fake news are fabricated stories that try to disguise themselves as authentic content, in order to affect other social media users. Fake news was widely used in the 2016 U.S. elections, with accusations that foreign governments, such as Iran and Russia, were using bots (i.e., online automatic algorithms), to spread falsified content attacking Hillary Clinton and supporting President Trump (Kelly et al. 2018 ). Recent research has furthermore shown how the Chinese government strategically uses millions of online comments to distract the Chinese public from discussing sensitive issues and promote nationalism (King et al. 2017 ). In their latest incarnation, fake news uses an advanced AI technique called “Deep Fake” to generate ultra-realistic forged images and videos of political leaders while manipulating what those leaders say (Schwartz 2018 ). Such methods can easily fool even the sharpest viewer. In response, research has begun to explore ways that social media platforms can combat fake news through algorithms that determine the quality of shared content (e.g., Pennycook and Rand 2019 ).

One factor that has helped the rise of fake news is echo chambers. This occurs as the repeated sharing of fake news by group members enhance familiarity and support (Schwarz and Newman 2017 ). Repetition of such articles by bots can only increase that effect. Recent research has shown that in a perceived social setting, such as social media, participants were less likely to fact-check information (Jun et al. 2017 ), and avoided information that didn’t fit well with their intuition (Woolley and Risen 2018 ). Schwarz and Newman ( 2017 ) state that misinformation might be difficult to correct, especially if the correction is not issued immediately and the fake news has already settled into the minds of users. It was also shown that even a single exposure to fake news can create long term effect on users, making their effect larger than previously thought (Pennycook et al. 2019 ).

Notably, some research has found that exposure to opposing views (i.e., removing online echo chambers) may in fact increase (versus decrease) polarization (Bail et al. 2018 ). Accordingly, more work from policy makers, businesses, and academics is needed to understand and potentially combat political extremism. For example, policy makers and social media platforms will continually be challenged to fight “fake news” without censoring free speech. Accordingly, research that weighs the risk of limited freedom of expression versus the harms of spreading fake news would yield both theoretical and practically meaningful insights.

The far future

In this section, we highlight three emerging trends we believe will have a have long-term influence on the future of social media. Note that although we label these trends as being in the “far” future, many of the issues described here are already present or emerging. However, they represent more complex issues that we believe will take longer to address and be of mainstream importance for marketing than the six issues discussed previously under the immediate and near futures.

Increased sensory richness

In its early days, the majority of social media posts (e.g., on Facebook, Twitter) were text. Soon, these platforms allowed for the posting of pictures and then videos, and separate platforms dedicated themselves to focus on these specific forms of media (e.g., Instagram and Pinterest for pictures, Instagram and SnapChat for short videos). These shifts have had demonstrable consequences on social media usage and its consequences as some scholars suggest that image-based posts convey greater social presence than text alone (e.g., Pittman and Reich 2016 ). Importantly however, a plethora of new technologies in the market suggest that the future of social media will be more sensory-rich.

One notable technology that has already started infiltrating social media is augmented reality (AR). Perhaps the most recognizable examples of this are Snapchat’s filters, which use a device’s camera to superimpose real-time visual and/or video overlays on people’s faces (including features such as makeup, dog ears, etc.). The company has even launched filters to specifically be used on users’ cats (Ritschel 2018 ). Other social media players quickly joined the AR bandwagon, including Instagram’s recent adoption of AR filters (Rao 2017 ) and Apple’s Memoji messaging (Tillman 2018 ). This likely represents only the tip of the iceberg, particularly given that Facebook, one of the industry’s largest investors in AR technology, has confirmed it is working on AR glasses (Constine 2018 ). Notably, the company plans to launch a developer platform, so that people can build augmented-reality features that live inside Facebook, Instagram, Messenger and Whatsapp (Wagner 2017 ). These developments are supported by academic research suggesting that AR often provides more authentic (and hence positive) situated experiences (Hilken et al. 2017 ). Accordingly, whether viewed through glasses or through traditional mobile and tablet devices, the future of social media is likely to look much more visually augmented.

While AR allows users to interact within their current environments, virtual reality (VR) immerses the user in other places, and this technology is also likely to increasingly permeate social media interactions. While the Facebook-owned company Oculus VR has mostly been focusing on the areas of immersive gaming and film, the company recently announced the launch of Oculus Rooms where users can spend time with other users in a virtual world (playing games together, watching media together, or just chatting; Wagner 2018 ). Concurrently, Facebook Spaces allows friends to meet online in virtual reality and similarly engage with one another, with the added ability to share content (e.g., photos) from their Facebook profiles (Whigham 2018 ). In both cases, avatars are customized to represent users within the VR-created space. As VR technology is becoming more affordable and mainstream (Colville 2018 ) we believe social media will inevitably play a role in the technology’s increasing usage.

While AR and VR technologies bring visual richness, other developments suggest that the future of social media might also be more audible. A new player to the social media space, HearMeOut, recently introduced a platform that enables users to share and listen to 42-s audio posts (Perry 2018 ). Allowing users to use social media in a hands-free and eyes-free manner not only allows them to safely interact with social media when multitasking (particularly when driving), but voice is also said to add a certain richness and authenticity that is often missing from mere text-based posts (Katai 2018 ). Given that podcasts are more popular than ever before (Bhaskar 2018 ) and voice-based search queries are the fastest-growing mobile search type (Robbio 2018 ), it seems likely that this communication modality will accordingly show up more on social media use going forward.

Finally, there are early indications that social media might literally feel different in the future. As mobile phones are held in one’s hands and wearable technology is strapped onto one’s skin, companies and brands are exploring opportunities to communicate to users through touch. Indeed, haptic feedback (technology that recreates the sense of touch by applying forces, vibrations, or motions to the user; Brave et al. 2001 ) is increasingly being integrated into interfaces and applications, with purposes that go beyond mere call or message notifications. For example, some companies are experimenting with integrating haptics into media content (e.g., in mobile ads for Stoli vodka, users feel their phone shake as a woman shakes a cocktail; Johnson 2015 ), mobile games, and interpersonal chat (e.g., an app called Mumble! translates text messages into haptic outputs; Ozcivelek 2015 ). Given the high levels of investment into haptic technology (it is predicted to be a $20 billion industry by 2022; Magnarelli 2018 ) and the communicative benefits that stem from haptic engagement (Haans and IJsselsteijn 2006 ), we believe it is only a matter of time before this modality is integrated into social media platforms.

Future research might explore how any of the new sensory formats mentioned above might alter the nature of content creation and consumption. Substantively-focused researchers might also investigate how practitioners can use these tools to enhance their offerings and augment their interactions with customers. It is also interesting to consider how such sensory-rich formats can be used to bridge the gap between the online and offline spaces, which is the next theme we explore.

Online/offline integration and complete convergence

A discussion occurring across industry and academia is on how marketers can appropriately integrate online and offline efforts (i.e., an omnichannel approach). Reports from industry sources have shown that consumers respond better to integrated marketing campaigns (e.g., a 73% boost over standard email campaigns; Safko 2010 ). In academia meanwhile, the majority of research considering online promotions and advertisements has typically focused on how consumers respond to these strategies through online only measures (e.g., Manchanda et al. 2006 ), though this has begun to change in recent years with more research examining offline consequences to omnichannel strategies (Lobschat et al. 2017 ; Kumar et al. 2017 ).

Considering the interest in integrated marketing strategies over the last few years, numerous strategies have been utilized to follow online and offline promotions and their impacts on behavior such as the usage of hashtags to bring conversations online, call-to-actions, utilizing matching strategies on “traditional” avenues like television with social media. While there is currently online/offline integration strategies in marketing, we believe the future will go even further in blurring the lines between what is offline and online to not just increase the effectiveness of marketing promotions, but to completely change the way customers and companies interact with one another, and the way social media influences consumer behavior not only online, but offline.

For brands, there are a number of possible trends in omnichannel marketing that are pertinent. As mentioned earlier, a notable technology that has begun infiltrating social media is augmented reality (AR). In addition to what already exists (e.g., Snapchat’s filters, Pokémon Go), the future holds even more possibilities. For example, Ikea has been working to create an AR app that allows users to take photos of a space at home to exactly , down to the millimeter size and lighting in the room, showcase what a piece of furniture would look like in a consumer’s home (Lovejoy 2017 ). Another set of examples of AR comes from beauty company L’Oréal. In 2014 for the flagship L’Oréal Paris brand they released a mobile app called Makeup Genius that allowed consumers to virtually try on makeup on their phones (Stephen and Brooks 2018 ). Since then, they have developed AR apps for hair color and nail polish, as well as integrating AR into mobile ecommerce webpages for their luxury beauty brand Lancôme. AR-based digital services such as these are likely to be at the heart of the next stage of offline/online integration.

AR, and similar technology, will likely move above and beyond being a tool to help consumers make better decisions about their purchases. Conceivably, similar to promotions that currently exist to excitse consumers and create communities, AR will be incorporated into promotions that integrate offline and online actions. For example, contests on social media will advance to the stage where users get to vote on the best use of AR technology in conjunction with a brand’s products (e.g., instead of users submitting pictures of their apartments to show why they should win free furniture, they could use AR to show how they would lay out the furniture if they were to win it from IKEA).

Another way that the future of online/offline integration on social media needs to be discussed is in the sense of a digital self. Drawing on the extended self in the digital age (Belk 2013 ), the way consumers consider online actions as relevant to their offline selves may be changing. For example, Belk ( 2013 ) spoke of how consumers may be re-embodied through avatars they create to represent themselves online, influencing their offline selves and creating a multiplicity of selves (i.e., consumers have more choice when it comes to their self-representation). As research has shown how digital and social media can be used for self-presentation, affiliation, and expression (Back et al. 2010 ; Gosling et al. 2007 ; Toubia and Stephen 2013 ; Wilcox and Stephen 2012 ), what does it mean for the future if consumers can create who they want to be?

In addition, when considering digital selves, what does this mean for how consumers engage with brands and products? Currently, social media practice is one where brands encourage consumer engagement online (Chae et al. 2017 ; Godes and Mayzlin 2009 ), yet the implications for how these types of actions on the part of the brand to integrate online social media actions and real-life behavior play out are unclear. Research has begun to delve into the individual-level consequences of a consumer’s social media actions on marketing relevant outcomes (Grewal et al. 2019 ; John et al. 2017 ; Mochon et al. 2017 ; Zhang et al. 2017 ), however much is still unknown. As well, while there is recent work examining how the device used to create and view content online impacts consumer perceptions and behaviors (e.g., Grewal and Stephen 2019 ), to date research has not examined these questions in the context of social media. Therefore, future research could address how digital selves (both those held offline and those that only exist online), social media actions, and if the way consumers reach and use various platforms (i.e., device type, app vs. webpage, etc.) impact consumer behavior, interpersonal relationships, and brand-related measures (e.g., well-being, loyalty, purchase behaviors).

Social media by non-humans

The buzz surrounding AI has not escaped social media. Indeed, social bots (computer algorithms that automatically produce content and interact with social media users; Ferrara et al. 2016 ) have inhabited social media platforms for the last decade (Lee et al. 2011 ), and have become increasingly pervasive. For example, experts estimate that up to 15% of active Twitter accounts are bots (Varol et al. 2017 ), and that percentage appears to be on the rise (Romano 2018 ). While academics and practitioners are highly concerned with bot detection (Knight 2018 ), in the vast majority of current cases, users do not appear to recognize when they are interacting with bots (as opposed to other human users) on social media (Stocking and Sumida 2018 ). While some of these bots are said to be benign, and even useful (e.g., acting as information aggregators), they have also been shown to disrupt political discourse (as mentioned earlier), steal personal information, and spread misinformation (Ferrara et al. 2016 ).

Of course, social bots are not only a problem for social media users but are also a nagging concern plaguing marketers. Given that companies often assess marketing success on social media through metrics like Likes, Shares, and Clicks, the existence of bots poses a growing threat to accurate marketing metrics and methods for ROI estimation, such as attribution modelling (Bilton 2014 ). Similarly, when these bots act as “fake followers,” it can inflate the worth of influencers’ audiences (Bogost 2018 ). This can also be used nefariously by individuals and firms, as shown in a New York Times Magazine expose that documented the market used by some influencers to purchase such “fake” followers to inflate their social media reach (Confessore et al. 2018 ). As discussed above in relation to influencer marketing, where it has been commonplace for influencers to be paid for posts at rates proportionate to their follower counts, there have been perverse incentives to game the system by having non-human “fake” bot followers. This, however, erodes consumer trust in the social media ecosystem, which is a growing issue and a near-term problem for many firms using social media channels for marketing purposes.

However, there are instances when consumers do know they are interacting with bots, and do not seem to mind. For example, a number of virtual influencers (created with CGI, as mentioned earlier) seem to be garnering sizeable audiences, despite the fact they are clearly non-human (Walker 2018 ). One of the most popular of these virtual influencers, Lil Miquela, has over 1.5 million followers on Instagram despite openly confessing, “I am not a human being... I’m a robot” (Yurieff 2018 ). Future research might try to understand the underlying appeal of these virtual influencers, and the potential boundary conditions of their success.

Another category of social bots gaining increasing attention are therapy bots. These applications (e.g., “Woebot;” Molteni 2017 ) aim to support the mental health of users by proactively checking in on them, “listening” and chatting to users at any time and recommending activities to improve users’ wellbeing (de Jesus 2018 ). Similar bots are being used to “coach” users, and help them quit maladaptive behaviors, like smoking (e.g., QuitGenius; Crook 2018 ). Interestingly, by being explicitly non-human, these agents are perceived to be less judgmental, and might accordingly be easier for users to confide in.

Finally, the Internet of Things revolution has ushered in with it the opportunity for a number of tangible products and interfaces to “communicate” via social media. For example, in what started as a design experiment, “Brad,” a connected toaster, was given the ability to “communicate” with other connected toasters, and to tweet his “feelings” when neglected or under-used (Vanhemert 2014 ). While this experiment was deliberately designed to raise questions about the future of consumer-product relationships (and product-product “relationships”), the proliferation of autonomous tangible devices does suggest a future in which they have a “voice,” even in the absence of humans (Hoffman and Novak 2018 ).

Going forward, we believe the presence of bots on social media will be more normalized, but also more regulated (e.g., a recent law passed in California prevents bots from masquerading as humans; Smith 2018 ). Further, consumers and companies alike will be become increasingly interested in how bots communicate and interact with each other outside of human involvement. This brings up interesting potential research questions for academics and practitioners alike. How will the presence of non-humans change the nature of content creation and conversation in social media? And how should companies best account for the presence of non-humans in their attribution models?

Future research directions and conclusion

This article has presented nine themes pertinent to the future of social media as it relates to (and is perhaps influenced by) marketing. The themes have implications for individuals/consumers, businesses and organizations, and also public policymakers and governments. These themes, which represent our own thinking and a synthesis of views from extant research, industry experts, and popular public discourse, are of course not the full story of what the future of social media will entail. They are, however, a set of important issues that we believe will be worth considering in both academic research and marketing practice.

To stimulate future research on these themes and related topics, we present a summary of suggested research directions in Table 2 . These are organized around our nine themes and capture many of the suggested research directions mentioned earlier. As a sub-field within the field of marketing, social media is already substantial and the potential for future research—based on identified needs for new knowledge and answers to perplexing questions—suggests that this sub-field will become even more important over time. We encourage researchers to consider the kinds of research directions in Table 2 as examples of issues they could explore further. We also encourage researchers in marketing to treat social media as a place where interesting (and often very new) consumer behaviors exist and can be studied. As we discussed earlier in the paper, social media as a set of platform businesses and technologies is interesting, but it is how people use social media and the associated technologies that is ultimately of interest to marketing academics and practitioners. Thus, we urge scholars to not be overly enticed by the technological “shiny new toys” at the expense of considering the behaviors associated with those technologies and platforms.

Finally, while we relied heavily (though not exclusively) on North American examples to illustrate the emergent themes, there are likely interesting insights to be drawn by explicitly exploring cross-cultural differences in social media usage. For example, variations in regulatory policies (e.g., GDPR in the European Union) may lead to meaningful differences in how trust and privacy concerns manifest. Further, social media as a political tool might be more influential in regions where the mainstream media is notoriously government controlled and censored (e.g., as was the case in many of the Arab Spring countries). While such cross-cultural variation is outside the scope of this particular paper, we believe it represents an area of future research with great theoretical and practical value.

In reviewing the social media ecosystem and considering where it is heading in the context of consumers and marketing practice, we have concluded that this is an area that is very much still in a state of flux. The future of social media in marketing is exciting, but also uncertain. If nothing else, it is vitally important that we better understand social media since it has become highly culturally relevant, a dominant form of communication and expression, a major media type used by companies for advertising and other forms of communication, and even has geopolitical ramifications. We hope that the ideas discussed here stimulate many new ideas and research, which we ultimately hope to see being mentioned and shared across every type of social media platform.

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The authors thank the special issue editors and reviewers for their comments, and the Oxford Future of Marketing Initiative for supporting this research. The authors contributed equally and are listed in alphabetical order or, if preferred, order of Marvel superhero fandom from highest to lowest and order of Bon Jovi fandom from lowest to highest.

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Appel, G., Grewal, L., Hadi, R. et al. The future of social media in marketing. J. of the Acad. Mark. Sci. 48 , 79–95 (2020). https://doi.org/10.1007/s11747-019-00695-1

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Research Papers

Social media impact on the ‘cosmos’ blockchain ecosystem: state and prospect.

  • Ivan Pavlyshyn
  • Anna Petrenko
  • Bohdan Opryshko
  • Bohdan Oliinyk
  • Sergii Kavun

The proliferation of blockchain technology heralds transformative impacts across various sectors, offering decentralization, transparency, and enhanced security. This paper explores the unique case of Cosmos, a scalable blockchain ecosystem designed to address the challenges of isolation and interoperability among existing blockchains. With its implementation of Tendermint consensus and the Inter-Blockchain Communication protocol, Cosmos stands out in facilitating seamless cross-blockchain interactions. The ATOM token serves a dual role as the network’s currency and a governance tool, empowering stakeholders in decision-making processes.

Significantly, this study investigates the intricate relationship between Cosmos and social media platforms, examining how online sentiment influences voting on governance proposals, with a detailed analysis of two specific proposals. Furthermore, the paper delves into Cosmos’ integral role in the burgeoning Decentralized Finance sector, underscoring how its modular architecture fosters financial innovation.

In the broader context, there are numerous PoS (Proof of Stake) networks. Cosmos, one of the foundational and longstanding projects, exemplifies a classic blockchain economic model, making it an ideal subject for this analysis. Finally, the paper assesses Cosmos’ contribution to the overarching Web3 vision, asserting its significance as a foundational element for a decentralized, user-oriented digital framework. Our findings illuminate Cosmos’ multifaceted impact, from technological innovation to reshaping societal structures, reaffirming blockchain’s potential in redefining modern paradigms.

JEL Classification Codes: M31; M15; C58; L17

MSC 2010 Subject Classification Codes: 00A06; 37M10; 62M10; 91B84; 91B82

  • blockchain economics
  • economic analysis
  • business processes
  • financial analysis
  • market analysis

1. Introduction

The art of blockchain has garnered considerable notice and uptake across diverse domains owing to its capacity to transform age-old paradigms of data logging, dealings, and data administration. Fundamentally, it stands as a dispersed and decentralized digital chronicle that dutifully notes transactions across numerous machines in a manner that radiates transparency, resists tampering, and invites scrutiny.

Cosmos is a scalable and cross-network blockchain ecosystem designed to simplify development, deployment, and cross-blockchain interoperability ( Cosmos Whitepaper 2023 ). Created to address the isolation and incompatibility challenges among blockchains, it offers unique tools for secure and managed blockchain interaction ( Cosmos Network 2023 ). The core technology is the Tendermint consensus, an innovative Proof of Stake (PoS) algorithm that enhances security and scalability ( Haber & Stornetta 1991 ). Cosmos also incorporates the Inter-Blockchain Communication (IBC) protocol, enabling various blockchains to exchange messages and values ( Grinberg 2011 ).

The ATOM token serves as the central currency, used for consensus within the Cosmos Hub blockchain. ATOM holders can stake their tokens to maintain network security and also use them for transaction fees ( Cosmos Network 2023 ). Importantly, ATOM holders can participate in network governance, thereby influencing the ecosystem’s future ( Hayes 2019 ).

Cosmos engagement with social media significantly impacts its growth and influence. Social platforms disseminate information, attract new users, and facilitate community participation in decision making ( Crane & Sornette, 2008 ). Discussions on social media contribute to shaping the ecosystem’s future and direct attention to new opportunities ( Bamert et al. 2013 ). Overall, Cosmos is a continually evolving ecosystem that leverages active participant interaction across various platforms, including social media, to grow and innovate ( Antonopoulos & Wood 2018 ).

The concept of decentralized finance (DeFi) also finds a compatible environment within the Cosmos ecosystem. The modular architecture and customizable modules for smart contracts, liquidity protocols, and decentralized exchanges make it easier for developers to create DeFi applications ( Cosmos Network 2023 ). This serves as a catalyst for financial innovation, as it enables a variety of use-cases that are not only confined to payments or transactions but also extend to lending, borrowing, asset management, and more. Through Cosmos, DeFi projects can tap into a robust, secure, and interoperable network that helps them scale and connect with other blockchains.

Looking ahead, Cosmos aims to become a cornerstone in a decentralized internet architecture, often referred to as Web3. This revolutionary concept proposes a new internet layer that is free from centralized control and enhances user sovereignty ( Guan et al. 2022 ). With features like data portability, user-owned databases, and peer-to-peer interactions, Web3 aspires to distribute power back to individual users ( Nakamoto 2008 ). Cosmos, with its technology stack and philosophy, aligns well with this vision, acting as an essential building block for a more democratic, transparent, and efficient digital world. Overall, the impact of Cosmos extends beyond mere technological innovation; it contributes to the larger narrative of how blockchain can reshape societal structures and empower individuals ( Maurer et al. 2013 ).

2. Literature Review

As you can see, similar approaches that were developed earlier can be used in different spheres once we review the results of further research of the following publications. In modern literature, there is a lot of research related to social media’s impact on the blockchain ecosystem. Additionally, this research aligns with various theoretical perspectives on the nature of the interactions between social media and the blockchain ecosystem, highlighting different ways to apply the findings of these studies. In other words, each study of such events and corresponding hypotheses have a different interpretation depending on the original goals.

For instance, Mankala et al. ( 2023 ) researched and reported that increasing public concerns about the environment have led to many studies that have explored current issues and approaches towards its protection. Much less studied, however, is the topic of public opinion surrounding the social media impact that cryptocurrencies are having on the environment. The cryptocurrency market and blockchain ecosystem—in particular, Bitcoin—currently rivals other top well-known assets such as precious metals and exchanged traded funds in market value, and the market is growing. Their work ( Mankala et al. 2023 ) examines public opinion expressed about the environmental impacts of Bitcoin derived from Twitter feeds. Three primary research questions were addressed in this work related to topics of public interest, their location, and people and places involved. Their findings show that factions of the public are interested in protecting the environment, with topics that resonate mainly related to energy.

Another publication ( Guinda & Bhattacharyya 2021 ) presents an updated correlation analysis of 31 crypto assets, among them and with some equity and gold indices. Furthermore, they conducted a PCA to identify the group of cryptos that present different correlation patterns and may help us build a diversified portfolio. The correlation update shows that these cryptoassets, which account for approximately 80% of the market, have been positively correlated since 2017 and Ether has been the asset with the highest results. These correlations increase during bear markets, especially in the current bear period started in April 2021. When analyzing Bitcoin against equity markets, they confirmed that correlation is very volatile and swings from positive to negative continuously, which makes it very difficult to use Bitcoin as an equity hedge. As a closing, they have observed that the only times that Bitcoin presented negative correlation with equity indexes coincides with times when gold also showed negative correlation, which could reveal the use of the digital asset as a store of value.

Enough interesting statistical data research has been shown by Wike et al. ( 2022 ), and this analysis focuses on technology use and views of internet and social media in the context of democracy and society. The survey was conducted in 19 advanced economies in North America, Europe, the Middle East, and the Asia-Pacific region. For non-US data, this report draws on nationally representative surveys of 20,944 adults from Feb. 14 to June 3, 2022. The survey is weighted to be representative of the US adult population by gender, race, ethnicity, partisan affiliation, education, and other categories. Respondents who took part in the telephone survey had somewhat higher rates of internet use, smartphone ownership, and social media use.

As can be seen, the social media impact can have some different implementations in other spheres. For example, Kanchan and Gaidhane ( 2023 ) could show their research aimed to conduct an introductory study of the existing published literature on why to choose and how to use social media to obtain population health information and to gain knowledge about various health sectors like disease surveillance, health education, health research, health and behavioral modification, influence policy, and enhance professional development and doctor-patient relation development. They searched for publications using databases like PubMed, NCBI, and Google Scholar, and combined 2022 social media usage statistics from PWC, Infographics Archive, and Statista online websites. The American Medical Association (AMA) policy about Professionalism in Social Media Use, American College of Physicians-Federations of State Medical Boards (ACP-FSMB) guidelines for Online Medical Professionalism, and Health Insurance Portability and Accountability Act (HIPAA) social media violations were also briefly reviewed. Their findings reflect the benefits and drawbacks of using web platforms and how they impact public health ethically, professionally, and socially. During their research, they discovered that social media’s impact on public health concerns is both positive and negative, and we attempted to explain how social networks are assisting people in achieving health, which is still a source of much debate.

Venturing into the Depths of Downvoting: Insights from Blockchain-based Social Media Networks – Commencing our journey, we delve into the intricate realm of user behavior within blockchain-driven social media ecosystems. Authored by R. Sun, C. Li, J. Liu, and X. Sun, this illuminating piece, published on 1 May 2023, draws from a reservoir of four years’ worth of data sourced from Steemit. At the heart of this exploration lies an unraveling of the enigmatic influence wielded by automated entities on the ebb and flow of voting dynamics. An intriguing revelation emerges—a substantial contingent of these mechanized agents purposefully cast their votes in opposition to the prevailing content.

Pioneering a Media Revolution: The Fusion of Blockchain Tech and Media Transformation—Embarking on an odyssey of transformation, this article penned by Momčilo Bajac and M. Vojinović, and unveiled on August 28, 2022, unearths the impact of groundbreaking Distributed Ledger Technologies (DLTs), colloquially known as blockchain technologies, on the evolving visage of media landscapes. As we navigate through the narrative, we witness the profound influence that these innovative technologies have cast upon the traditional bastions of news dissemination and the journalism profession itself.

Bridging the Democratic Deficit with Blockchain: Navigating the Crossroads of Social and News Media—Our expedition continues with a piece authored by N. Nicoli, S. Louca, and P. Iosifidis, released on August 19, 2022, inviting contemplation on the potential of blockchain technology to reshape the trajectory of democratic deficits pervasive within the realm of information and communication. Amidst these pages, we encounter a thoughtful exploration of the dual role that blockchain may undertake—either as a remedy to prevailing challenges or as an exacerbating force amplifying existing issues.

Unveiling Blockchain’s Role in Enriching Social Media: A Methodical Literature Review—Embarking on a scholarly voyage, crafted by the adept hands of M. A. Hisseine, D. Chen, and X. Yang, and unveiled on June 28, 2022, we find a comprehensive and systematic review of literature centered around the fusion of blockchain technology and the social media landscape. Through this analysis, a prevailing theme emerges—earlier scholarly efforts have predominantly fixated on combating the proliferation of misinformation and reinforcing the fortress of data privacy within these interconnected digital realms.

In addition, we plan to consider some other cases in the blockchain ecosystem. Nowadays in the globalized world, there is an ongoing process of evaluation of the development level of characteristics and usage possibilities of the blockchain ecosystem analyzed by Kotenko et al. ( 2020 ) and Kryshtanovych et al. ( 2021 ). According to Kavun, Zavgorodnia, and Petrenko ( 2020 ), as well as Potii et al. ( 2019 ) and Pavlyshyn I. ( 2016 ), the creation of broad opportunities for the exchange of knowledge processes significantly enhances the competitiveness of these entities in both domestic and foreign markets.

A perusal of academic literature reveals that the interrelation between blockchain technology and social media is a burgeoning research domain. Yet, the majority of these investigations address overarching themes, such as the influence of social media on the dissemination of blockchain and cryptocurrency intel, and the employment of blockchain to augment security and transparency in social media platforms.

3. Problem Statement

Despite the vast potential and opportunities, there remains a significant challenge in comprehensively understanding and evaluating the influence of social media on blockchain ecosystems, such as Cosmos ( Cosmos Network 2023 ; Hayes 2019 ). Given the integral role that social media now plays in people’s lives ( Crane & Sornette 2008 ), its impact on the evolution of cryptocurrencies and blockchain technologies warrants thorough exploration ( Bonneau et al. 2015 ). Currently, there is a significant knowledge gap regarding how information disseminated via social media shapes the perception and adoption of blockchain technology, especially within the Cosmos ecosystem ( Antonopoulos & Wood 2018 ; Bamert et al. 2013 ). Understanding how various forms of social media influence changes in Cosmos’ financial metrics can aid in devising strategies to bolster this ecosystem further ( Grinberg 2011 ). Thus, the central inquiry of this paper is: to what degree, and in what manner, does social media influence the Cosmos blockchain ecosystem and its financial performance? This exploration encompasses a review of the current state of research in this domain ( Bonneau et al. 2015 ; Hayes 2019 ), an investigation into the repercussions of social media on Cosmos’ financial metrics ( Antonopoulos & Wood 2018 ), and a discourse on potential trajectories and perspectives for future research ( Cosmos Network 2023 ).

Despite the expansive promise and opportunities that come with integrating blockchain and social media, there is a notable gap in our understanding of how these two spheres interact, especially within specific blockchain ecosystems like Cosmos ( Cosmos Network 2023 ; Hayes 2019 ). Given the pervasive role of social media in contemporary life ( Crane & Sornette 2008 ), an exhaustive examination of its impact on the maturation of cryptocurrencies and blockchain technologies is imperative ( Bonneau et al. 2015 ). At present, the lacuna in our knowledge pertains to how information propagation via social media influences both public perception and subsequent adoption of blockchain technologies, particularly within the Cosmos ecosystem ( Antonopoulos & Wood 2018 ; Bamert et al. 2013 ). A nuanced understanding of how different social media platforms affect variations in Cosmos’ financial metrics could assist in formulating strategies to fortify the ecosystem further ( Grinberg 2011 ).

Therefore, the focal question of this paper is: To what extent, and in what ways, does social media exert influence on the financial performance and general dynamics of the Cosmos blockchain ecosystem? Our investigation will include a synthesis of the extant literature in this field ( Bonneau et al. 2015 ; Hayes 2019 ), an empirical analysis of the impact of social media on Cosmos’ financial indicators ( Antonopoulos & Wood 2018 ), and a contemplative discussion on potential directions for future scholarly inquiry ( Cosmos Network 2023 ).

Specifically, the research tasks we aim to address in our study are as follows:

  • Identification of key social media platforms that have a tangible impact on the Cosmos blockchain ecosystem.
  • Examination of the mechanisms through which social media influences voting outcomes within the blockchain ecosystem, and the metrics that may be affected.
  • Assessment of the correlation, if any, between Twitter sentiment and voting results within the Cosmos ecosystem.

These tasks are designed to offer a comprehensive perspective on the interaction between social media and the Cosmos blockchain, thereby contributing to the broader understanding of the dynamic interplay between digital technologies and financial ecosystems.

4. Research Approach

The selection of the Twitter platform as a data source is underpinned by several considerations. Primarily, Twitter is extensively utilized by members of the cryptocurrency community, rendering it an optimal medium for examining the interplay between social media and blockchain initiatives. Moreover, Twitter’s API offers robust tools for the systematic collection and processing of data, thereby enhancing the reliability and precision of our analysis. Our research is delineated as follows ( Figure 4.1 ).

Research Process Structure

Research Process Structure.

Through a holistic approach and the deployment of advanced analytical tools, our research furnishes a profound comprehension of the nexus between social media activity and blockchain project dynamics.

5. Results and Analyses

5.1 relationship, percentage of stacked tokens, inflation, and apr.

As part of our research, we assessed the three-dimensional relationship between the following key metrics in the Cosmos blockchain protocol network: token staking percentage (staked_ratio), inflation rate, and APR. Within the blockchain network, ‘inflation’ refers to the programmed increase in the total supply of tokens over time. This is a strategic mechanism in Cosmos designed to encourage participation and maintain network security. Unlike conventional economic inflation, which is often seen as a decrease in purchasing power, inflation in the blockchain context serves a functional purpose. It incentivizes users to engage in staking by rewarding them with newly created tokens. This dynamic rate of token creation is adjusted based on the staking ratio, ensuring a balanced ecosystem where active participation is rewarded and network integrity is upheld ( Everstake, 2023a , b ).

According to the insights drawn from Figures 5.1 and 5.2 , there’s a straightforward relationship between the rate of tokens being staked and the inflation rate within the blockchain network. Specifically, when the staking rate falls below the threshold of 67% during the period from 09.18.21 to 03.18.23 on Figure 5.1 , the inflation rate experiences an uptick during the same period. Conversely, an increase in the staking rate above 67% during the period from 03.18.23 to 05.18.23 on Figure 5.1 leads to a decrease in the inflation rate during the same period as shown in Figure 5.2 .

Dynamics of changes in staked ratio over time.

Dynamics of changes in staked ratio over time.

Dynamics of changes in APR and inflation over time

Dynamics of changes in APR and inflation over time.

The study also uncovers a directly proportional relationship between the Annual Percentage Rate (APR) and the inflation rate. Within the blockchain protocol for ATOM, the APR is determined by a formula that takes into account both the inflation rate and the staking ratio. The formula is given as Equation 5.1:

Here, the variables are defined as follows:

  • APR : Annual Percentage Rate
  • inflation : Current inflation rate
  • staked ratio : Percentage of tokens currently staked
  • NetFactor : A constant factor, defined as 90%, which adjusts the APR by reducing the total value by 10%.

This empirical evidence lends credence to the initial hypothesis, suggesting a direct correlation between APR and the inflation rate. Importantly, the ATOM blockchain protocol sets the inflation rate within a range of 7% to 20%. Therefore, any variations in APR are exclusively attributed to changes in the inflation rate and staking ratio; other external or internal variables do not impact this particular metric.

Based on the analysis of the data presented in Figure 5.2 , we can conclude that there is a weak inverse correlation between the percentage of token staking and APR with a coefficient of –0.04. The graph shows that even with significant changes in the staking percentage, the APR can remain relatively stable or vary in a different direction. This could be due to the fact that the APR depends not only on the staking percentage but also on the inflation rate, which in turn can be subject to its own dynamic changes. However, a strong direct correlation with a coefficient of 0.98 is observed between inflation rate and APR.

Overall, our analysis highlights the importance of understanding the relationship between key metrics when analyzing the blockchain ecosystem. The most important is to understand the relationship between inflation rate and staking percentage, as these parameters directly affect such an important metric as APR.

Based on our research, we confirmed the hypothesis that there is a relationship between token staking percentage, inflation, and the APR in the Cosmos blockchain ecosystem. The analysis showed that when the token-stacking percentage drops to the 67% mark, inflation starts to rise. In the context of the ATOM protocol, inflation varies between 7% and 20%. A change in parameters such as token staking or inflation directly affects the APR. For example, the correlation between inflation rate and APR is 98%, indicating that these metrics are closely related.

5.2 Relationship between total staking volume and ATOM price

In addition to the previous analysis, we examined the relationship between total staking volume and the price of the ATOM token in the Cosmos blockchain ecosystem.

In a visual analysis of the charts ( Figure 5.3 ), we find that the total staking volume and the ATOM price tend to increase over time. The total amount of staked funds increased strongly in April 2023, which can also be linked to the fall in inflation in the charts in the last section. In the price chart for the period described, no particular changes are observed.

Dynamics of total stake and ATOM price change over time

Dynamics of total stake and ATOM price change over time

If we limit our analysis to the data for a single year, taking the year 2022 as a reference ( Figure 5.4 ), we can observe a decrease in prices from the beginning of the year until the summer of 2022, after which the price becomes more stable. Concurrently, the value of the total stake consistently increases throughout the interval over the course of the year.

Figure 5.4 Dynamics of total stake and ATOM price change for the period of 2022

Dynamics of total stake and ATOM price change for the period of 2022.

In our correlation analysis, we found that there is a weak inverse relationship between total staking volume and ATOM price, with a correlation coefficient of –0.18. This may imply that as total staking volume increases, ATOM price tends to decrease, and vice versa. However, given the weakness of this relationship, this trend may be unstable and subject to change due to other factors.

The Pearson correlation coefficient between ATOM price and total stake for 2022 ( Figure 5.4 ) is approximately –0.65. This indicates a moderate inverse relationship between these two variables: when ATOM price increases, total stake tends to decrease, and vice versa.

Since the ATOM blockchain protocol has a condition that delegates cannot make an unstake from the time of staking their savings for 21 days, consider the correlation with a shift of 21 days ( Figure 5.5 ).

Figure 5.5 Dynamics of total stake change (21-day shift) and ATOM price change over the period 2022

Dynamics of total stake change (21-day shift) and ATOM price change over the period 2022.

The correlation between ATOM price and total stake, taking into account the shift of total stake data 21 days into the future, is approximately –0.53. As the correlation became weaker, we decided to calculate a rolling correlation with a window of 30 days ( Figure 5.6 ).

Dynamics of change in the correlation with 30 days data window of total stake and ATOM price change over the period of 2022

Dynamics of change in the correlation with 30 days data window of total stake and ATOM price change over the period of 2022.

Correlation with 30 days data window is a technique that measures the degree of relationship between two variables over time. In this case, we see that the correlation ranges from –0.8 to 0.8, indicating a moderate and almost fairly stable negative relationship between ATOM price and total stake.

To confirm the obtained results, we will similarly calculate the data for the whole period with a window of a month and a quarter.

This chart ( Figure 5.7 ) shows the correlation between ATOM price and total stake over two windows: 30 days (green line) and 90 days (red line), which roughly corresponds to one quarter.

Evolution of the correlation (with a monthly and quarterly window) of total stake and ATOM price change over the period 2022

Evolution of the correlation (with a monthly and quarterly window) of total stake and ATOM price change over the period 2022.

Both correlation windows show that there is a moderate negative correlation between ATOM price and Total stake. This means that during the whole period considered, when ATOM price increased, Total stake tended to decrease, and vice versa.

It is important to note that the correlation with a 90-day window provides a smoother line, indicating less sensitivity to short-term fluctuations, compared to the correlation with a 30-day window.

Overall Analysis: There is a weak inverse relationship between total staking volume and ATOM price with a correlation coefficient of –0.18.

Analysis for Year 2022: The correlation for this period is –0.65, indicating a moderate inverse relationship.

Analysis with 21-Day Delay: The correlation with the 21-day forward bias of the total stake data is approximately –0.53.

Correlation with 30-Day Window for Year 2022: Varies from –0.8 to 0.8, indicating a moderate and almost fairly stable negative relationship.

Analysis with a monthly and quarterly window shows a moderate negative correlation between ATOM price and total stake, with a smoother line when using a 90-day window.

Thus, all the data suggest that there is a moderate inverse correlation between ATOM price and staking volume. This correlation may indicate a tendency for ATOM price to decrease with increasing staking volume and vice versa. However, it should be taken into account that this relationship may be unstable and change under the influence of other factors.

5.3 The impact of social media on blockchain voting outcomes

Social media has emerged as a powerful tool for the dissemination of information and the formation of public opinion, especially in rapidly evolving technological landscapes like blockchain. The advent of blockchain-based voting systems has introduced a new dynamic to governance, allowing community members to vote on proposals in a transparent and secure manner. Given that these blockchain systems often handle decisions that can influence the future trajectory and even the value of the respective ecosystems, understanding the influence of social media on voting outcomes becomes paramount.

In this section, we aim to explore this dynamic by focusing not just on one, but multiple proposals within different blockchain systems, starting with Proposal #797 in the Cosmos Hub as a case study. Social media sentiment, especially on platforms like Twitter, can act as a barometer for public sentiment and potentially even predict the outcomes of these proposals. By examining multiple proposals, we aim to draw generalized conclusions about the overarching trends and effects of social media on blockchain-based decision-making processes.

5.3.1. Voting 797 sentiment score of tweets

Social media plays a significant role in shaping public opinion and can have an important impact on the decision-making process within blockchain systems. In this research, we conducted a sentiment analysis of tweets to gauge public opinion on the proposal.

Proposal #797 offers to increase from 175 to 180 the number of validators in Cosmos Hub.

As part of our research, we analyzed over 100,000 tweets related to Cosmos Hub and Proposal #797. Among them, we identified and scrutinized tweets from key influencers and blockchain experts who have thousands of followers and whose opinions are often considered authoritative in the community.

Tweets were also selected based on their popularity, taking into account the number of retweets, likes, and impressions. This allowed us to gauge not only the sentiments of individuals, but also the broader audience reaction to Proposal #797.

Using such a funnel, we successfully filtered 143 tweets from the existing tweets that related to Proposal #797. Below is a graph ( Figure 5.8 ) of the sentiment distribution among these particular tweets.

Distribution of sentiment score for tweets related to the vote on Proposal #797

Distribution of sentiment score for tweets related to the vote on Proposal #797.

The average sentiment for tweets related to the vote on Proposal #797 is approximately 0.1734. This value indicates an overall neutral or slightly positive coloring of the discussion. Further categorized into positive, neutral, and negative sentiment. For this purpose we set the threshold for neutral sentiment from –0.1 to 0.1, for positive sentiment from 0.1 to 1 and for neutral sentiment from –1 to –0.1 ( Figure 5.9 ).

Distribution of sentiment classification for tweets related to the vote on Proposal #797

Distribution of sentiment classification for tweets related to the vote on Proposal #797.

The histogram of sentiment distribution shows the following:

  • Positive Tweets (66.43%): The majority of tweets had a positive sentiment, reflecting general support for the proposal.
  • Neutral tweets (28.67%): A significant proportion of tweets were neutral, perhaps reflecting a neutral or reserved reaction to the proposal.
  • Negative tweets (4.90%): A small proportion of tweets were negative, indicating limited opposition to the proposal.
  • This distribution is consistent with the poll result, where 86.07% voted FOR.

Next, let’s look at the distribution of tweets by categories such as number of retweets, likes, quotes, bookmarks and impressions ( Figures 5.10[a] , 5.10[b] , 5.11[a] ):

Distribution of retweets for tweets related to the vote on Proposal #797

Distribution of retweets for tweets related to the vote on Proposal #797.

Normalized distribution of retweets for tweets related to the vote on Proposal #797

Normalized distribution of retweets for tweets related to the vote on Proposal #797.

Figure 5.11[a] Distribution of reply for tweets related to the vote on Proposal #797

Distribution of reply for tweets related to the vote on Proposal #797.

The distribution of retweets and replies on Twitter reveals an interesting pattern: the majority of tweets receive a relatively small number of retweets, averaging around 4.23 per tweet. Additionally, there is a significant number of tweets that go unnoticed, garnering zero likes. However, there are a few outliers that manage to amass more than 100 retweets, indicating that while most tweets have limited reach, some break through and achieve widespread attention. Similarly, on a normalized graph ( Figure 5.11[b] ) along the Y-axis, a similar distribution pattern can be observed. However, here it is evident that the majority of tweets have a number of replies equal to zero.

Normalized distribution of reply for tweets related to the vote on Proposal #797

Normalized distribution of reply for tweets related to the vote on Proposal #797.

Likes: Likes follow a similar distribution, with most tweets having a relatively small number of likes ( Figures 5.12[a] , 5.12[b] ).

Distribution of likes for tweets related to the vote on Proposal #797

Distribution of likes for tweets related to the vote on Proposal #797.

Normalized distribution of likes for tweets related to the vote on Proposal #797

Normalized distribution of likes for tweets related to the vote on Proposal #797.

Impressions: Impressions have a more even distribution, indicating diversity in the number of impressions between different tweets ( Figures 5.13[a] , 5.13[b] ).

Distribution of impressions for tweets related to the vote on Proposal #797

Distribution of impressions for tweets related to the vote on Proposal #797.

Normalized distribution of impressions for tweets related to the vote on Proposal #797

Normalized distribution of impressions for tweets related to the vote on Proposal #797.

Quotes: These metrics also follow a similar distribution, with most tweets having low values ( Figure 5.14 ).

Distribution of quotes for tweets related to the vote on Proposal #797

Distribution of quotes for tweets related to the vote on Proposal #797.

Based on the analysis of over 100,000 tweets related to Cosmos Hub and Proposal #797 from key influencers and the general public, the following conclusions can be drawn.

Sentiment Towards Proposal #797:

The average sentiment score of 0.1734 indicates a generally neutral to slightly positive view toward the proposal, mirroring the 86.07% ‘FOR’ vote in the actual poll. This strong correlation between sentiment and voting behavior suggests that social media sentiment can be a reliable indicator of community perspective.

Engagement Metrics:

The similar distribution patterns in retweets, likes, and quotes indicate a level of uniformity in how tweets, regardless of sentiment, engage the audience. This could imply that the Twitter community is equally willing to engage with differing viewpoints on Proposal #797.

Sentiment Categories ( Table 5.1 ):

  • –Positive Tweets: Making up 66.43% of the total, the high percentage of positive tweets demonstrates a broad base of support for the proposal.
  • –Neutral Tweets: At 28.67%, the proportion of neutral tweets may represent a section of the community that is either indifferent or awaiting more information before forming a strong opinion.
  • –Negative Tweets: Comprising just 4.90% of the tweets, negative sentiment was minimal, corroborating the overwhelming support indicated in the poll.

Analyzing sentiment of tweets related to Proposal #797.

Audience Reach ( Table 5.2 ):

Interaction analysis of tweets related to Proposal #797.

The impressions, which were more uniformly distributed both on standard and normalized graphs, suggest that tweets about Proposal #797 reached a broad and varied audience. This adds depth to the understanding of engagement metrics. It shows that the issue garnered widespread discussion but did not consistently receive high levels of active engagement, such as retweets or likes.

Overall Community Involvement ( Table 5.3 ):

Comparison with voting results of Proposal #797.

The general consistency between high positive sentiment, a significant number of neutral tweets, and the poll results indicates an engaged and generally supportive community. However, the varied engagement metrics hint that this support may not be universally intense, as the community appears to engage similarly with tweets of differing sentiments.

Positive response: The analysis confirms a positive response to the proposal on Twitter, which is consistent with the high level of support in the voting results.

Engagement: Tweets show active engagement with users, which may indicate widespread support for the proposal among active users.

Consistency of online community: The overall positive sentiment according to the voting results indicates consistency between online community and formal voting. It is important to note that our analyses are limited to the data that was available in the tweets. Some community members may have expressed their opinions through other channels or may have chosen not to express them publicly at all. Nevertheless, our results indicate that social media analysis can serve as an important tool for understanding community sentiment and predicting voting outcomes. This can help community members better understand and take into account different perspectives when making decisions.

5.3.1.2 Vote 797, impact of voting results on economic performance

Next, consider the impact of voting results on the economic performance of the ATOM blockchain.

During the voting period running from May 1, 2023, to July 1, 2023, we have seen some momentum in the economic performance of the ATOM blockchain ( Figures 5.15 – 5.16 ).

Dynamics of changes in APR and inflation over time

One of the key metrics we analyzed was the staked ratio—the percentage of total tokens that are currently participating in staking. After increasing the number of validators as a result of voting, the staked ratio showed a slight increase. This indicates that the new validators have successfully attracted additional resources in the form of staked tokens.

This increase in the staked ratio also had an impact on another important metric, Inflation. As a result of the increase in the staked ratio we observed a moderate decrease in the inflation rate. This is consistent with the mechanisms of the protocol, according to which an increase in the share of staked tokens leads to a decrease in inflationary pressure.

The APR, or annual percentage rate that participants receive for participating in staking, also depends on the inflation rate and the proportion of staked tokens.

According to the formula 5.1, an increase in staked ratio and a decrease in inflation leads to a change in APR.

Thus, during the voting period, we observed a complex interplay of economic indicators, where each change in one indicator triggers a chain of changes in others. These dynamics emphasize the close relationship between voting, validator activity and the blockchain economic environment.

The graphs ( Figure 5.17 ) above show the changes in total stake and ATOM price during the voting period from May 1, 2023, to July 1, 2023.

Dynamics of total stake and ATOM price change

Dynamics of total stake and ATOM price change.

  • Total Stake: This graph shows the total volume of tokens that were staked on the blockchain during the specified period. As can be seen, the total stake shows a steady growth throughout the period. This is likely due to the increase in the number of post-vote validators, which resulted in additional resources for staking.
  • ATOM Price: This chart shows the changes in the price of the ATOM token during the period in question. Here we can see that the ATOM price also shows a relatively stable state during this period. The increase in the number of validators may have contributed to some changes.

Based on the analysis of the economic performance of the ATOM blockchain during the voting period from May 1, 2023, to July 1, 2023, the following conclusions can be drawn.

Changes in Staking: There was a slight increase in the staked ratio following the increase in the number of validators as a result of voting. This supports the hypothesis that the new validators helped to attract additional resources to the blockchain.

Impact on Inflation: The increase in the staked ratio led to a moderate decrease in the inflation rate, which is consistent with the mechanisms of the blockchain protocol ATOM.

APR dynamics: The Annual Percentage Rate (APR) for staking participants has also undergone changes due to fluctuations in staked ratio and inflation.

Total Stake: The ‘total stake’ graph showed a steady increase over the entire period analyzed, indicating active participation of participants in staking, especially after the vote.

ATOM Price Dynamics: Despite changes in the blockchain structure and an increase in the number of validators, the price of the ATOM token has remained relatively stable. This suggests that the factors influencing the price were more complex than just changes in the validator ecosystem.

5.3.2 Voting 88 evaluating the sentiment of tweets

To gauge public opinion on Proposal #88 to raise the community pool rate from the current 2% to 10%, we conducted a sentiment analysis of tweets.

The graph below shows the distribution of sentiment for tweets related to the vote. We can see that the majority of tweets have a positive sentiment, which is consistent with the overwhelming majority of ‘FOR’ votes in the voting results. However, there are also a significant number of tweets with neutral and negative sentiment, indicating that there is a diversity of opinion and discussion around this proposal ( Figure 5.18 ).

Distribution of sentiment score for tweets related to the vote on Proposal #88

Distribution of sentiment score for tweets related to the vote on Proposal #88.

The average sentiment for tweets related to voting is 0.154 on a scale of –1 to 1. This value indicates an overall positive sentiment in tweets related to this vote.

The distribution chart above ( Figure 5.19 ) shows the sentiment classes for tweets related to voting:

Distribution of sentiment classification for tweets related to the vote on Proposal #88

Distribution of sentiment classification for tweets related to the vote on Proposal #88.

  • For this purpose, we arranged the threshold for neutral sentiment from –0.1 to 0.1, for positive sentiment from 0.1 to 1 and for neutral sentiment from –1 to –0.1.
  • Positive sentiment: 54.55% of tweets
  • Neutral sentiment: 35.84% of tweets
  • Negative sentiment: 9.62% of tweets

This distribution reflects an overall positive attitude towards voting, but also indicates some neutral and negative views.

Next, let’s look at the distribution of tweets by categories such as number of retweets, likes, quotes, bookmarks, and impressions.

Distribution of the number of retweets.

Most average positive tweets have less than 10 retweets ( Figures 5.20[a] , 5.20[b] ).

Distribution of retweets for tweets related to the vote on Proposal #88

Distribution of retweets for tweets related to the vote on Proposal #88.

Normalized distribution of retweets for tweets related to the vote on Proposal #88

Normalized distribution of retweets for tweets related to the vote on Proposal #88.

Distribution of the number of replies: Most tweets have less than 10 replies ( Figures 5.21[a] , 5.21[b] ).

Distribution of replies for tweets releated to the vote on Proposition #88

Distribution of replies for tweets releated to the vote on Proposition #88.

Normalized distribution of replies for tweets related to the vote on Proposal #88

Normalized distribution of replies for tweets related to the vote on Proposal #88.

Distribution of the number of likes: Most tweets have less than 50 likes ( Figures 5.22[a] , 5.22[b] ).

Distribution of likes for tweets related to the vote on Proposal #88

Distribution of likes for tweets related to the vote on Proposal #88.

Normalized distribution of likes for tweets related to the vote on Proposal #88

Normalized distribution of likes for tweets related to the vote on Proposal #88.

Distribution of the number of quotes.

Very few tweets have been quoted ( Figure 5.23 ).

Distribution of quotes for tweets related to the vote on Proposal #88

Distribution of quotes for tweets related to the vote on Proposal #88.

Distribution of number of impressions: Most tweets have less than 5,000 impressions ( Figures 5.24[a] , 5.24[b] ).

Distribution of impressions for tweets related to the vote on Proposal #88

Distribution of impressions for tweets related to the vote on Proposal #88.

Normalized distribution of impressions for tweets related to the vote on Proposal #88

Normalized distribution of impressions for tweets related to the vote on Proposal #88.

Average values for various metrics in average positive tweets ( Table 5.4 ).

Analyzing sentiment of tweets related to Proposal #88.

As part of researching the discussion of the vote on Proposal #88 (‘Increase the Community Pool Tax on the Cosmos Hub’) on Twitter, a comprehensive analysis of user sentiment and interaction was conducted. The research included the following key aspects:

Positive Reaction ( Table 5.4 ): The analysis confirms a positive reaction to the proposal on Twitter, which is consistent with the high level of support in the voting results.

Engagement ( Table 5.5 ): Medium positive tweets show active user interaction, which may indicate widespread support for the offer among active users.

Interaction analysis of tweets related to Proposal #88.

Consistency with Twitter discussion ( Table 5.6 ): The overall positive sentiment according to the voting results indicates consistency between online discussion and formal voting.

Comparison with voting results of Proposal #88.

5.3.2.2 Voting 88, impact of voting results on economic performance

In this analysis, we look at the evolution of staked ratio and inflation over the period from November 1, 2022, to January 31, 2023, which includes the Proposal #88 voting period.

Figures 5.25 and 5.26 show that during the voting period there is a slight increase in the staked ratio at the moment after the vote is passed.

Dynamics of change of APR and inflation in time

Dynamics of change of APR and inflation in time.

Dynamics of change of staked ratio in time

Dynamics of change of staked ratio in time.

At the same time, we see that inflation shows an upward trend during this period. This is also in line with our expectations because, as we discussed earlier, inflation is rising as the staked ratio is less than 67%.

Figure 5.25 shows the APR for the period from November 1, 2022, to January 31, 2023. There is a tendency to increase APR in the period under consideration. It may be noted that after the change in community pool tax to 10% APR the growth should be reduced, but in fact this is not shown in the graph.

For the next point of analysis we will look at two key metrics: total staking and the ATOM token price. Both of these metrics were examined for the period from November 1, 2022, to January 31, 2023, which covers the period of the vote on the community tax increase proposal ( Figure 5.27 ).

Dynamics of change of total stake and price in time

Dynamics of change of total stake and price in time.

Correlation analysis of these two indicators showed a positive relationship, with a correlation coefficient of approximately 0.54. This means that when the total volume of staking increases, the price of ATOM token also tends to increase, and vice versa.

This relationship can be clearly seen in the graph, where both indicators are plotted over time. During the voting period, the total staking volume and ATOM price show an overall upward trend. This can be attributed to an increase in interest in blockchain participation and, by extension, participation in staking in response to the proposal to increase the community tax. An increase in the tax rate may incentivize participants to participate more in staking, which in turn may affect the price of ATOM.

Based on our analysis of the economic performance of the ATOM blockchain for the period from November 1, 2022, to January 31, 2023, covering the Proposal 88 voting period, the following conclusions can be drawn:

Changes in staking: During the voting period, there is an increase in the staked ratio. This increase is particularly pronounced after voting decisions have been made.

Inflation dynamics: The increase in inflation during the analyzed period correlates with the statement that inflation rises when the staked ratio is less than 67%. This means that a smaller volume of tokens in staking leads to higher inflation.

APR trend: Despite the expected decrease in APR growth after the changes in the community pool tax, real data show its continued growth. This indicates the possible influence of other economic or market factors on APR.

Correlation of staking volume and ATOM price: The positive correlation between total staking volume and ATOM price (with a correlation coefficient of 0.54) confirms that when staking participation increases, ATOM price also tends to increase.

5.4 Conclusions about the social media analysis

Overall, based on our analysis, we can conclude that Twitter activity has a significant impact on various aspects of blockchain project performance, including validator stacking, token price, trading volume, APR, and voting outcomes. These findings open new perspectives for further research on the impact of social media on the performance of blockchain projects.

Online Community Reaction: Interactions and sentiment from Twitter users demonstrate that social media actively reflects the sentiment and preferences of the blockchain community. Discussion of votes on Proposals #797 and #88 ( Tables 5.7 , 5.8 , 5.9 , 5.10 , 5.11 , 5.12 ) shows a positive trend in line with the results of official votes.

Analyzing Sentiment of tweets related to Proposal #797.

Interaction Analysis of tweets related to proposal #797

Comparison with voting results of Proposal #797

Interaction Analysis of tweets related to proposal #88.

Voting #88:

The analysis confirms the positive reaction to the proposal on Twitter, which is consistent with the high level of support in the voting results.

  • Changes in staking: There was a noticeable increase in the staked ratio after both votes were approved, indicating active community participation in the blockchain activities, as well as on the active response and increase in the percentage of funds withdrawn after the vote to add five validators was passed.
  • Inflation dynamics: Inflation and its relationship with the staked ratio confirm the complex economic mechanisms of the ATOM blockchain. It is observed that inflation increases with less tokens in staking, suggesting that a certain level of participation in staking needs to be maintained to stabilize inflation.
  • APR and market factors: Despite changes in the voting record #88, the APR continued to rise, indicating the possible impact of other market and economic factors, which caused the APR to increase despite the passage of the community pool rate-hike vote.
  • The correlation does not suggest a direct causal relationship but highlights a possible interaction between Twitter activity and the financial dynamics of blockchain projects.
  • Correlation with ATOM price and total stake: The positive correlation between these indicators indicates an important relationship between staking participation and the ATOM market price. Also note that if we track the correlation between staking volume and ATOM price, the analysis with a monthly and quarterly window shows a moderate negative correlation between the two, with a smoother line when using a 90-day window.
  • We observed a complex interplay of economic indicators, where each change in one indicator triggers a chain of changes in others. These dynamics emphasize the close relationship between voting, validator activity, and the blockchain economic environment.

Based on these points, we recommend the following improvements and directions for future research:

  • Extend the analysis to other social media such as Facebook, Reddit, and Telegram to gain a more comprehensive understanding of the impact of social media on the blockchain projects.
  • Deepen the research on the interaction between social media and economic conditions to better understand how external factors can influence outcomes.
  • Examine the influence of specific communities and influential social media personalities on blockchain projects to determine which factors and comments have the greatest impact on market participants’ decisions.
  • Sociological analysis of community reaction: Detailed research of the motives and reasons for community reaction to the ballot proposals will help to better understand the dynamics and underlying drivers of participant behavior.
  • Incentivizing participation in staking: Explore possible methods and tools to incentivize participation in staking in order to optimize inflation and other economic indicators.
  • Innovation impact research: Assessing how various technical and economic innovations in the ATOM blockchain ecosystem affect the dynamics of its economic performance.
  • .Benchmarking: Researching other blockchains and their ecosystems to compare with ATOM can help identify unique features and develop common development strategies.

As a result, further research in the above directions will enable a deeper and more complete understanding of the ATOM blockchain ecosystem, predict possible changes, and make informed decisions at various levels of governance.

6. Discussion

In our research, we investigated the impact of Twitter activity on various aspects of blockchain project performance, including validator stacking, token price, trading volumes, APR, and voting outcomes. Our results indicate a correlation between these elements, which supports our hypothesis of a meaningful impact of social media on blockchain projects. Specifically:

  • The correlation between Twitter activity and token price was 0.54, indicating that when Twitter activity increases, the ATOM price also tends to increase.
  • The positive average sentiment on Twitter (0.154 on a scale of –1 to 1) coincides with the high level of support for Proposal #88 in the poll results (99.27% of ‘FOR’ votes).
  • Average user interactions on Twitter, such as 6.18 retweets, 34.55 likes, and 4.45 replies, indicate active participation and discussion of the proposal on social media.

However, it is important to note that correlation does not indicate direct causation. Additional factors such as external economic conditions, market trends, and the characteristics of individual blockchain projects may also influence these results.

For future research, we recommend:

  • An in-depth analysis of other social platforms: In addition to Twitter, platforms like Facebook, Reddit, Telegram, and Discord play a key role in shaping public opinion in the cryptocurrency community. Exploring their influence can provide additional perspective.
  • Investigating the influence of influencers: Assessing the influence of key personalities in the cryptocurrency space in shaping public opinion.
  • Cross-correlation analysis: Examining the interaction between different metrics to identify more complex relationships.
  • Analyzing long-term trends: Analyzing how long-term trends in social media affect blockchain projects in the long-term.
  • To fully understand the impact of social media on blockchain projects, other factors such as external economic conditions, market trends, and the specifics of individual projects should also be considered.
  • Extend the scope to include other POS networks and examine the influence of platforms like Twitter, to test whether the hypotheses hold across different blockchain environments.
  • Investigating the impact of automated entities in the Cosmos ecosystem and recent Twitter developments: This research aims to delve deeper into the influence of automated agents and bots within the Cosmos ecosystem, particularly focusing on their effects on voting dynamics and user interactions. Special attention should be given to recent events such as the mass removal of bots on Twitter, to investigate how such actions impact the dynamics and user engagement in blockchain projects ( Reuters 2022 ). This will allow for an assessment of how the cleanup of social media from automated accounts can affect public perception and activity within the blockchain ecosystem, as well as strategies for community management and optimization of user interaction.

In conclusion, this analysis highlights the importance of social media for blockchain projects, confirming the need for further research in this area to better understand interactions and influences. We hope that our findings will serve as a starting point for the next stages of research.

7. Conclusions and Prospects

During the research process, a number of important correlations were found between Twitter activity and various aspects of Cosmos ecosystem functioning. Based on these results, we can conclude that Twitter plays a significant role in the dynamics of this ecosystem.

This important discovery indicates that Twitter interactions are not just a social activity, but a factor that can have a powerful impact on the economic and operational dynamics of blockchain projects. With the growing influence of cryptocurrencies and blockchain technologies on the global economy, social networks, especially Twitter, are becoming a key element in the decision-making system of blockchain projects.

With this in mind, it is recommended that market participants make greater use of social media, particularly Twitter, as a tool for analyzing and forecasting market behavior. This can help them better understand current trends, make informed decisions, and possibly anticipate future developments.

For in-depth analyses of this area, we suggest additional research directions such as: – exploring the impact of other social media (e.g. Reddit, Telegram, Discord) on blockchain projects and a detailed analysis of Twitter’s impact on other aspects of blockchain projects such as partnerships, integrations, and strategic decisions.

Conducted research results not only confirm the relevance of social media to the blockchain ecosystem, but also emphasize the need for further research into this impact. We hope that our findings will be useful for researchers, developers, market participants and all those interested in the impact of social media on blockchain projects.

Additional File

The additional file for this article can be found as follows:

Appendix A and B. DOI: https://doi.org/10.5334/dsj-2024-008.s1

Competing Interests

The authors have no competing interests to declare.

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