An Improved Detection of Cyberbullying on Social Media Using Randomized Sampling

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  • Published: 26 July 2023

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  • Nitasha Dhingra 1 ,
  • Suhani Chawla 1 ,
  • Oshin Saini 1 &
  • Rishabh Kaushal   ORCID: orcid.org/0000-0002-9200-7802 1  

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Due to the pandemic, the world’s dependence shifted to online platforms. It has made all age groups vulnerable to cyberbullying. Now more than ever, there is a need for online behavior monitoring. Existing algorithms tend to classify friendly banter as cyberbullying. They make use of binary classification by identifying offensive keywords. The lack of analysis of the context of data posted and the unavailability of public training data makes it challenging to train models accurately. Our models and research focus on the larger picture by making use of context as a significant parameter during the classification. The dataset chosen was such that its annotation was based on 5 parameters that considered the context of conversations happening online. This paper executes various machine learning algorithms, SVM, random forest, AdaBoost, and MLP algorithms, on a benchmark cyberbullying-representations dataset extracted from Twitter. We conducted randomized oversampling on the best-performing SVM model, which resulted in a significantly higher average F1 score outperforming the baseline score.

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Data related to this work shall be made available on reasonable request.

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Dhingra, N., Chawla, S., Saini, O. et al. An Improved Detection of Cyberbullying on Social Media Using Randomized Sampling. Int Journal of Bullying Prevention (2023). https://doi.org/10.1007/s42380-023-00188-4

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Teens and cyberbullying 2022, nearly half of u.s. teens have been bullied or harassed online, with physical appearance being seen as a relatively common reason why. older teen girls are especially likely to report being targeted by online abuse overall and because of their appearance.

Pew Research Center conducted this study to better understand teens’ experiences with and views on bullying and harassment online. For this analysis, we surveyed 1,316 U.S. teens. The survey was conducted online by Ipsos from April 14 to May 4, 2022.

This research was reviewed and approved by an external institutional review board (IRB), Advarra, which is an independent committee of experts that specializes in helping to protect the rights of research participants.

Ipsos recruited the teens via their parents who were a part of its  KnowledgePanel , a probability-based web panel recruited primarily through national, random sampling of residential addresses. The survey is weighted to be representative of U.S. teens ages 13 to 17 who live with parents by age, gender, race, ethnicity, household income and other categories.

Here are the  questions used for this report , along with responses, and  its methodology .

While bullying existed long before the internet, the rise of smartphones and social media has brought a new and more public arena into play for this aggressive behavior.

social media cyberbullying research paper

Nearly half of U.S. teens ages 13 to 17 (46%) report ever experiencing at least one of six cyberbullying behaviors asked about in a Pew Research Center survey conducted April 14-May 4, 2022. 1

The most commonly reported behavior in this survey is name-calling, with 32% of teens saying they have been called an offensive name online or on their cellphone. Smaller shares say they have had false rumors spread about them online (22%) or have been sent explicit images they didn’t ask for (17%).

Some 15% of teens say they have experienced someone other than a parent constantly asking them where they are, what they’re doing or who they’re with, while 10% say they have been physically threatened and 7% of teens say they have had explicit images of them shared without their consent.

In total, 28% of teens have experienced multiple types of cyberbullying.

Defining cyberbullying in this report

This report measures cyberbullying of teens using six distinct behaviors:

  • Offensive name-calling
  • Spreading of false rumors about them
  • Receiving explicit images they didn’t ask for
  • Physical threats
  • Constantly being asked where they are, what they’re doing, or who they’re with by someone other than a parent
  • Having explicit images of them shared without their consent

Teens who indicate they have personally experienced any of these behaviors online or while using their cellphone are considered targets of cyberbullying in this report. The terms “cyberbullying” and “online harassment” are used interchangeably throughout this report.

Age and gender are related to teens’ cyberbullying experiences, with older teen girls being especially likely to face this abuse

Teens’ experiences with online harassment vary by age. Some 49% of 15- to 17-year-olds have experienced at least one of the six online behaviors, compared with 42% of those ages 13 to 14. While similar shares of older and younger teens report being the target of name-calling or rumor spreading, older teens are more likely than their younger counterparts (22% vs. 11%) to say someone has sent them explicit images they didn’t ask for, an act sometimes referred to as cyberflashing ; had someone share explicit images of them without their consent, in what is also known as revenge porn (8% vs. 4%); or been the target of persistent questioning about their whereabouts and activities (17% vs. 12%).

A bar chart showing that older teen girls more likely than younger girls or boys of any age to have faced false rumor spreading, constant monitoring online, as well as cyberbullying overall

While there is no gender difference in having ever experienced online abuse, teen girls are more likely than teen boys to say false rumors have been spread about them. But further differences are seen when looking at age and gender together: 15- to 17-year-old girls stand out for being particularly likely to have faced any cyberbullying, compared with younger teen girls and teen boys of any age. Some 54% of girls ages 15 to 17 have experienced at least one of the six cyberbullying behaviors, while 44% of 15- to 17-year-old boys and 41% of boys and girls ages 13 to 14 say the same. These older teen girls are also more likely than younger teen girls and teen boys of any age to report being the target of false rumors and constant monitoring by someone other than a parent.

White, Black and Hispanic teens do not statistically differ in having ever been harassed online, but specific types of online attacks are more prevalent among certain groups. 2 For example, White teens are more likely to report being targeted by false rumors than Black teens. Hispanic teens are more likely than White or Black teens to say they have been asked constantly where they are, what they’re doing or who they’re with by someone other than a parent.

There are also differences by household income when it comes to physical threats. Teens who are from households making less than $30,000 annually are twice as likely as teens living in households making $75,000 or more a year to say they have been physically threatened online (16% vs. 8%).

A bar chart showing that older teen girls stand out for experiencing multiple types of cyberbullying behaviors

Beyond those differences related to specific harassing behaviors, older teen girls are particularly likely to say they experience multiple types of online harassment. Some 32% of teen girls have experienced two or more types of online harassment asked about in this survey, while 24% of teen boys say the same. And 15- to 17-year-olds are more likely than 13- to 14-year-olds to have been the target of multiple types of cyberbullying (32% vs. 22%).

These differences are largely driven by older teen girls: 38% of teen girls ages 15 to 17 have experienced at least two of the harassing behaviors asked about in this survey, while roughly a quarter of younger teen girls and teen boys of any age say the same.

Beyond demographic differences, being the target of these behaviors and facing multiple types of these behaviors also vary by the amount of time youth spend online. Teens who say they are online almost constantly are not only more likely to have ever been harassed online than those who report being online less often (53% vs 40%), but are also more likely to have faced multiple forms of online abuse (37% vs. 21%).

These are some of the findings from a Pew Research Center online survey of 1,316 U.S. teens conducted from April 14 to May 4, 2022.

Black teens are about twice as likely as Hispanic or White teens to say they think their race or ethnicity made them a target of online abuse

There are numerous reasons why a teen may be targeted with online abuse. This survey asked youth if they believed their physical appearance, gender, race or ethnicity, sexual orientation or political views were a factor in them being the target of abusive behavior online.

A bar chart showing that teens are more likely to think they've been harassed online because of the way they look than their politics

Teens are most likely to say their physical appearance made them the target of cyberbullying. Some 15% of all teens think they were cyberbullied because of their appearance.

About one-in-ten teens say they were targeted because of their gender (10%) or their race or ethnicity (9%). Teens less commonly report being harassed for their sexual orientation or their political views – just 5% each.

Looking at these numbers in a different way, 31% of teens who have personally experienced online harassment or bullying think they were targeted because of their physical appearance. About one-in-five cyberbullied teens say they were targeted due to their gender (22%) or their racial or ethnic background (20%). And roughly one-in-ten affected teens point to their sexual orientation (12%) or their political views (11%) as a reason why they were targeted with harassment or bullying online.

A bar chart showing that Black teens are more likely than those who are Hispanic or White to say they have been cyberbullied because of their race or ethnicity

The reasons teens cite for why they were targeted for cyberbullying are largely similar across major demographic groups, but there are a few key differences. For example, teen girls overall are more likely than teen boys to say they have been cyberbullied because of their physical appearance (17% vs. 11%) or their gender (14% vs. 6%). Older teens are also more likely to say they have been harassed online because of their appearance: 17% of 15- to 17-year-olds have experienced cyberbullying because of their physical appearance, compared with 11% of teens ages 13 to 14.

Older teen girls are particularly likely to think they have been harassed online because of their physical appearance: 21% of all 15- to 17-year-old girls think they have been targeted for this reason. This compares with about one-in-ten younger teen girls or teen boys, regardless of age, who think they have been cyberbullied because of their appearance.

A teen’s racial or ethnic background relates to whether they report having been targeted for cyberbullying because of race or ethnicity. Some 21% of Black teens report being made a target because of their race or ethnicity, compared with 11% of Hispanic teens and an even smaller share of White teens (4%).

There are no partisan differences in teens being targeted for their political views, with 5% of those who identify as either Democratic or Republican – including those who lean toward each party – saying they think their political views contributed to them being cyberbullied.

Black or Hispanic teens are more likely than White teens to say cyberbullying is a major problem for people their age

In addition to measuring teens’ own personal experiences with cyberbullying, the survey also sought to understand young people’s views about online harassment more generally.

social media cyberbullying research paper

The vast majority of teens say online harassment and online bullying are a problem for people their age, with 53% saying they are a major problem. Just 6% of teens think they are not a problem.

Certain demographic groups stand out for how much of a problem they say cyberbullying is. Seven-in-ten Black teens and 62% of Hispanic teens say online harassment and bullying are a major problem for people their age, compared with 46% of White teens. Teens from households making under $75,000 a year are similarly inclined to call this type of harassment a major problem, with 62% making this claim, compared with 47% of teens from more affluent homes. Teen girls are also more likely than boys to view cyberbullying as a major problem.

Views also vary by community type. Some 65% of teens living in urban areas say online harassment and bullying are a major problem for people their age, compared with about half of suburban and rural teens.

Partisan differences appear as well: Six-in-ten Democratic teens say this is a major problem for people their age, compared with 44% of Republican teens saying this.

Roughly three-quarters of teens or more think elected officials and social media sites aren’t adequately addressing online abuse

In recent years, there have been several initiatives and programs aimed at curtailing bad behavior online, but teens by and large view some of those behind these efforts – including social media companies and politicians – in a decidedly negative light.

A bar chart showing that large majorities of teens think social media sites and elected officials are doing an only fair to poor job addressing online harassment

According to teens, parents are doing the best of the five groups asked about in terms of addressing online harassment and online bullying, with 66% of teens saying parents are doing at least a good job, including one-in-five saying it is an excellent job. Roughly four-in-ten teens report thinking teachers (40%) or law enforcement (37%) are doing a good or excellent job addressing online abuse. A quarter of teens say social media sites are doing at least a good job addressing online harassment and cyberbullying, and just 18% say the same of elected officials. In fact, 44% of teens say elected officials have done a poor job addressing online harassment and online bullying.

Teens who have been cyberbullied are more critical of how various groups have addressed online bullying than those who haven’t

social media cyberbullying research paper

Teens who have experienced harassment or bullying online have a very different perspective on how various groups have been handling cyberbullying compared with those who have not faced this type of abuse. Some 53% of teens who have been cyberbullied say elected officials have done a poor job when it comes to addressing online harassment and online bullying, while 38% who have not undergone these experiences say the same (a 15 percentage point gap). Double-digit differences also appear between teens who have and have not been cyberbullied in their views on how law enforcement, social media sites and teachers have addressed online abuse, with teens who have been harassed or bullied online being more critical of each of these three groups. These harassed teens are also twice as likely as their peers who report no abuse to say parents have done a poor job of combatting online harassment and bullying.

Aside from these differences based on personal experience with cyberbullying, only a few differences are seen across major demographic groups. For example, Black teens express greater cynicism than White teens about how law enforcement has fared in this space: 33% of Black teens say law enforcement is doing a poor job when it comes to addressing online harassment and online bullying; 21% of White teens say the same. Hispanic teens (25%) do not differ from either group on this question.

Large majorities of teens believe permanent bans from social media and criminal charges can help reduce harassment on the platforms

Teens have varying views about possible actions that could help to curb the amount of online harassment youth encounter on social media.

A bar chart showing that half of teens think banning users who bully or criminal charges against them would help a lot in reducing the cyberbullying teens may face on social media

While a majority of teens say each of five possible solutions asked about in the survey would at least help a little, certain measures are viewed as being more effective than others.

Teens see the most benefit in criminal charges for users who bully or harass on social media or permanently locking these users out of their account. Half of teens say each of these options would help a lot in reducing the amount of harassment and bullying teens may face on social media sites.

About four-in-ten teens think that if social media companies looked for and deleted posts they think are bullying or harassing (42%) or if users of these platforms were required to use their real names and pictures (37%) it would help a lot in addressing these issues. The idea of forcing people to use their real name while online has long existed and been heavily debated: Proponents see it as a way to hold bad actors accountable and keep online conversations more civil , while detractors believe it would do little to solve harassment and could even  worsen it .

Three-in-ten teens say school districts monitoring students’ social media activity for bullying or harassment would help a lot. Some school districts already use digital monitoring software to help them identify worrying student behavior on school-owned devices , social media and other online platforms . However, these programs have been met with criticism regarding privacy issues , mixed results and whether they do more harm than good .

A chart showing that Black or Hispanic teens more optimistic than White teens about the effectiveness of five potential solutions to curb online abuse

Having personally experienced online harassment is unrelated to a teen’s view on whether these potential measures would help a lot in reducing these types of adverse experiences on social media. Views do vary widely by a teen’s racial or ethnic background, however.

Black or Hispanic teens are consistently more optimistic than White teens about the effectiveness of each of these measures.

Majorities of both Black and Hispanic teens say permanently locking users out of their account if they bully or harass others or criminal charges for users who bully or harass on social media would help a lot, while about four-in-ten White teens express each view.

In the case of permanent bans, Black teens further stand out from their Hispanic peers: Seven-in-ten say this would help a lot, followed by 59% of Hispanic teens and 42% of White teens.

  • It is important to note that there are various ways researchers measure youths’ experiences with cyberbullying and online harassment. As a result, there may be a range of estimates for how many teens report having these experiences. In addition, since the Center last polled on this topic in 2018, there have been changes in how the surveys were conducted and how the questions were asked. For instance, the 2018 survey asked about bullying by listing a number of possible behaviors and asking respondents to “check all that apply.” This survey asked teens to answer “yes” or “no” to each item individually. Due to these changes, direct comparisons cannot be made across the two surveys. ↩
  • There were not enough Asian American teen respondents in the sample to be broken out into a separate analysis. As always, their responses are incorporated into the general population figures throughout the report. ↩

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Table of contents, connection, creativity and drama: teen life on social media in 2022, teens, social media and technology 2022, online harassment occurs most often on social media, but strikes in other places, too, about one-in-five americans who have been harassed online say it was because of their religion, some americans who have been targeted by troubling behaviors online wouldn’t call it ‘harassment’, most popular.

About Pew Research Center Pew Research Center is a nonpartisan fact tank that informs the public about the issues, attitudes and trends shaping the world. It conducts public opinion polling, demographic research, media content analysis and other empirical social science research. Pew Research Center does not take policy positions. It is a subsidiary of The Pew Charitable Trusts .

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Cyberbullying detection: advanced preprocessing techniques & deep learning architecture for Roman Urdu data

  • Amirita Dewani   ORCID: orcid.org/0000-0002-3816-3644 1 ,
  • Mohsin Ali Memon   ORCID: orcid.org/0000-0003-2638-4252 1 &
  • Sania Bhatti   ORCID: orcid.org/0000-0002-0887-8083 1  

Journal of Big Data volume  8 , Article number:  160 ( 2021 ) Cite this article

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Social media have become a very viable medium for communication, collaboration, exchange of information, knowledge, and ideas. However, due to anonymity preservation, the incidents of hate speech and cyberbullying have been diversified across the globe. This intimidating problem has recently sought the attention of researchers and scholars worldwide and studies have been undertaken to formulate solution strategies for automatic detection of cyberaggression and hate speech, varying from machine learning models with vast features to more complex deep neural network models and different SN platforms. However, the existing research is directed towards mature languages and highlights a huge gap in newly embraced resource poor languages. One such language that has been recently adopted worldwide and more specifically by south Asian countries for communication on social media is Roman Urdu i-e Urdu language written using Roman scripting. To address this research gap, we have performed extensive preprocessing on Roman Urdu microtext. This typically involves formation of Roman Urdu slang- phrase dictionary and mapping slangs after tokenization. We have also eliminated cyberbullying domain specific stop words for dimensionality reduction of corpus. The unstructured data were further processed to handle encoded text formats and metadata/non-linguistic features. Furthermore, we performed extensive experiments by implementing RNN-LSTM, RNN-BiLSTM and CNN models varying epochs executions, model layers and tuning hyperparameters to analyze and uncover cyberbullying textual patterns in Roman Urdu. The efficiency and performance of models were evaluated using different metrics to present the comparative analysis. Results highlight that RNN-LSTM and RNN-BiLSTM performed best and achieved validation accuracy of 85.5 and 85% whereas F1 score was 0.7 and 0.67 respectively over aggression class.

Introduction

Cyberbullying (aka hate speech, cyberaggression and toxic speech) is a critical social problem plaguing today’s Internet users typically youth and lead to severe consequences like low self-esteem, anxiety, depression, hopelessness and in some cases causes lack of motivation to be alive, ultimately resulting in death of a victim [ 1 ]. Cyberbullying incidents can occur via various modalities. For example, it can take the form of sharing/ posting offensive video content or uploading violent images or sharing the pictures without permission of the owner etc. However, cyberbullying via textual content is far more common [ 2 ]. In Pakistan, the usage of internet, smartphones and social media has increasingly become prevalent these days and the very frequent users are youngsters. According to a report, more than 65% of all the users lie between 18 and 29, and typically women are more susceptible and unprotected. People often use offensive language, use hate speech, and become aggressive to bully celebrities, leaders, women and an individual [ 3 ]. In Pakistan, victims have reported life disturbing and annoying experiences and most of the victims are educated youngsters (age group of 21–30 years) [ 4 ]. The traffic in cyberspace has escalated significantly during covid-19 pandemic. A report “COVID 19 and Cyber Harassment”, released by DRF in 2020 highlights a great rise in the number of cyberbullying and harassment cases during the pandemic. The complaints registered with DRF’s Cyber Harassment Helpline were surged by 189% [ 5 ].

Recently, Roman Urdu language has been a contemporary trend and a viable language for communication on different social networking platforms. Urdu is national and official language of Pakistan and predominant among most communities across different regions. A survey statistic in [ 6 ] affirms that 300 million people are speaking Urdu language and approximately 11 million speakers are in Pakistan from which maximum users switched to Roman Urdu language for the textual communication, typically on social media. It is linguistically rich and morphologically complex language [ 7 ]. Roman Urdu language is highly variant with respect to word structures, writing styles, irregularities, and grammatical compositions. It is deficit of standard lexicon and available resources and hence become extremely challenging when performing NLP tasks.

An elaboration of script of Urdu instances and Roman Urdu is given in Table 1 . Instances highlighted are describing anti-social behavior.

This paper addresses toxicity/cyberbullying detection problem in Roman Urdu language using deep learning techniques and advanced preprocessing methods including usage of lexicons/resource that are typically developed to accomplish this work. Intricacies in analyzing the structure and patterns behind these typical aggressive behaviors, typically in a newly adopted language, and forming it as a comprehensive computational task is very complicated. The major contributions of this study are formation of a slang and contraction mapping procedure along with slang lexicon for Roman Urdu language and development of hybrid deep neural network models to capture complex aggression and bullying patterns.

The rest of the paper is organized as follows: Review of existing literature is presented in " Related Work " Section. " Problem statement " Section states research gap and gives formal definition of the addressed problem. " Methodology " Section describes the steps of research methodology and techniques and models used for the experimentations. Advanced preprocessing steps applied on Roman Urdu data are elaborated in " Data Preprocessing on Roman Urdu microtext " section. Implementation of proposed model architecture and hyperparameter settings are discussed in " Experimental Setup " section. " Results and Discussion " Section highlights and discusses study results and finally " Conlusion " Section concludes the research work and provides future research directions.

Related work

Due to the accretion of social media communication and adverse effects arising from its darker side on users, the field of automatic cyberbullying detection has become an emerging and evolving research trend [ 8 ]. Research work in [ 9 ] presents cyberbullying detection algorithm for textual data in English language. It is considered as one of the pioneers and highly cited research. They divided the task in text-classification sub problems related to sensitive topics and collected 4500 textual comments on controversial YouTube videos. This study implemented Naive Bayes, SVM and J48 binary and multiclass classifiers using general and specific feature sets. Study contributed in [ 10 ] applied deep learning architectures on Kaggle dataset and conducted experimental analysis to determine the effectiveness and performance of deep learning algorithms LSTM, BiLSTM, RNN and GRU in detecting antisocial behavior. Authors in [ 11 ] extracted data from four platforms i-e Twitter, YouTube, Wikipedia, and Reddit for developing an online hate classifier in English language using different classification techniques. Research carried out in [ 12 ] developed an automated approach to detect toxicity and unethical behavior in online communication using word embeddings and varying neural network layers. They suggested that LSTM layers and mimicked word embedding can uncover such behavior with good accuracy level.

Few of the studies in recent years has been contributed by researchers on other languages apart from English. Research work in [ 13 ] is unique and has gathered textual data from Instagram and twitter in Turkish language. They have implemented Naïve Bayes Multinomial, SVM, KNN and decision trees for cyberbullying detection along with Chi-square and information gain (IG) for feature selection. Work accomplished in [ 14 ] also addresses the problem of cyberaggression in Turkish language. The work extends comparison of different machine learning algorithms and found optimal results using Light Gradient Boosting Model. Van Hee, Cynthia, et al. in [ 15 ] proposed cyberbullying detection scheme for Dutch language. This is the first study on Dutch social media. Data was collected from ASKfm where users can ask and answer questions. The research uses default parameter settings for un-optimized linear kernel SVM based on n-grams and keyword system to identify bullying traces. F1 score for Dutch language was 61%. Problem of Arabic language cyberbullying detection was addressed and accomplished in [ 16 ]. This study used Dataiku DSS and WEKA for ML tasks. The data was scrapped from facebook and twitter. The study concluded that even though the detection approach was not comparable with the other studies in English language but overall Naive Bayes and SVM yield reasonable performance. Research work in [ 17 ] by Gomez-Adorno, Helena, et al. proposed automatic aggression detection for Spanish tweets. Several types of n-grams and linguistically motivated patterns were used but the best run could only achieve F1 score of 42.85%. Studies presented in [ 18 , 19 , 20 ] are based on automatic detection of cyberbullying content in German language. Research conducted in [ 18 ] proposed an approach based on SVM, CNN and ensemble model using unigram, bigrams and character N-grams for categorizing offensive tweets in German language. Research presented in [ 21 ] attempted for the very first time to identify bullying traces in Indonesian language. Association Rule mining and FP growth text mining were used to identify trends for bullying patterns in Jakarta and Surabaya cities using social media text. This baseline study on Indonesian language was further extended by Nurrahmi, Hani et al. in [ 22 ]. Study in [ 23 ] made first attempt to develop a corpus of code-mixed data considering Hindi and English language. They proposed a scheme for hate speech detection using N-grams and lexical features. An ensemble approach by combining the predictions of Convolutional Neural Network (CNN) and SVM algorithms were used for identifying such patterns. The weighted F1 score for Hindi language ranged between 0.37 and 0.55 for different experiments [ 24 ]. In the year 2019, Association for computational linguistics initiated the project for automatic detection of cyberbullying in Polish language [ 25 ]. Research conducted in [ 26 ] attempted to uncover cyberbullying patterns in Bengali language implementing passive aggressive, SVM and logistic regression. The optimum accuracy achieved was 78.1%. Recently, work contributed in [ 27 ] presented first study in Roman Urdu using lexicon based approach. The dataset was highly skewed comprising of only 2.2% toxic data. As according to [ 28 ], biased sampling and measurement errors are highly prone to classification errors when working on such datasets. Moreover, pattern detection based on predefined bullying and non-bullying lexicons were shortcomings of this study.

For automated detection of complex cyberbullying patterns, studies contributed by different scholars employ supervised, unsupervised, hybrid and deep learning models, vast feature engineering techniques, corpora, and social media platforms. However, the existing literature is mainly oriented towards unstructured data in English language. Some recent studies and projects have been initiated in other languages as discussed previously. To the best of our knowledge and literature review, no detailed work has been contributed in Roman Urdu to systematically analyze cyberbullying detection phenomenon using advanced preprocessing techniques (involving the usage of Roman Urdu resources) and deep learning approaches under different configurations.

Problem statement

The escalated usage of social networking sites and freedom of speech has given optimal ground to individuals across all demographics for cyberbullying and cyberaggression. This leaves drastic and noticeable impacts on behavior of a victim, ranging from disturbance in emotional wellbeing and isolation from society to more severe and deadly consequences [ 29 ]. Automatic Cyberbullying detection has remained very challenging task since social media content is in natural language and is usually posted in unstructured free-text form leaving behind the language norms, rules, and standards. Evidently, there exists a substantial number of research studies which primarily focus on discovering cyberbullying textual patterns over diverse social media platforms as discussed previously in literature review section. However, most of the detection schemes and automated approaches formulated are for resource-rich and mature languages spoken worldwide. Roman Urdu is typically spoken in South Asia and is a highly resource deficient language. Hence this research puts novel efforts to propose data pre-processing techniques on Roman Urdu scripting and develop deep learning-based hybrid models for automated cyberbullying detection in Roman Urdu language. The outcomes of this study, if implemented, will assist cybercrime centers and investigation agencies for monitoring social media contents and in making cyberspace secure and safer place for all segments of society.

Methodology

The research methodology is depicted in Fig.  1 .

figure 1

Proposed research methodology

The development of hate speech/cyberbullying corpus with minor skew and automated development of domain specific roman Urdu stop words is published in our previous work [ 30 ]. The work details formation of computational linguist resources. Further steps of methodology are discussed in subsequent sections. The Deep Neural Network (DNN) based techniques and models used for the experimentations are detailed below.

Model description

Recurrent neural networks (rnn).

RNN [ 31 ] has been applied in literature for successive time series applications with temporal dependencies. An unfolded RNN can handle processing of current data by utilizing past data. Meanwhile, RNN has the issue of training long-term dependencies. This has been addressed by one of the RNN variant.

Long short-term memory networks (LSTM)

LSTM has been employed as an advanced version of RNN network. It resolves the shortcoming of RNN by applying memory cells also known as hidden layer units. Memory cells are controlled through three gates named as: input gate, output gate and forget gate. They have the self-connections which store the temporal state of network [ 31 ]. Input and output gates address and control the flow of information from memory cell input and output to rest of the network. The forget gate, usually called as a remember vector, transfers the information with higher weights from previous neuron to the next neuron. The forget gate is added to the memory cell. The information resides in memory depending upon the high activation results; the information will be stored in memory cell iff the input unit has high activation. However, the information will be transferred to next neuron if the output unit has high activation. Otherwise, input information with high weights resides in memory cell [ 31 ].

Mathematically, LSTM network can be described as [ 32 ]:

where W h   ∈  R m × d and U h   ∈  R m × m indicates weight matrices, x t denotes the current word embedding, b h   ∈  R m refers to bias term, whereas f(x ) is a non-linear function.

LSTM has more complex architecture including hidden states and tends to remember information for either short or long term. The hidden state [ 33 ] of LSTM is computed as follows:

where f t denotes the forget gate, i t refers to the input gate, c t denotes the cell state, o t is the output gate, h t is the regular hidden state, σ indicates sigmoid function, and ◦ is the Hadamard product.

Bidirectional Long short-term memory networks (BiLSTM)

In the traditional recurrent neural network model and LSTM model, the propagation of information is only in forward direction. This results in computation of an output vector only based on the current input at time t and the output of the previous unit. The back propagation of information in network is achieved by merging two bidirectional recurrent neural network (BiRNN) and LSTM units, one for forward direction and one for backward direction. This helps in capturing contextual information and enhances learning ability [ 34 ].

In bidirectional LSTM, outputs of two LSTM networks are stacked together. The first LSTM is a regular sequence starting from the starting of the paragraph, while the second LSTM is a standard sequence, and the series of inputs are fed in the opposite order. The first hidden state is denoted by ht forward whereas second LSTM unit’s hidden state is denoted by ht backward . After processing data, the final state ht Bilstm is computed by concatenating the two hidden states as given in Eq.  3 .

where  ⊕  denotes a concatenation operator.

Convolutional neural networks (CNN)

Convolution neural networks (aka CNN), originally incorporated for image processing tasks, have become very efficacious in different NLP and text classification applications. The network identifies correlations and patterns of data via their feature maps. Information about local structure of data is extracted by applying multiple filters with different dimensions [ 35 ].

Data preprocessing on Roman Urdu microtext

Big social media data in Roman Urdu language is inconsistent, incomplete, or precise, missing in certain behaviors or trends, and is likely to incorporate many errors. Roman Urdu users highly deviate language norms while communicating on social media. Hence data preprocessing is immensely significant. Some major data preprocessing steps applied on Roman Urdu microtext are detailed below.

Handling Unicode and encoded text formats

Unicode scheme provides every character in natural language text a unique code from 0 to 0 × 10FFFF. The uncleaned Roman Urdu data comprised of special symbols, emojis, and other typical stray characters represented using Unicode. We used Unicode transformation type 8 encoding to convert the data. This data was converted and handled using re and string modules in python.

Text cleaning

Text cleaning is essential step to eliminate or at least reduce noise from Microtext. This step comprised of case transformation, removal of punctuations and URLs, elimination of additional white spaces, exclusion of hashtags, digits & special character removal and removal of metadata/non-linguistic features.

Tokenization

Tokenization is immensely essential phase of text processing. It is the process of generating tokens by splitting textual content into words, phrases, or other meaningful parts. It is generally a form of text segmentation [ 36 ]. Tokenization was performed using Keras tokenizer to prepare the text for implementing deep learning networks.

Filtering stop words

Stop words are non-semantic division of text in natural language. The necessity that stop words should be eliminated from text is that they make the text higher dimensional with redundant features which are less significant for analysts. Removing stop words reduces the dimensionality of term space [ 37 ]. Development of domain specific stop words in Roman Urdu language automatically using statistical techniques and bilingual experts’ input, comprising of 173 words is detailed in our previous work [ 30 ]. Insignificant Roman Urdu words were typically articles such as ek (ایک), conjunctions and pronouns such as tum (تم), tumhara (تمھارا), us (اُس), wo/who (وہ), usko (اُسکو), preposition such as main (میں), pe (پے), par (پر), demonstratives such as ye (یے), inko (انکو), yahan(یہاں), and interrogatives such as kahan (کہاں), kab (کب), kisko (کس کو), kiski (کس کی) etc. Stop words were removed from Roman Urdu corpus, leaving behind the index terms which are important.

Mapping slangs and contractions

Existing libraries, APIs and toolkits in python language primarily support preprocessing functions for English and other mature languages. They can be partially used for Roman Urdu language. Moreover, most of the communication in Roman Urdu comprises of bully terms being used as slangs. High dimensional textual data also suppress significant features. Hence contraction mapping is mandatory for dimensionality reduction and to capture complex bullying patterns. Currently, Pycontractions Library [ 38 ] only supports English contraction mapping process. To address this problem, the study developed data slang mapping process. To map slangs to original terms and phrases in Roman Urdu language, we created Slang lexicon in Roman Urdu (SLRU) which also included Roman Urdu abuses and offensive terms used as a norm by Roman Urdu users. SLRU is in the form of a dictionary. It comprises of the key: value pairs, where key is the slang and value is its equivalent Roman Urdu phrase/term such as “AFIK”: “Jahan tak mujhay pata hai”, ASAP: “Jitna jaldi ho sakay”, “tbh”: “Sach main” and so on. The process of slang mapping is detailed in Fig.  2 .

figure 2

Mapping process for slangs in Roman Urdu

The results of mapping process are highlighted in Fig.  3 .

figure 3

Mapping on Roman Urdu Data

Experimental setup

This section discusses implementation of proposed neural network architecture and all hyper-parameter settings. All the experiments were performed on 11 Gen, core i7, 4 cores, 8 logical microprocessors, with 2.8 GHz processor speed, 256 GB Solid State Drive and python version 3.8, 64 bits.

Proposed model implementation and hyper-parameter settings

All models were implemented and trained in Keras; a high-level neural network API that works with open-source machine learning framework called TensorFlow [ 39 ]. All the implementation was accomplished using PyCharm. The optimal parameters and results were achieved through repeated experimentations.

Data was split into training and testing datasets. The data split was 0.8 for training and 0.2 for testing i-e 80% of data instances were used for training and 20% were holdout for testing and validation purpose. The sets were made using shuffled array. This allows model to learn over different data instances. Moreover, it helps to uncover reliability of model and consistency of results over repeated executions. Random state is also generated using numpy.random [ 40 ] for random sampling during splitting of data to ensure reproducible splits.

Textual input data must be integer encoded. In RNN-LSTM architecture, a sequential model was created. Initially an Embedding layer was added to the network and textual Roman Urdu data was provided as an input. Embedding layer embedded high dimensional text data in low dimensional vector space for generating dense vector representation of data. Embedding was formed using 2000 features and 128 embed dimensions. The experiment was initially executed on 20 epochs and 50 batch size. The batch size was based on the fact that model was having single lstm layer, and comparatively took lower training and validation time per step. The execution time for each epoch was approximately 10 ms. SpatialDropout1D was used with rate of 0.3. It helped to regularize the activations and maintain effective learning rate of the model. For updating network weights iteratively, this work uses binary cross entropy loss function and Adam optimizer. Sigmoid activation function was also implemented. It is denoted by f(x) and is defined as:

The Spatial Dropout layer was implemented instead of a simple Dropout. The major reason being was to retain the context of textual data established by neighboring words. Dropping random words (except for stop words, which were already removed during preprocessing step) can highly affect the context of uttered sentences and ultimately the performance of model. We incorporated two hidden dense layers denoted by D 1 and D 2 . The output of each hidden layer was computed to get the final output for cyberbullying text detection.

Keras tokenizer was used to accomplish pre-tokenization of all the data required for the implementation of RNN-biLSTM model. We created a sequential model with Embedding layer having 2000 maximum features. Subsequently a biLSTM layer comprising of two LSTM layers, one to read sequence in forward direction and other in backward direction, each with 64 units was added. Hidden layer (H 1 ) was formed using sigmoid activation function. For down sampling the feature maps, Dropout layer was added with 0.2 dropout rate. Moreover, we used 128 batch size to utilize low to moderate computational resources while still not slowing down the training process. Batch size highlights number of samples processed by model before updating of internal parameters. To combat overfitting, we added second dropout layer with rate of 0.25. Adam optimization was used with learning rate of 0.01 since batch size was not too small. For this model, we used binary cross entropy loss function. As the Epochs increase, the generalization ability of the model improves. However, too many epochs also lead to the problem of overfitting. The model was executed over different number of Epochs and average execution time for each epoch was 13 to 15 ms. The performance of model stabilized over 20 Epochs, above which the improvement was almost negligible.

In CNN model, initially the sentence was transformed into matrix where each row of matrix represented word vectors representation of data. We used 1000 features and 32 dimensions. Two convolutional filters were applied with 8 and 16 filters and 3 kernel size. Each filter was used to perform one dimensional convolution on word embeddings. Both Layers were 1D in nature. We set two dropout layers with dropout rate of 0.25 to improve generalization ability of developed model. Hidden layers with Relu and sigmoid activation functions were used. To extract most salient and prominent features, global maximum pooling layers were used with pool size = 2. Flatten layer was created after convolutional layers to flatten the output of the previous layer to a single long feature vector. The experiment was simulated over different Epochs. However, results got stable at 30 epochs.

Results and discussion

Empirical evaluation of cyberbullying detection scheme performance in Roman Urdu and experimental setups is accomplished via accuracy, precision, recall, and f1 measure metrics.

All the implemented models were executed several times over number of epochs to get consistency in evaluation parameters until it was a minor difference of ± 0.1. The results for LSTM are depicted in Fig.  4 . To ensure results validity and reliability, for a comparatively less skewed dataset, F1 measure (i-e a harmonic mean of precision and recall) is used as an evaluation metric. Furthermore, we have also reported macro and weighted average scores across all the classes. The evaluation results of RNN-LSTM are given in Fig.  4 .

figure 4

RNN-LSTM evaluation Results

F1 score for RNN-LSTM over cyberbullying class was only 70%, however for non-cyberbullying class, score was 90%. We observed that nearly all the instances of majority class of non-cyber bullying are correctly classified by this model. The experimental simulation depicting model accuracy and validation accuracy during training and validation phases, before and after stabilization of evaluation parameters is represented in Figs.  5 and 6 respectively.

figure 5

RNN-LSTM Model accuracy graph for 20 epochs

figure 6

RNN-LSTM Model accuracy graph for 50 epochs

The accuracy improved over subsequent epochs. However, after 20 epochs it got stabilized. The average accuracy produced by this model was 93.5% during training and 85.5% during validation. Overall curve variation is indicating that no overfitting problem arise. The model loss during training and validation loss during testing over 20 and 50 epochs is shown in Figs.  7 and 8 respectively. The cross-entropy loss considered during configuration over different epochs converged well, thus indicating optimal model performance.

figure 7

RNN-LSTM Model loss plot- Binary Cross entropy for 20 epochs

figure 8

RNN-LSTM Model loss plot- Binary Cross entropy for 50 epochs

The evaluation results of RNN-BILSTM model over 20 epochs are given in Fig.  9 .

figure 9

RNN-BiLSTM evaluation Results

RNN-LSTM also performed reasonably well for cyberbullying detection task on Roman Urdu data. F1 score for non-cyberbullying content prediction was 90% whereas for cyberbullying content, the score was 67% only. This indicates that model erroneously classified/misclassified some of the aggressive class instances and TN rate was at average. Figs.  10 and 11 are depicting model accuracy and validation accuracy for RNN-BiLSTM.

figure 10

RNN-biLSTM Model accuracy plot for 20 epochs

figure 11

RNN-biLSTM Model accuracy plot for 50 epochs

The accuracy improved highly during training process up to 20 epochs. Overall average accuracy was 97% in training and 85% on validation set. 20% of the data was used for as a validation set, as stated earlier. During experimentation, we identified that accuracy of our model is not improving after a specific point i-e after 20 Epochs. The trivial variations can be clearly visualized from the graph in Fig.  9 . Model loss and validation loss during training and testing process for RNN-BiLSTM over 20 and 50 epochs is given in Figs.  12 and 13 respectively.

figure 12

RNN-BiLSTM Model loss plot- Binary Cross entropy for 20 epochs

figure 13

RNN-BiLSTM Model loss plot- Binary Cross entropy for 50 epochs

The cross-entropy loss was minimal (approximately 1.2), indicating good prediction capability of developed model.

Figure  14 represents the evaluation results for CNN model.

figure 14

CNN model evaluation Results

CNN performed well for prediction of non-cyberbullying content, providing F1 score of 87%. However, model did not yield good efficiency for categorizing cyberbullying class, producing f1-score of 52%. The repeated experiments performed for CNN showed continuous improvements up to 30 Epochs. Figure  15 depicts model accuracy and validation accuracy. The experimental simulation over 50 epochs only shown minor improvements as represented in Fig.  16 . The average execution time for Epoch was 9 ms each. The training accuracy of 98% was achieved over different executions whereas model produced 85% validation accuracy.

figure 15

CNN Model accuracy plot for 30 epochs

figure 16

CNN Model accuracy plot for 50 epochs

CNN model loss and validation loss results at 30 and 50 epochs are presented in Fig.  17 and 18 respectively. The loss was minimal during training and converged. During validation the loss increased and diverged indicating only moderate performance over unseen instances typically from aggressive class.

figure 17

CNN Model loss plot- Binary Cross entropy for 30 epochs

figure 18

CNN Model loss plot- Binary Cross entropy for 50 epochs

The compiled model results indicating evaluation measures at stabilized epochs are depicted in Table 2 .

Cyberbullying has become an alarming social threat for today’s youth and has recently gained huge attention from research community. This research has addressed the problem of cyberbullying detection in Roman Urdu Language. Since Roman Urdu is highly resource deficient language, having different writing patterns, word structures, and irregularities thus making this work a challenging task. In this work we have presented advanced preprocessing techniques mainly a slang mapping mechanism, domain specific stop word removal, handling encoded formats and formulation of deep learning architecture to detect cyberbullying patterns in Roman Urdu language. We created experiments with vast parameters to build optimal classifier for cyberbullying tweets. The results highlighted that RNN-LSTM and RNN-BiLSTM with concatenation of forward and backward units provided better performance in 20 Epochs as compared to CNN. The existing work can be extended in numerous ways. The future studies can focus on development of ensemble models to uncover harassing and hate speech patterns. Moreover, the incorporation of context-specific features and handling of morphological variations might produce better results.

Availability of data and materials

The used raw dataset in this research is not publicly available. The data that support the findings of this research work are available from the corresponding author, on valid request due to privacy and ethical restrictions.

Abbreviations

Recurrent neural network

Long-short term memory

Bidirectional long-short term memory

Convolutional neural network

Social networking

Right to left

Left to right

True negative

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Acknowledgements

We would like to thank Institute of Information and Communication Technology, Mehran University of Engineering & Technology, for providing resources and funding, necessary to accomplish this research work.

This research has been performed at Institute of Information and Communication Technology, Mehran University of Engineering and Technology, Pakistan and is fully funded under MUET funds for postgraduate students.

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Authors and affiliations.

Institute of Information and Communication Technologies, Department of Software Engineering, Mehran University of Engineering & Technology, Jamshoro, Sindh, Pakistan

Amirita Dewani, Mohsin Ali Memon & Sania Bhatti

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Dewani, A., Memon, M.A. & Bhatti, S. Cyberbullying detection: advanced preprocessing techniques & deep learning architecture for Roman Urdu data. J Big Data 8 , 160 (2021). https://doi.org/10.1186/s40537-021-00550-7

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  • Advanced preprocessing
  • Deep learning
  • Hate speech detection
  • Cyberbullying

social media cyberbullying research paper

SYSTEMATIC REVIEW article

Cyberbullying among adolescents and children: a comprehensive review of the global situation, risk factors, and preventive measures.

\nChengyan Zhu&#x;

  • 1 School of Political Science and Public Administration, Wuhan University, Wuhan, China
  • 2 School of Medicine and Health Management, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
  • 3 College of Engineering, Design and Physical Sciences, Brunel University London, Uxbridge, United Kingdom

Background: Cyberbullying is well-recognized as a severe public health issue which affects both adolescents and children. Most extant studies have focused on national and regional effects of cyberbullying, with few examining the global perspective of cyberbullying. This systematic review comprehensively examines the global situation, risk factors, and preventive measures taken worldwide to fight cyberbullying among adolescents and children.

Methods: A systematic review of available literature was completed following PRISMA guidelines using the search themes “cyberbullying” and “adolescent or children”; the time frame was from January 1st, 2015 to December 31st, 2019. Eight academic databases pertaining to public health, and communication and psychology were consulted, namely: Web of Science, Science Direct, PubMed, Google Scholar, ProQuest, Communication & Mass Media Complete, CINAHL, and PsycArticles. Additional records identified through other sources included the references of reviews and two websites, Cyberbullying Research Center and United Nations Children's Fund. A total of 63 studies out of 2070 were included in our final review focusing on cyberbullying prevalence and risk factors.

Results: The prevalence rates of cyberbullying preparation ranged from 6.0 to 46.3%, while the rates of cyberbullying victimization ranged from 13.99 to 57.5%, based on 63 references. Verbal violence was the most common type of cyberbullying. Fourteen risk factors and three protective factors were revealed in this study. At the personal level, variables associated with cyberbullying including age, gender, online behavior, race, health condition, past experience of victimization, and impulsiveness were reviewed as risk factors. Likewise, at the situational level, parent-child relationship, interpersonal relationships, and geographical location were also reviewed in relation to cyberbullying. As for protective factors, empathy and emotional intelligence, parent-child relationship, and school climate were frequently mentioned.

Conclusion: The prevalence rate of cyberbullying has increased significantly in the observed 5-year period, and it is imperative that researchers from low and middle income countries focus sufficient attention on cyberbullying of children and adolescents. Despite a lack of scientific intervention research on cyberbullying, the review also identified several promising strategies for its prevention from the perspectives of youths, parents and schools. More research on cyberbullying is needed, especially on the issue of cross-national cyberbullying. International cooperation, multi-pronged and systematic approaches are highly encouraged to deal with cyberbullying.

Introduction

Childhood and adolescence are not only periods of growth, but also of emerging risk taking. Young people during these periods are particularly vulnerable and cannot fully understand the connection between behaviors and consequences ( 1 ). With peer pressures, the heat of passion, children and adolescents usually perform worse than adults when people are required to maintain self-discipline to achieve good results in unfamiliar situations. Impulsiveness, sensation seeking, thrill seeking, and other individual differences cause adolescents to risk rejecting standardized risk interventions ( 2 ).

About one-third of Internet users in the world are children and adolescents under the age of 18 ( 3 ). Digital technology provide a new form of interpersonal communication ( 4 ). However, surveys and news reports also show another picture in the Internet Age. The dark side of young people's internet usage is that they may bully or suffer from others' bullying in cyberspace. This behavior is also acknowledged as cyberbullying ( 5 ). Based on Olweus's definition, cyberbullying is usually regarded as bullying implemented through electronic media ( 6 , 7 ). Specifically, cyberbullying among children and adolescents can be summarized as the intentional and repeated harm from one or more peers that occurs in cyberspace caused by the use of computers, smartphones and other devices ( 4 , 8 – 12 ). In recent years, new forms of cyberbullying behaviors have emerged, such as cyberstalking and online dating abuse ( 13 – 15 ).

Although cyberbullying is still a relatively new field of research, cyberbullying among adolescents is considered to be a serious public health issue that is closely related to adolescents' behavior, mental health and development ( 16 , 17 ). The increasing rate of Internet adoption worldwide and the popularity of social media platforms among the young people have worsened this situation with most children and adolescents experiencing cyberbullying or online victimization during their lives. The confines of space and time are alleviated for bullies in virtual environments, creating new venues for cyberbullying with no geographical boundaries ( 6 ). Cyberbullying exerts negative effects on many aspects of young people's lives, including personal privacy invasion and psychological disorders. The influence of cyberbullying may be worse than traditional bullying as perpetrators can act anonymously and connect easily with children and adolescents at any time ( 18 ). In comparison with traditional victims, those bullied online show greater levels of depression, anxiety and loneliness ( 19 ). Self-esteem problems and school absenteeism have also proven to be related to cyberbullying ( 20 ).

Due to changes in use and behavioral patterns among the youth on social media, the manifestations and risk factors of cyberbullying have faced significant transformation. Further, as the boundaries of cyberbullying are not limited by geography, cyberbullying may not be a problem contained within a single country. In this sense, cyberbullying is a global problem and tackling it requires greater international collaboration. The adverse effects caused by cyberbullying, including reduced safety, lower educational attainment, poorer mental health and greater unhappiness, led UNICEF to state that “no child is absolutely safe in the digital world” ( 3 ).

Extant research has examined the prevalence and risk factors of cyberbullying to unravel the complexity of cyberbullying across different countries and their corresponding causes. However, due to variations in cyberbullying measurement and methodologies, no consistent conclusions have been drawn ( 21 ). Studies into inconsistencies in prevalence rates of cyberbullying, measured in the same country during the same time period, occur frequently. Selkie et al. systematically reviewed cyberbullying among American middle and high school students aged 10–19 years old in 2015, and revealed that the prevalence of cyberbullying victimization ranged from 3 to 72%, while perpetration ranged from 1 to 41% ( 22 ). Risk and protective factors have also been broadly studied, but confirmation is still needed of those factors which have more significant effects on cyberbullying among young people. Clarification of these issues would be useful to allow further research to recognize cyberbullying more accurately.

This review aims to extend prior contributions and provide a comprehensive review of cyberbullying of children and adolescents from a global perspective, with the focus being on prevalence, associated risk factors and protective factors across countries. It is necessary to provide a global panorama based on research syntheses to fill the gaps in knowledge on this topic.

Search Strategies

This study strictly employed Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We consulted eight academic databases pertaining to public health, and communication and psychology, namely: Web of Science, Science Direct, PubMed, Google Scholar, ProQuest, Communication & Mass Media Complete, CINAHL, and PsycArticles. Additional records identified through other sources included the references of reviews and two websites, Cyberbullying Research Center and United Nations Children's Fund. With regard to the duration of our review, since most studies on cyberbullying arose around 2015 ( 9 , 21 ), this study highlights the complementary aspects of the available information about cyberbullying during the recent 5 year period from January 1st, 2015 to December 31st, 2019.

One researcher extracted keywords and two researchers proposed modifications. We used two sets of subject terms to review articles, “cyberbullying” and “child OR adolescent.” Some keywords that refer to cyberbullying behaviors and young people are also included, such as threat, harass, intimidate, abuse, insult, humiliate, condemn, isolate, embarrass, forgery, slander, flame, stalk, manhunt, as well as teen, youth, young people and student. The search formula is (cyberbullying OR cyber-bullying OR cyber-aggression OR ((cyber OR online OR electronic OR Internet) AND (bully * OR aggres * OR violence OR perpetrat * OR victim * OR threat * OR harass * OR intimidat * OR * OR insult * OR humiliate * OR condemn * OR isolate * OR embarrass * OR forgery OR slander * OR flame OR stalk * OR manhunt))) AND (adolescen * OR child OR children OR teen? OR teenager? OR youth? OR “young people” OR “elementary school student * ” OR “middle school student * ” OR “high school student * ”). The main search approach is title search. Search strategies varied according to the database consulted, and we did not limit the type of literature for inclusion. Journals, conference papers and dissertations are all available.

Specifically, the inclusion criteria for our study were as follows: (a). reported or evaluated the prevalence and possible risk factors associated with cyberbullying, (b). respondents were students under the age of 18 or in primary, junior or senior high schools, and (c). studies were written in English. Exclusion criteria were: (a). respondents came from specific groups, such as clinical samples, children with disabilities, sexual minorities, specific ethnic groups, specific faith groups or samples with cross-national background, (b). review studies, qualitative studies, conceptual studies, book reviews, news reports or abstracts of meetings, and (c). studies focused solely on preventive measures that were usually meta-analytic and qualitative in nature. Figure 1 presents the details of the employed screening process, showing that a total of 63 studies out of 2070 were included in our final review.

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Figure 1 . PRISMA flow chart diagram showing the process of study selection for inclusion in the systematic review on children and adolescents cyberbullying.

Meta-analysis was not conducted as the limited research published within the 5 years revealed little research which reported odds ratio. On the other hand, due to the inconsistency of concepts, measuring instruments and recall periods, considerable variation could be found in research quality ( 23 ). Meta-analysis is not a preferred method.

Coding Scheme

For coding, we created a comprehensive code scheme to include the characteristics. For cyberbullying, we coded five types proposed by Willard ( 24 – 26 ), which included verbal violence, group violence, visual violence, impersonating and account forgery, and other behaviors. Among them, verbal violence is considered one of the most common types of cyberbullying and refers to the behavior of offensive responses, insults, mocking, threats, slander, and harassment. Group violence is associated with preventing others from joining certain groups or isolating others, forcing others to leave the group. Visual violence relates to the release and sharing of embarrassing photos and information without the owners' consent. Impersonating and account forgery refers to identity theft, stealing passwords, violating accounts and the creation of fake accounts to fraudulently present the behavior of others. Other behaviors include disclosure of privacy, sexual harassment, and cyberstalking. To comprehensively examine cyberbullying, we coded cyberbullying behaviors from both the perspectives of cyberbullying perpetrators and victims, if mentioned in the studies.

In relation to risk factors, we drew insights from the general aggression model, which contributes to the understanding of personal and situational factors in the cyberbullying of children and adolescents. We chose the general aggression model because (a) it contains more situational factors than other models (e.g., social ecological models) - such as school climate ( 9 ), and (b) we believe that the general aggression model is more suitable for helping researchers conduct a systematic review of cyberbullying risk and protective factors. This model provides a comprehensive framework that integrates domain specific theories of aggression, and has been widely applied in cyberbullying research ( 27 ). For instance, Kowalski and colleagues proposed a cyberbullying encounter through the general aggression model to understand the formation and development process of youth cyberbullying related to both victimization and perpetration ( 9 ). Victims and perpetrators enter the cyberbullying encounter with various individual characteristics, experiences, attitudes, desires, personalities, and motives that intersect to determine the course of the interaction. Correspondingly, the antecedents pertaining to cyberbullying are divided into two broad categories, personal factors and situational factors. Personal factors refer to individual characteristics, such as gender, age, motivation, personality, psychological states, socioeconomic status and technology use, values and perceptions, and other maladaptive behaviors. Situational factors focus on the provocation/support, parental involvement, school climate, and perceived anonymity. Consequently, our coders related to risk factors consisting of personal factors and situational factors from the perspectives of both cyberbullying perpetrators and victims.

We extracted information relating to individual papers and sample characteristics, including authors, year of publication, country, article type, sampling procedures, sample characteristics, measures of cyberbullying, and prevalence and risk factors from both cyberbullying perpetration and victimization perspectives. The key words extraction and coding work were performed twice by two trained research assistants in health informatics. The consistency test results are as follows: the Kappa value with “personal factors” was 0.932, and the Kappa value with “situational factors” was 0.807. The result shows that the coding consistency was high enough and acceptable. Disagreements were resolved through discussion with other authors.

Quality Assessment of Studies

The quality assessment of the studies is based on the recommended tool for assessing risk of bias, Cochrane Collaboration. This quality assessment tool focused on seven items: random sequence generation, allocation concealment, blinding of participants and personnel, blinding of outcome assessment, incomplete outcome data, selective reporting, and other sources of bias ( 28 ). We assessed each item as “low risk,” “high risk,” and “unclear” for included studies. A study is considered of “high quality” when it meets three or more “low risk” requirements. When one or more main flaw of a study may affect the research results, the study is considered as “low quality.” When a lack of information leads to a difficult judgement, the quality is considered to be “unclear.” Please refer to Appendix 1 for more details.

This comprehensive systematic review comprised a total of 63 studies. Appendices 2 , 3 show the descriptive information of the studies included. Among them, 58 (92%) studies measured two or more cyberbullying behavior types. The sample sizes of the youths range from several hundred to tens of thousands, with one thousand to five thousand being the most common. As for study distribution, the United States of America, Spain and China were most frequently mentioned. Table 1 presents the detail.

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Table 1 . Descriptive information of studies included (2015–2019).

Prevalence of Global Cyberbullying

Prevalence across countries.

Among the 63 studies included, 22 studies reported on cyberbullying prevalence and 20 studies reported on prevalence from victimization and perpetration perspectives, respectively. Among the 20 studies, 11 national studies indicated that the prevalence of cyberbullying victimization and cyberbullying perpetration ranged from 14.6 to 52.2% and 6.3 to 32%, respectively. These studies were conducted in the United States of America ( N = 4) ( 29 – 32 ), South Korea ( N = 3) ( 33 – 35 ), Singapore ( N = 1) ( 36 ), Malaysia ( N = 1) ( 37 ), Israel ( N = 1) ( 38 ), and Canada ( N = 1) ( 39 ). Only one of these 11 national studies is from an upper middle income country, and the rest are from highincome countries identified by the World Bank ( 40 ). By combining regional and community-level studies, the prevalence of cyberbullying victimization and cyberbullying perpetration ranged from 13.99 to 57.5% and 6.0 to 46.3%, respectively. Spain reported the highest prevalence of cyberbullying victimization (57.5%) ( 41 ), followed by Malaysia (52.2%) ( 37 ), Israel (45%) ( 42 ), and China (44.5%) ( 43 ). The lowest reported victim rates were observed in Canada (13.99%) and South Korea (14.6%) ( 34 , 39 ). The reported prevalence of cyberbullying victimization in the United States of America ranged from 15.5 to 31.4% ( 29 , 44 ), while in Israel, rates ranged from 30 to 45% ( 26 , 42 ). In China, rates ranged from 6 to 46.3% with the country showing the highest prevalence of cyberbullying perpetration (46.30%) ( 15 , 43 , 45 , 46 ). Canadian and South Korean studies reported the lowest prevalence of cyberbullying perpetration at 7.99 and 6.3%, respectively ( 34 , 39 ).

A total of 10 studies were assessed as high quality studies. Among them, six studies came from high income countries, including Canada, Germany, Italy, Portugal, and South Korea ( 13 , 34 , 39 , 46 – 48 ). Three studies were from upper middle income countries, including Malaysia and China ( 37 , 43 ) and one from a lower middle income country, Nigeria ( 49 ). Figures 2 , 3 describe the prevalence of cyberbullying victimization and perpetration respectively among high quality studies.

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Figure 2 . The prevalence of cyberbullying victimization of high quality studies.

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Figure 3 . The prevalence of cyberbullying perpetration of high quality studies.

Prevalence of Various Cyberbullying Behaviors

For the prevalence of cyberbullying victimization and perpetration, the data were reported in 18 and 14 studies, respectively. Figure 4 shows the distribution characteristics of the estimated value of prevalence of different cyberbullying behaviors with box plots. The longer the box, the greater the degree of variation of the numerical data and vice versa. The rate of victimization and crime of verbal violence, as well as the rate of victimization of other behaviors, such as cyberstalking and digital dating abuse, has a large degree of variation. Among the four specified types of cyberbullying behaviors, verbal violence was regarded as the most commonly reported behaviors in both perpetration and victimization rates, with a wide range of prevalence, ranging from 5 to 18%. Fewer studies reported the prevalence data for visual violence and group violence. Studies also showed that the prevalence of impersonation and account forgery were within a comparatively small scale. Specific results were as follows.

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Figure 4 . Cyberbullying prevalence across types (2015–2019).

Verbal Violence

A total of 13 studies reported verbal violence prevalence data ( 15 , 26 , 34 , 37 – 39 , 42 , 43 , 47 , 48 , 50 , 51 ). Ten studies reported the prevalence of verbal violence victimization ranging from 2.8 to 47.5%, while seven studies claimed perpetration prevalence ranging from 1.5 to 31.8%. Malaysia reported the highest prevalence of verbal violence victimization (47.5%) ( 37 ), followed by China (32%) ( 43 ). China reported that the prevalence of verbal violence victimization ranged from 5.1 to 32% ( 15 , 43 ). Israel reported that the prevalence of verbal violence victimization ranged from 3.4 to 18% ( 26 , 38 , 42 ). For perpetration rate, Malaysia reported the highest level at 31.8% ( 37 ), while a study for Spain reported the lowest, ranging from 3.2 to 6.4% ( 51 ).

Group Violence

The prevalence of group violence victimization was explored within 4 studies and ranged from 5 to 17.8% ( 26 , 34 , 42 , 43 ), while perpetration prevalence was reported in three studies, ranging from 10.1 to 19.07% ( 34 , 43 , 47 ). An Israeli study suggested that 9.8% of respondents had been excluded from the Internet, while 8.9% had been refused entry to a group or team ( 26 ). A study in South Korea argued that the perpetration prevalence of group violence was 10.1% ( 34 ), while a study in Italy reported that the rate of online group violence against others was 19.07% ( 47 ).

Visual Violence

The prevalence of visual violence victimization was explored within three studies and ranged from 2.6 to 12.1% ( 26 , 34 , 43 ), while the perpetration prevalence reported in four studies ranged from 1.7 to 6% ( 34 , 43 , 47 , 48 ). For victimization prevalence, a South Korean study found that 12.1% of respondents reported that their personal information was leaked online ( 34 ). An Israel study reported that the prevalence of outing the picture was 2.6% ( 26 ). For perpetration prevalence, a South Korean study found that 1.7% of respondents had reported that they had disclosed someone's personal information online ( 34 ). A German study reported that 6% of respondents had written a message (e.g., an email) to somebody using a fake identity ( 48 ).

Impersonating and Account Forgery

Four studies reported on the victimization prevalence of impersonating and account forgery, ranging from 1.1 to 10% ( 15 , 42 , 43 ), while five studies reported on perpetration prevalence, with the range being from 1.3 to 9.31% ( 15 , 43 , 47 , 48 , 51 ). In a Spanish study, 10% of respondents reported that their accounts had been infringed by others or that they could not access their account due to stolen passwords. In contrast, 4.5% of respondents reported that they had infringed other people's accounts or stolen passwords, with 2.5% stating that they had forged other people's accounts ( 51 ). An Israeli study reported that the prevalence of being impersonated was 7% ( 42 ), while in China, a study reported this to be 8.6% ( 43 ). Another study from China found that 1.1% of respondents had been impersonated to send dating-for-money messages ( 15 ).

Other Behaviors

The prevalence of disclosure of privacy, sexual harassment, and cyberstalking were also explored by scholars. Six studies reported the victimization prevalence of other cyberbullying behaviors ( 13 , 15 , 34 , 37 , 42 , 43 ), and four studies reported on perpetration prevalence ( 34 , 37 , 43 , 48 ). A study in China found that 1.2% of respondents reported that their privacy had been compromised without permission due to disputes ( 15 ). A study from China reported the prevalence of cyberstalking victimization was 11.9% ( 43 ), while a Portuguese study reported that this was 62% ( 13 ). In terms of perpetration prevalence, a Malaysian study reported 2.7% for sexual harassment ( 37 ).

Risk and Protective Factors of Cyberbullying

In terms of the risk factors associated with cyberbullying among children and adolescents, this comprehensive review highlighted both personal and situational factors. Personal factors referred to age, gender, online behavior, race, health conditions, past experiences of victimization, and impulsiveness, while situational factors consisted of parent-child relationship, interpersonal relationships, and geographical location. In addition, protective factors against cyberbullying included: empathy and emotional intelligence, parent-child relationship, and school climate. Table 2 shows the risk and protective factors for child and adolescent cyberbullying.

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Table 2 . Risk and protective factors of cyberbullying among children and adolescents.

In terms of the risk factors associated with cyberbullying victimization at the personal level, many studies evidenced that females were more likely to be cyberbullied than males ( 13 , 26 , 29 , 38 , 43 , 52 , 54 , 55 , 58 ). Meanwhile, adolescents with mental health problems ( 61 ), such as depression ( 33 , 62 ), borderline personality disorder ( 63 ), eating disorders ( 41 ), sleep deprivation ( 56 ), and suicidal thoughts and suicide plans ( 64 ), were more likely to be associated with cyberbullying victimization. As for Internet usage, researchers agreed that youth victims were probably those that spent more time online than their counterparts ( 32 , 36 , 43 , 45 , 48 , 49 , 60 ). For situational risk factors, some studies have proven the relationship between cyberbullying victims and parental abuse, parental neglect, family dysfunction, inadequate monitoring, and parents' inconsistency in mediation, as well as communication issues ( 33 , 64 , 68 , 73 ). In terms of geographical location, some studies have reported that youths residing in city locations are more likely to be victims of cyberbullying than their peers from suburban areas ( 61 ).

Regarding the risk factors of cyberbullying perpetration at the personal level, it is generally believed that older teenagers, especially those aged over 15 years, are at greater risk of becoming cyberbullying perpetrators ( 55 , 67 ). When considering prior cyberbullying experiences, evidence showed that individuals who had experienced cyberbullying or face-to-face bullying tended to be aggressors in cyberbullying ( 35 , 42 , 49 , 51 , 55 ); in addition, the relationship between impulsiveness and cyberbullying perpetration was also explored by several pioneering scholars ( 55 , 72 , 80 ). The situational factors highlight the role of parents and teachers in cyberbullying experiences. For example, over-control and authoritarian parenting styles, as well as inharmonious teacher-student relationships ( 61 ) are perceived to lead to cyberbullying behaviors ( 74 , 75 ). In terms of differences in geographical locations, students residing in cities have a higher rate of online harassment than students living in more rural locations ( 49 ).

In terms of the protective factors in child and adolescent cyberbullying, scholars have focused on youths who have limited experiences of cyberbullying. At the personal level, high emotional intelligence, an ability for emotional self-control and empathy, such as cognitive empathy ability ( 44 , 55 ), were associated with lower rates of cyberbullying ( 57 ). At the situational level, a parent's role is seen as critical. For example, intimate parent-child relationships ( 46 ) and open active communication ( 19 ) were demonstrated to be related to lower experiences of cyberbullying and perpetration. Some scholars argued that parental supervision and monitoring of children's online activities can reduce their tendency to participate in some negative activities associated with cyberbullying ( 31 , 46 , 73 ). They further claimed that an authoritative parental style protects youths against cyberbullying ( 43 ). Conversely, another string of studies evidenced that parents' supervision of Internet usage was meaningless ( 45 ). In addition to conflicting roles of parental supervision, researchers have also looked into the role of schools, and posited that positive school climates contribute to less cyberbullying experiences ( 61 , 79 ).

Some risk factors may be protective factors under another condition. Some studies suggest that parental aggressive communication is related to severe cyberbullying victims, while open communication is a potential protective factor ( 19 ). Parental neglect, parental abuse, parental inconsistency in supervision of adolescents' online behavior, and family dysfunction are related to the direct or indirect harm of cyberbullying ( 33 , 68 ). Parental participation, a good parental-children relationship, communication and dialogue can enhance children's school adaptability and prevent cyberbullying behaviors ( 31 , 74 ). When parental monitoring reaches a balance between control and openness, it could become a protective factor against cyberbullying, and it could be a risk factor, if parental monitoring is too low or over-controlled ( 47 ).

Despite frequent discussion about the risk factors associated with cyberbullying among children and adolescents, some are still deemed controversial factors, such as age, race, gender, and the frequency of suffering on the internet. For cyberbullying victims, some studies claim that older teenagers are more vulnerable to cyberbullying ( 15 , 38 , 52 , 53 ), while other studies found conflicting results ( 26 , 33 ). As for student race, Alhajji et al. argued that non-white students were less likely to report cyberbullying ( 29 ), while Morin et al. observed no significant correlation between race and cyberbullying ( 52 ). For cyberbullying perpetration, Alvarez-Garcia found that gender differences may have indirect effects on cyberbullying perpetration ( 55 ), while others disagreed ( 42 , 61 , 68 – 70 ). Specifically, some studies revealed that males were more likely to become cyberbullying perpetrators ( 34 , 39 , 56 ), while Khurana et al. presented an opposite point of view, proposing that females were more likely to attack others ( 71 ). In terms of time spent on the Internet, some claimed that students who frequently surf the Internet had a higher chance of becoming perpetrators ( 49 ), while others stated that there was no clear and direct association between Internet usage and cyberbullying perpetration ( 55 ).

In addition to personal and situational factors, scholars have also explored other specific factors pertaining to cyberbullying risk and protection. For instance, mindfulness and depression were found to be significantly related to cyber perpetration ( 76 ), while eating disorder psychopathology in adolescents was associated with cyber victimization ( 41 ). For males who were familiar with their victims, such as family members, friends and acquaintances, they were more likely to be cyberstalking perpetrators than females or strangers, while pursuing desired closer relationships ( 13 ). In the school context, a lower social likability in class was identified as an indirect factor for cyberbullying ( 48 ).

This comprehensive review has established that the prevalence of global childhood and adolescent victimization from cyberbullying ranges from 13.99 to 57.5%, and that the perpetration prevalence ranges from 6.0 to 46.3%. Across the studies included in our research, verbal violence is observed as one of the most common acts of cyberbullying, including verbal offensive responses, insults, mocking, threats, slander, and harassment. The victimization prevalence of verbal violence is reported to be between 5 and 47.5%, and the perpetration prevalence is between 3.2 and 26.1%. Personal factors, such as gender, frequent use of social media platforms, depression, borderline personality disorder, eating disorders, sleep deprivation, and suicidal tendencies, were generally considered to be related to becoming a cyberbullying victim. Personal factors, such as high school students, past experiences, impulse, improperly controlled family education, poor teacher-student relationships, and the urban environment, were considered risk factors for cyberbullying perpetration. Situational factors, including parental abuse and neglect, improper monitoring, communication barriers between parents and children, as well as the urban environment, were also seen to potentially contribute to higher risks of both cyberbullying victimization and perpetration.

Increasing Prevalence of Global Cyberbullying With Changing Social Media Landscape and Measurement Alterations

This comprehensive review suggests that global cyberbullying rates, in terms of victimization and perpetration, were on the rise during the 5 year period, from 2015 to 2019. For example, in an earlier study conducted by Modecki et al. the average cyberbullying involvement rate was 15% ( 81 ). Similar observations were made by Hamm et al. who found that the median rates of youth having experienced bullying or who had bullied others online, was 23 and 15.2%, respectively ( 82 ). However, our systematic review summarized global children and adolescents cyberbullying in the last 5 years and revealed an average cyberbullying perpetration rate of 25.03%, ranging from 6.0 to 46.3%, while the average victimization was 33.08%, ranging from 13.99 to 57.5%. The underlying reason for increases may be attributed to the rapid changing landscape of social media and, in recent years, the drastic increase in Internet penetration rates. With the rise in Internet access, youths have greater opportunities to participate in online activities, provided by emerging social media platforms.

Although our review aims to provide a broader picture of cyberbullying, it is well-noted in extant research that difficulties exist in accurately estimating variations in prevalence in different countries ( 23 , 83 ). Many reasons exist to explain this. The first largely relates poor or unclear definition of the term cyberbullying; this hinders the determination of cyberbullying victimization and perpetration ( 84 ). Although traditional bullying behavior is well-defined, the definition cannot directly be applied to the virtual environment due to the complexity in changing online interactions. Without consensus on definitions, measurement and cyberbullying types may vary noticeably ( 83 , 85 ). Secondly, the estimation of prevalence of cyberbullying is heavily affected by research methods, such as recall period (lifetime, last year, last 6 months, last month, or last week etc.), demographic characteristics of the survey sample (age, gender, race, etc.), perspectives of cyberbullying experiences (victims, perpetrators, or both victim and perpetrator), and instruments (scales, study-specific questions) ( 23 , 84 , 86 ). The variety in research tools and instruments used to assess the prevalence of cyberbullying can cause confusion on this issue ( 84 ). Thirdly, variations in economic development, cultural backgrounds, human values, internet penetration rates, and frequency of using social media may lead to different conclusions across countries ( 87 ).

Acknowledging the Conflicting Role of the Identified Risk Factors With More Research Needed to Establish the Causality

Although this review has identified many personal and situational factors associated with cyberbullying, the majority of studies adopted a cross-sectional design and failed to reveal the causality ( 21 ). Nevertheless, knowledge on these correlational relationships provide valuable insights for understanding and preventing cyberbullying incidents. In terms of gender differences, females are believed to be at a higher risk of cyberbullying victimization compared to males. Two reasons may help to explain this. First, the preferred violence behaviors between two genders. females prefer indirect harassment, such as the spreading of rumors, while males tend toward direct bullying (e.g., assault) ( 29 ) and second, the cultural factors. From the traditional gender perspective, females tended to perceive a greater risk of communicating with others on the Internet, while males were more reluctant to express fear, vulnerability and insecurity when asked about their cyberbullying experiences ( 46 ). Females were more intolerant when experiencing cyberstalking and were more likely to report victimization experiences than males ( 13 ). Meanwhile, many researchers suggested that females are frequent users of emerging digital communication platforms, which increases their risk of unpleasant interpersonal contact and violence. From the perspective of cultural norms and masculinity, the reporting of cyberbullying is also widely acknowledged ( 37 ). For example, in addition, engaging in online activities is also regarded as a critical predictor for cyberbullying victimization. Enabled by the Internet, youths can easily find potential victims and start harassment at any time ( 49 ). Participating in online activities directly increases the chance of experiencing cyberbullying victimization and the possibility of becoming a victim ( 36 , 45 ). As for age, earlier involvement on social media and instant messaging tools may increase the chances of experiencing cyberbullying. For example, in Spain, these tools cannot be used without parental permission before the age of 14 ( 55 ). Besides, senior students were more likely to be more impulsive and less sympathetic. They may portray more aggressive and anti-social behaviors ( 55 , 72 ); hence senior students and students with higher impulsivity were usually more likely to become cyberbullying perpetrators.

Past experiences of victimization and family-related factors are another risk for cyberbullying crime. As for past experiences, one possible explanation is that young people who had experienced online or traditional school bullying may commit cyberbullying using e-mails, instant messages, and text messages for revenge, self-protection, or improving their social status ( 35 , 42 , 49 , 55 ). In becoming a cyberbullying perpetrator, the student may feel more powerful and superior, externalizing angry feelings and relieving the feelings of helplessness and sadness produced by past victimization experiences ( 51 ). As for family related factors, parenting styles are proven to be highly correlated to cyberbullying. In authoritative families, parents focus on rational behavioral control with clear rules and a high component of supervision and parental warmth, which have beneficial effects on children's lifestyles ( 43 ). Conversely, in indulgent families, children's behaviors are not heavily restricted and parents guide and encourage their children to adapt to society. The characteristics of this indulgent style, including parental support, positive communication, low imposition, and emotional expressiveness, possibly contribute to more parent-child trust and less misunderstanding ( 75 ). The protective role of warmth/affection and appropriate supervision, which are common features of authoritative or indulgent parenting styles, mitigate youth engagement in cyberbullying. On the contrary, authoritarian and neglectful styles, whether with excessive or insufficient control, are both proven to be risk factors for being a target of cyberbullying ( 33 , 76 ). In terms of geographical location, although several studies found that children residing in urban areas were more likely to be cyberbullying victims than those living in rural or suburban areas, we cannot draw a quick conclusion here, since whether this difference attributes to macro-level differences, such as community safety or socioeconomic status, or micro-level differences, such as teacher intervention in the classroom, courses provided, teacher-student ratio, is unclear across studies ( 61 ). An alternative explanation for this is the higher internet usage rate in urban areas ( 49 ).

Regarding health conditions, especially mental health, some scholars believe that young people with health problems are more likely to be identified as victims than people without health problems. They perceive health condition as a risk factor for cyberbullying ( 61 , 63 ). On the other hand, another group of scholars believe that cyberbullying has an important impact on the mental health of adolescents which can cause psychological distress consequences, such as post-traumatic stress mental disorder, depression, suicidal ideation, and drug abuse ( 70 , 87 ). It is highly possible that mental health could be risk factors, consequences of cyberbullying or both. Mental health cannot be used as standards, requirements, or decisive responses in cyberbullying research ( 13 ).

The Joint Effort Between Youth, Parents, Schools, and Communities to Form a Cyberbullying-Free Environment

This comprehensive review suggests that protecting children and adolescents from cyberbullying requires joint efforts between individuals, parents, schools, and communities, to form a cyberbullying-free environment. For individuals, young people are expected to improve their digital technology capabilities, especially in the use of social media platforms and instant messaging tools ( 55 ). To reduce the number of cyberbullying perpetrators, it is necessary to cultivate emotional self-regulation ability through appropriate emotional management training. Moreover, teachers, counselors, and parents are required to be armed with sufficient knowledge of emotional management and to develop emotional management capabilities and skills. In this way, they can be alert to the aggressive or angry emotions expressed by young people, and help them mediate any negative emotions ( 45 ), and avoid further anti-social behaviors ( 57 ).

For parents, styles of parenting involving a high level of parental involvement, care and support, are desirable in reducing the possibility of children's engagement in cyberbullying ( 74 , 75 ). If difficulties are encountered, open communication can contribute to enhancing the sense of security ( 73 ). In this vein, parents should be aware of the importance of caring, communicating and supervising their children, and participate actively in their children's lives ( 71 ). In order to keep a balance between control and openness ( 47 ), parents can engage in unbiased open communication with their children, and reach an agreement on the usage of computers and smart phones ( 34 , 35 , 55 ). Similarly, it is of vital importance to establish a positive communication channel with children ( 19 ).

For schools, a higher priority is needed to create a safe and positive campus environment, providing students with learning opportunities and ensuring that every student is treated equally. With a youth-friendly environment, students are able to focus more on their academic performance and develop a strong sense of belonging to the school ( 79 ). For countries recognizing collectivist cultural values, such as China and India, emphasizing peer attachment and a sense of collectivism can reduce the risk of cyberbullying perpetration and victimization ( 78 ). Besides, schools can cooperate with mental health agencies and neighboring communities to develop preventive programs, such as extracurricular activities and training ( 44 , 53 , 62 ). Specifically, school-based preventive measures against cyberbullying are expected to be sensitive to the characteristics of young people at different ages, and the intersection of race and school diversity ( 29 , 76 ). It is recommended that school policies that aim to embrace diversity and embody mutual respect among students are created ( 26 ). Considering the high prevalence of cyberbullying and a series of serious consequences, it is suggested that intervention against cyberbullying starts from an early stage, at about 10 years old ( 54 ). Schools can organize seminars to strengthen communication between teachers and students so that they can better understand the needs of students ( 61 ). In addition, schools should encourage cyberbullying victims to seek help and provide students with opportunities to report cyberbullying behaviors, such as creating online anonymous calls.

Conclusions and Limitations

The comprehensive study has reviewed related research on children and adolescents cyberbullying across different countries and regions, providing a positive understanding of the current situation of cyberbullying. The number of studies on cyberbullying has surged in the last 5 years, especially those related to risk factors and protective factors of cyberbullying. However, research on effective prevention is insufficient and evaluation of policy tools for cyberbullying intervention is a nascent research field. Our comprehensive review concludes with possible strategies for cyberbullying prevention, including personal emotion management, digital ability training, policy applicability, and interpersonal skills. We highlight the important role of parental control in cyberbullying prevention. As for the role of parental control, it depends on whether children believe their parents are capable of adequately supporting them, rather than simply interfering in their lives, restricting their online behavior, and controlling or removing their devices ( 50 ). In general, cyberbullying is on the rise, with the effectiveness of interventions to meet this problem still requiring further development and exploration ( 83 ).

Considering the overlaps between cyberbullying and traditional offline bullying, future research can explore the unique risk and protective factors that are distinguishable from traditional bullying ( 86 ). To further reveal the variations, researchers can compare the outcomes of interventions conducted in cyberbullying and traditional bullying preventions simultaneously, and the same interventions only targeting cyberbullying ( 88 ). In addition, cyberbullying also reflects a series of other social issues, such as personal privacy and security, public opinion monitoring, multinational perpetration and group crimes. To address this problem, efforts from multiple disciplines and novel analytical methods in the digital era are required. As the Internet provides enormous opportunities to connect young people from all over the world, cyberbullying perpetrators may come from transnational networks. Hence, cyberbullying of children and adolescents, involving multiple countries, is worth further attention.

Our study has several limitations. First, national representative studies are scarce, while few studies from middle and low income countries were included in our research due to language restrictions. Many of the studies included were conducted in schools, communities, provinces, and cities in high income countries. Meanwhile, our review only focused on victimization and perpetration. Future studies should consider more perspectives, such as bystanders and those with the dual identity of victim/perpetrator, to comprehensively analyze the risk and protective factors of cyberbullying.

Data Availability Statement

The original contributions presented in the study are included in the article/ Supplementary Material , further inquiries can be directed to the corresponding author/s.

Author Contributions

SH, CZ, RE, and WZ conceived the study and developed the design. WZ analyzed the result and supervised the study. CZ and SH wrote the first draft. All authors contributed to the article and approved the submitted version.

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.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpubh.2021.634909/full#supplementary-material

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Keywords: cyberbullying, children, adolescents, globalization, risk factors, preventive measures

Citation: Zhu C, Huang S, Evans R and Zhang W (2021) Cyberbullying Among Adolescents and Children: A Comprehensive Review of the Global Situation, Risk Factors, and Preventive Measures. Front. Public Health 9:634909. doi: 10.3389/fpubh.2021.634909

Received: 29 November 2020; Accepted: 10 February 2021; Published: 11 March 2021.

Reviewed by:

Copyright © 2021 Zhu, Huang, Evans and Zhang. 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: Wei Zhang, weizhanghust@hust.edu.cn

† These authors have contributed equally to this work and share first authorship

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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Current perspectives: the impact of cyberbullying on adolescent health

Charisse l nixon.

Pennsylvania State University, the Behrend College, Erie, PA, USA

Cyberbullying has become an international public health concern among adolescents, and as such, it deserves further study. This paper reviews the current literature related to the effects of cyberbullying on adolescent health across multiple studies worldwide and provides directions for future research. A review of the evidence suggests that cyberbullying poses a threat to adolescents’ health and well-being. A plethora of correlational studies have demonstrated a cogent relationship between adolescents’ involvement in cyberbullying and negative health indices. Adolescents who are targeted via cyberbullying report increased depressive affect, anxiety, loneliness, suicidal behavior, and somatic symptoms. Perpetrators of cyberbullying are more likely to report increased substance use, aggression, and delinquent behaviors. Mediating/moderating processes have been found to influence the relationship between cyberbullying and adolescent health. More longitudinal work is needed to increase our understanding of the effects of cyberbullying on adolescent health over time. Prevention and intervention efforts related to reducing cyberbullying and its associated harms are discussed.

Adolescents in the United States culture are moving from using the Internet as an “extra” in everyday communication (cyber utilization) to using it as a “primary and necessary” mode of communication (cyber immersion). 1 In fact, 95% of adolescents are connected to the Internet. 2 This shift from face-to-face communication to online communication has created a unique and potentially harmful dynamic for social relationships – a dynamic that has recently been explored in the literature as cyberbullying and Internet harassment.

In general, cyberbullying involves hurting someone else using information and communication technologies. This may include sending harassing messages (via text or Internet), posting disparaging comments on a social networking site, posting humiliating pictures, or threatening/intimidating someone electronically. 3 – 7 Unfortunately, cyberbullying behavior has come to be accepted and expected among adolescents. 8 Compared to traditional bullying, cyberbullying is unique in that it reaches an unlimited audience with increased exposure across time and space, 6 , 9 preserves words and images in a more permanent state, 10 and lacks supervision. 6 Further, perpetrators of cyberbullying do not see the faces of their targets, 11 and subsequently may not understand the full consequences of their actions, thereby decreasing important feelings of personal accountability. 9 This has often been referred to in the literature as the “disinhibition effect”. 12

Cyberbullying has emerged as a relatively new form of bullying within the last decade. 13 , 14 This new focus on cyberbullying has, in part, been driven by recent news media highlighting the connection between cyberbullying and adolescent suicides (US News, 2013 15 ), with one of the most recent cases involving Rebecca Sedwick, a 12-year-old girl from Polk County, FL, USA who jumped to her death after experiencing relentless acts of cyberbullying. Initial work on cyberbullying has focused on documenting prevalence rates, sex-related effects, and identifying similarities/differences to traditional forms of bullying. More recently, work has been conducted on establishing the psychosocial (for example, depression, anxiety) and psychosomatic correlates (for example, headaches, stomachaches) of cyberbullying.

Given that cyberbullying is a relatively new construct, it is important to note that there are still definitional and methodological inconsistencies throughout the literature. For example, some scholars have chosen to adopt a more conservative criterion to define cyberbullying (for example, “willful and repeated harm inflicted through the use of computers, cell phones, and other electronic devices” 3 , 6 ), while other scholars have used a more broad definition (for example, “using electronic means to intentionally harm someone else” 16 ). The term cyberbullying in this review will represent an umbrella term that includes related constructs such as Internet bullying, online bullying, and information communication technologies and Internet harassment. Another inconsistency in the literature includes the use of different reference points when assessing adolescents’ involvement with cyberbullying. For example, some researchers have asked adolescents to think about their experiences with cyberbullying within the last year, 17 – 19 while others have inquired about adolescents’ experiences within the past 9 months, 20 or the past couple of months. 21 , 22 Given these methodological inconsistencies, it is not surprising that the prevalence rates of cyberbullying victimization and perpetration vary widely. For example, prevalence rates for cyberbullying victimization range from 4%–72%, 23 , 24 with an average of 20%–40% of adolescents reporting victimization via cyberbullying. 25 Prevalence rates for cyberbullying perpetration also vary, ranging from 3%–36% 26 , 27 (Also unpublished data, Kowalski and Witte 2006). Although the variability is significant, the research is clear that cyberbullying is prevalent during adolescence and as such, merits further study.

The purpose of the current review is to explore the impact of cyberbullying on adolescent health across multiple studies worldwide. It is anticipated that this information can be used to increase the knowledge of practitioners, health care providers, educators, and scholars, and subsequently better inform prevention and intervention efforts related to reducing cyberbullying and its associated harm. The first section of this paper reviews the effects of cyberbullying victimization and perpetration on adolescent health. The next section includes a brief discussion of individual risk factors related to participation in cyberbullying. The third section highlights mediating and moderating processes related to the impact of cyberbullying on adolescent health. The final section addresses prevention and intervention efforts related to minimizing cyberbullying and its subsequent effect on adolescent health.

Effects of cyberbullying

The effects of cyberbullying have been predominantly explored in the area of adolescents’ mental health concerns. In general, researchers have examined the relationship between involvement with cyberbullying and adolescents’ tendency to internalize issues (for example, the development of negative affective disorders, loneliness, anxiety, depression, suicidal ideation, and somatic symptoms). This relationship has been explored among Finnish youth, 28 Turkish youth, 26 German youth, 29 Asian and Pacific Islander youth, 17 American youth, 20 youth living in Northern Ireland, 30 Swedish youth, 31 Australian youth, 32 Israeli youth, 33 Canadian youth, 34 Czech youth, 35 Chinese youth, 36 and Taiwanese youth. 37 Although not as prolific, past work has also examined the impact of cyberbullying on adolescents’ tendency to externalize issues (for example, through substance use, delinquency).

Cyberbullying victimization and internalizing issues

Past work has revealed a significant relationship between one’s involvement in cyberbullying and affective disorders. For example, results indicate that there is a significant relationship between cybervictimization and depression among adolescents, 20 , 38 – 43 and among college students. 44 Specifically, results showed that higher levels of cyberbullying victimization were related to higher levels of depressive affect. Raskauskas and Stoltz 45 asked adolescents open-ended questions about the negative effects of cyberbullying. Notably, 93% of cybervictims reported negative effects, with the majority of victims reporting feelings of sadness, hopelessness, and powerlessness. Perren et al 39 further investigated the relationship between depression and cybervictimization among Swiss and Australian adolescents by controlling for traditional forms of victimization. Their results demonstrated that cybervictimization explained a significant amount of the variance in adolescent’s depressive symptomology, even when controlling for traditional forms of victimization.

Cyberbullying has been conceptualized as a stressor. For example, Finkelhor et al 46 found that 32% of targets of cyberbullying experienced at least one symptom of stress. Similarly, targets of online harassment reported increased rates of trauma symptomology. 47 Relatedly, findings from the Second Youth Internet Safety Survey 48 indicated that 38% of adolescent victims reported that they were emotionally distressed (ie, extremely upset) as a result of being harassed on the Internet. Not surprisingly, Sourander et al 28 found that cybervictims feared for their safety. It is posited that cyberbullying is more stressful than traditional bullying, perhaps in part related to the anonymity of cyberbullying. Compared to traditional bullying, targets of cyberbullying are less likely to know their perpetrators. 4 In fact, in a recent American study, half of the targets who were cyberbullied reported that they did not know their perpetrators, 49 thereby contributing to increased fears related to the identities of their perpetrators. Literally, the perpetrators could be anyone; even the victims’ closest friends. 45 Consistent with these findings, a recent study conducted in the US found that cyberbullying victimization was related to adolescents’ increased fear of victimization, even when controlling for their past victimization experiences and disordered school environments. 50 Moreover, youth who were targets of cyberbullying reported increased feelings of embarrassment, hurt, self-blame, and fear. 41 , 51 In telephone interviews with adolescents about their experiences with online harassment, Finkelhor et al 46 reported that adolescents felt angry, embarrassed, and upset. Consistent with a myriad of other studies, the most common response to cyberbullying was anger, 6 , 18 , 51 , 52 followed by upset and worry. 52

However, reactions to being cyberbullied may depend on the form of cyberbullying. For example, Ortega et al 53 found that different forms of cyberbullying may elicit different emotional reactions – for instance, being bullied online may evoke a different emotional reaction than being bullied via a cell phone. In terms of predicting the most deleterious outcomes, past studies have shown that pictures/video images were the most harmful to adolescents. 9 In support of the need to examine unique contexts of victimization, results from a more recent study conducted in the US revealed that different forms of electronic victimization (ie, cell phones, computers) were related to different psychological outcomes, with victimization via the computer (for example, online posts, pictures, email) being more harmful to adolescents than victimization via the phone (for example, text messaging and phone calls). 42

Cybervictimization is related to disruptions in adolescents’ relationships. Specifically, targets of cyberbullying reported more loneliness from their parents and peers, 54 along with increased feelings of isolation and helplessness. 40 Not surprisingly, targets of cyberbullying reported fewer friendships, 41 more emotional and peer relationship problems, 28 lower school attachment, 35 , 54 and more empathy. 35 Past work has shown that adolescents who were victimized via cyberbullying were more likely to lose trust in others, 11 experience increased social anxiety, 20 , 42 , 56 and decreased levels of self-esteem. 20 , 24 , 29 , 41 – 44 , 57 , 58 Importantly, the relationship between cybervictimization and adolescents’ psychosocial problems remain even after controlling for relational and physical forms of victimization, 20 as well as school-based victimization. 42

Cyberbullying and suicidal behavior

Several researchers have examined the association between involvement with cyberbullying and adolescent suicidal behavior. 34 , 38 , 44 , 55 , 59 This relationship has been explored among middle school, high school, and college students. For example, Hinduja and Patchin 59 surveyed American middle school students and examined the relationship between involvement in cyberbullying (either as a victim or perpetrator) and suicidality. The results revealed that both targets and perpetrators of cyberbullying were more likely to think about suicide, as well as attempt suicide, when compared to their peers who were not involved with cyberbullying. This relationship between cyberbullying and suicidality was stronger for targets, as compared to perpetrators of cyberbullying. Specifically, targets of cyberbullying were almost twice as likely to have attempted suicide (1.9 times), whereas perpetrators were 1.5 times more likely compared to their uninvolved peers. 59 Klomek et al 38 looked at the relationship between cybervictimization, depression, suicidal ideation, and suicidal attempts among American high school students. Their study results showed that cyberbullying victimization was related to increased depressive affect and suicidal behavior. Similarly, using an even larger high school sample, Schneider et al 55 also found a positive relationship between cybervictimization and suicidal behavior. This relationship has recently been documented among college students as well. 44

In an effort to control for possible confounding variables, researchers have examined the unique contribution of cyberbullying in predicting suicidal behavior and depressive symptomology above and beyond adolescents’ sex, and their involvement in relational, verbal, and physical bullying. Bonanno and Hymel 34 surveyed Canadian adolescents and found that cybervictimization and cyberbullying contributed to adolescents’ depressive symptomology and suicidal ideation over and above their sex and involvement in traditional forms of bullying (ie, face-to-face bullying). Moreover, adolescents’ involvement in cyberbullying was a stronger predictor of suicidal ideation than it was for depressive symptomology. These researchers posited that perhaps, given the public and permanent nature of the computer, along with the perceived lack of control and anonymity involved, targets of cyberbullying might experience a loss of hope, thereby magnifying the relationship between cyberbullying and suicidal ideation. Those adolescents who were both victims and perpetrators of cyberbullying experienced the greatest risk for suicidal ideation. 34

In sum, past work has documented the positive relationship between adolescents’ involvement in cyberbullying and suicidal behavior. That is, the more adolescents are involved in cyberbullying, the more likely they are to engage in suicidal behavior; this relationship was stronger for targets than for perpetrators of cyberbullying. Recent research has expanded upon these findings and examined the potential experience(s) that might mediate the relationship between cyberbullying and suicidal behavior. 60 In a recent study of American high school students, Litwiller and Brausch 60 found that adolescents’ substance use and violent behavior partially mediated the relationship between cyberbullying and suicidal behavior, such that increased substance use and involvement in physical violence predicted increased adolescent suicidal behavior related to cyberbullying. Further, Litwiller and Brausch 60 conceptualized substance use and violent behavior as coping processes that adolescents might use to address the physical and psychological pain associated with their experiences related to cyberbullying. This study underscores the need for not only educators and health care professionals, but also parents, guardians and mentors - all caring adults to play a role in addressing adolescents’ substance use and violent behavior. Results from this study suggest the need for health care providers, educators, and caring adults to equip adolescents with constructive coping strategies to effectively address cyberbullying.

Cyberbullying (both victims and perpetrators) and somatic concerns

There have been relatively few studies examining the effect of cyberbullying on adolescents’ physical health. Of those studies that have been conducted, a significant relationship between cyberbullying and psychosomatic difficulties has been established. For example, Kowalski and Limber 21 surveyed American adolescents and found that those youth who were both victims and perpetrators of cyberbullying experienced more severe forms of psychological (for example, anxiety, depression, and suicidal behavior) and physical health concerns (for example, problems sleeping, headache, poor appetite, and skin problems). Additionally, adolescents’ grade level moderated these negative effects, with high school students who were both perpetrators and victims of cyberbullying reporting the highest levels of anxiety, depression, and the most physical health problems. Similarly, Beckman et al 22 surveyed Swedish adolescents and found a positive relationship between involvement with cyberbullying and psychosomatic difficulties, including increased difficulty sleeping, stomachaches, headaches, and a lack of appetite, with adolescents who were both victims and perpetrators experiencing the most severe psychosomatic symptoms. Finally, Sourander et al 28 investigated the relationship between cyberbullying and psychiatric and psychosomatic problems among Finnish adolescents. Their study results showed that cybervictims and cyberbully/victims were more likely to experience somatic problems, including difficulty sleeping, headaches, and stomachaches, as compared to their unaffected peers. Notably, in a recent large-scale study of adolescents in Stockholm, Sweden, Låftman et al 61 found that being a target of cyberbullying was associated with poorer physical health (for example, headaches, stomachaches, poor appetite, sleep disturbances, and so on), even when controlling for traditional bullying. Given that health care providers are often on the front lines responding to adolescents’ somatic concerns, it is imperative that these professionals are adequately trained in the area of cyberbullying. For example, health care providers can be trained to effectively screen adolescents’ for psychological and physical health issues related to cyberbulling experiences. Subsequently, it seems logical for medical schools and residency programs to consider coursework in digital networking or online social networking to increase the medical community’s knowledge regarding the health correlates related to cyberbullying. 62

Cyberbullying victimization and externalizing issues

Although not as well documented, the effects of cyberbullying victimization are also related to adolescents’ externalizing problems. For example, among a sample of youth living in the US, Ybarra et al 63 found that those adolescents who were harassed online were more likely to use alcohol, drugs, and carry a weapon at school. In fact, victimized youth were eight times more likely than their peers to carry a weapon to school in the past 30 days. In a study of Asian and Pacific Islander youth, Goebert et al 17 found that cyberbullying victimization was associated with adolescents’ increased substance abuse. For example, targets of cyberbullying were 2.5 times more likely to use marijuana and participate in binge drinking compared to their peers. Similarly, other studies have documented a significant relationship between increased cyberbullying victimization and increased substance use. 13 , 43 Finally, cyberbullying victimization was also related to increased levels of traditional bullying (for example, physical aggression, stealing) among a sample of adolescents living in Hong Kong. 36 (See Table 1 for a summary of cross-sectional studies examining the relationship between cyberbullying victimization and negative health correlates.)

Findings from literature on cyberbullying victimization and adolescent health using cross sectional design

Does sex matter with respect to cyberbullying victimization?

The answer to this question is not clear. Thus far, the literature is inconsistent with respect to sex-related effects and the prevalence rates for cybervictimization. Some studies have found no sex differences, 5 , 6 , 13 , 24 , 26 , 29 , 31 , 57 , 64 – 66 while other studies have found sex effects documenting higher prevalence rates for females. 9 , 11 , 40 , 61 This sex effect indicating increased prevalence rates of cyberbullying among females has been documented among both younger and older adolescents. For example, among 10- and 11-year-olds, Devine and Lloyd 30 found that girls were more likely to be victims of cyberbullying compared to boys. Kowalski and Limber 4 found similar sex-based effects, documenting increased prevalence rates among adolescent females in 6th, 7th, and 8th grade. The same pattern has also been found among high school students. 17 This sex-based effect documenting increased prevalence rates for cybervictimization among females compared to males is consistent with research showing that females are more likely to be online for social networking, while males are more likely to be online for gaming. 68 Subsequently, the sheer frequency of females’ online social networking behavior may provide them with more opportunities than males to become involved with cyberbullying. 69

Only a few studies have documented higher prevalence rates for cyberbullying among males. For example, among German adolescents, Katzer et al 29 found that males reported more victimization online than females. Among a sample of adolescents living in Cyprus, males were also at a higher risk for cybervictimization. 70 Finally findings from a recent study conducted in Hong Kong indicated that males were more likely to be victimized via cyberbullying than females. 36 In sum, with the exception of a handful of studies, the majority of research conducted to date has demonstrated no sex effects related to cyberbullying victimization.

Cyberbullying perpetration and problem behaviors

Generally speaking, studies that have examined the impact of cyberbullying perpetration on adolescent health have shown that those adolescent perpetrators of cyberbullying were more likely to engage in problem behaviors including higher levels of proactive and reactive aggression, property damage, 23 illegal acts, 71 substance use, delinquency, 72 , 74 and suicidal behavior. 34 , 59 , 71 Cyberbullying perpetration has been positively associated with hyperactivity, relational aggression, 74 conduct problems, 19 , 28 , 71 smoking, and drunkenness. 22 , 28 Results from a recent study surveying Australian adolescents found that those youth who cyberbullied others reported more social difficulties, as well as more stress, depression, and anxiety compared to their peers who were not involved in any type of bullying. 75 On the other hand, cyberbullying perpetration has been related to adolescents’ decreased levels of self-esteem, 76 self-efficacy, 36 prosocial behavior, perceived sense of belonging, 36 and safety at school. 28 Cyberbullying perpetration has also been associated with adolescents’ negative emotions such as anger, sadness, frustration, fear, and embarrassment. 19 , 72 , 77 Disruptions in relationships have also been associated with cyberbullying perpetration among youth, including lower levels of empathy, 36 , 74 increased levels of depression, 34 weaker emotional bonds with caregivers, lower parental monitoring, and increased use of punitive discipline. 73 Finally, perpetrators of cyberbullying were more likely to rationalize their destructive behaviors by minimizing the impact they had on others. For example, they were more likely to believe that their bullying behavior was not that harsh and that it did not bother their victims that much. 75 (See Table 2 for a summary of cross-sectional studies examining the relationship between cyberbullying perpetration and negative health correlates.)

Findings from literature on cyberbullying perpetration and adolescent health using cross sectional design

Similar to cyberbullying victimization, sex-related effects for cyberbullying perpetration have also been inconsistent. For example, some studies have found an increase in female perpetration, 78 while other studies have indicated an increase in male cyberbullying perpetration. 11 , 36 , 61 Still yet, some researchers have found no sex differences in the prevalence of cyberbullying perpetration. 9 , 13 , 19 , 23 More research is needed before we are able to draw firm conclusions regarding the role of sex in cyberbullying perpetration.

What about those adolescents who are both victims and perpetrators of cyberbullying?

Notably, of researchers who have compared all three roles in cyberbullying, those adolescents who were both perpetrators and targets (ie, bully/victims) experienced the most adverse health outcomes, including decreased psychological and physical health. 21 , 22 , 28 , 34 , 40 Specifically, these adolescents reported increased levels of depression, substance use, and conduct problems compared to their peers who were either only targets or perpetrators. 23 , 21 Adolescents who were both targets and perpetrators of cyberbullying also reported poorer relationships with their caregivers, and higher levels of victimization and perpetration offline, compared to their peers. These results suggest that this group of adolescents (ie, bullies/victims) may experience increased risk for associated negative health outcomes, and as such, may require extra support from health care professionals, educators, and caring adults. However, we currently know relatively very little about this group of adolescents. 79 More work is needed to increase our understanding of this potentially vulnerable group of adolescents.

Taken together, results from a myriad of studies worldwide suggest that involvement in cyberbullying puts adolescents at risk for increased internalizing problems including depression, anxiety, suicidal ideation, and psychosomatic concerns (for example, difficulties sleeping, headaches, and stomachaches), as well as a loss of connection from parents and peers, thereby threatening adolescents’ basic fundamental need for meaningful connections. 80 In addition, participation in cyberbullying also places adolescents at risk for increased externalizing issues, such as substance use and delinquent behavior.

How do the developmental changes in risk factors affect subsequent cyberbullying?

Recently, researchers have begun to examine how developmental changes in adolescent risk factors affect subsequent involvement in cyberbullying behavior. For example, Modecki et al 81 recently investigated the role of increasing developmental problems (ie, problem behavior and poor emotional well-being) among adolescents (number [N] =1,364) in predicting subsequent involvement in cyberbullying over a 3-year period, while controlling for sex and pubertal timing. The study findings demonstrated that adolescents’ developmental increases in problem behavior across grades 8 through 10 predicted their involvement with cyberbullying in grade 11. Specifically, developmental decreases in self-esteem and increases in problem behavior (ie, substance use, aggressive behavior, and delinquency) predicted adolescents’ cybervictimization and perpetration in grade 11. Interestingly, self-esteem was measured with items assessing identity and efficacy (for example, “How often do you feel satisfied with who are?” “How often do you feel sure about yourself?”). Results from this study suggest that heath care professionals and educators should carefully examine the trajectory of students’ sense of self, as well as problem behaviors (for example, physical aggression and substance use) during adolescence in an effort to reduce subsequent involvement with cyberbullying. Further, these results showed that adolescents who experienced increased depression in grade 8 were at higher risk for both cybervictimization and cyberperpetration in grade 11.

Researchers have also begun to examine the risk factors that may be related to involvement with cyberbullying behavior. For example, Sticca et al 67 examined longitudinal risk factors related to cyberbullying among 7th grade students. Their results showed that traditional bullying and rule-breaking behavior (for example, damaging property, cigarette/alcohol use) were the strongest predictors of cyberbullying perpetration, followed by the frequency of online communication (these researchers did not look at cyberbullying victimization). In sum, these study results showed that those adolescents who bullied others in the “real world” were more than four times likely to bully someone online several months later. These results suggest that effective prevention and intervention efforts designed to reduce cyberbullying may include early detection of delinquent behaviors offline, including substance use and aggressive behavior. Moreover, results from another recent longitudinal study demonstrated that adolescents’ loneliness and social anxiety predicted increases in subsequent cyberbullying victimization. 82 These results suggest that adolescents who are socially vulnerable may be at increased risk for experiencing online victimization.

Potential mediating and moderating processes that may influence the effect of cyberbullying on adolescent health

The message of past studies is clear: there is a cogent relationship between cyberbullying and negative adolescent health outcomes. In light of the negative impact of cyberbullying on adolescent health, it is imperative that future research examines potential mediating and moderating processes that might influence the impact of cyberbullying on adolescent health. We know that not all adolescents who experience cyberbullying report negative outcomes. 6 , 72 Subsequently, individual differences among adolescents need to be considered when examining the impact of cyberbullying on adolescent health. For example, according to the transactional theory of stress and coping, 83 the impact of cyberbullying does not solely depend on the event alone, but also on how the adolescent responds to the situation. We know that how adolescents respond to stressors (for example, cyberbullying) is influenced by a myriad of factors related to the individual adolescent, the context, and the stressor itself. 83 – 86 Moreover, the language we choose also affects how adolescents respond to stressors – language can either undermine or optimize adolescents’ responses. For example, the word “victim” tends to conjure up a sense of helplessness and a loss of control. 87 The word “target”, on the other hand, communicates deflection; that the individual has the power to deflect the aggressive behavior, thereby empowering the adolescent. 87 Subsequently, it follows that an adolescent who is identified as a “victim” may be more reluctant to seek help compared to an adolescent who is identified as a “target”. Clearly, the choice of language affects individuals’ ensuing responses. More work is needed to increase our understanding of these and others factors that may help to protect adolescents from adverse health outcomes. Adopting a contextual framework allows researchers to identify potential protective and at-risk variables that may mediate or moderate the effects of cyberbullying on adolescents’ health outcomes. Researchers and practitioners could then use this garnered knowledge to develop and sustain effective prevention and intervention programs to reduce cyberbullying behaviors and their associated harm. With that said, there is currently little known about how experiences with cyberbullying may interact with adolescents’ coping strategies, sex, and social support.

Coping strategies

Schenk and Fremouw 44 examined the coping strategies used by targets of cyberbullying. Their results revealed that targets of cyberbullying generally cope with cybervictimization by telling someone, avoiding friends or peers, getting revenge, and withdrawing from events, thus potentially undermining important social connections. However, Slonje and Smith 9 found that 50% of targets did not tell anyone, 35.7% told a friend, 8.9% told a parent or guardian, and 5.4% told someone else. Notably, the majority of targets do not tell adults, 10 , 88 – 91 with one study reporting up to 90% of adolescents not telling an adult about their experiences related to cyberbullying. 24 Although these studies have begun to identify the coping strategies used by targets of cyberbullying, the majority of these studies have not examined the effectiveness of these strategies in terms of reducing or promoting subsequent at-risk behavior. Strategy effectiveness is an important construct to study, as we begin to identify those strategies that help to reduce the negative effects of cyberbullying. For example, results from a recent longitudinal study conducted in the Netherlands by Völlink et al 93 demonstrated that adolescents’ use of emotion-focused coping strategies negatively affected their subsequent psychological (for example, depression) and physical health (for example, chest tightness, headaches). Past work has shown that adolescents’ coping strategies can mitigate or reduce the negative impact of cyberbullying, 87 and as such, they should be examined further.

Future work should also continue to examine the role of sex in moderating the relationship between cyberbullying and adolescents’ health. Although, as discussed earlier several studies have examined the sex effects related to the prevalence rates of cyberbullying, we know relatively very little about how sex may moderate the relationship between cyberbullying and adolescent health. In other words, is it possible that females may be more adversely affected by cyberbullying than males? This is an important question to consider when examining adolescent health outcomes. Of the few studies that have been conducted, inconsistent findings have been reported. For example, some studies have found that females are more likely to be distressed by cyberbullying than males, 18 , 93 , 94 while others have reported no sex differences. 20 Still yet, recent work conducted by Kowalski and Limber 21 revealed that among adolescents who were both perpetrators and targets of cyberbullying, males experienced more negative psychological (for example, depression and anxiety) and physical health concerns (for example, headache, problems sleeping, and skin problems) than females. In sum, future studies are needed to elucidate the potential role of sex in moderating the relationship between involvement with cyberbullying and adolescent health outcomes.

Social support

Research suggests that different forms of support may mitigate the effects of traditional forms of victimization on psychological well-being. 95 – 97 There are, however, very few studies that have examined how different forms of social support might mitigate the impact of cyberbullying on adolescent health. An exception to this is a recent study conducted by Machmutow et al, 93 who examined the moderating effects of different coping strategies on the relationship between cybervictimization and depressive symptoms using a longitudinal design. Results from their study showed that adolescents’ social support and feelings of helplessness predicted their depressive symptomology over time. Specifically, close feelings of social support mitigated the negative impact of cyberbullying on depressive symptomology, whereas feelings of helplessness increased depressive symptomology. Similarly, Fanti et al 70 examined how different forms of social support (ie, peer, family, and school) influenced the prevalence of cyberbullying. Using a longitudinal design, Fanti et al 70 found that adolescents’ family social support (for example, “I get the emotional support I need from my family”) was a protective factor for both cyberbullying victimization and cyberbullying perpetration, such that family social support was related to decreases in cyberbullying behaviors one year later, even after accounting for other risk factors. These results suggest that family social support may be an important protective factor in guarding against the negative health correlates of cyberbullying, and thus merits further scrutiny.

Prevention and intervention

Given the deleterious effects of cyberbullying, effective prevention and intervention efforts must be a priority. However, studies that investigate effective prevention and intervention efforts to address cyberbullying are currently lacking. 98 The few studies that have addressed prevention efforts related to cyberbullying suggest that attention be directed towards enhancing adolescents’ empathy and self-esteem, decreasing adolescents’ problem behaviors, promoting warm, nurturing relationships with their parents, and reducing their time spent online. For example, researchers who conducted a recent study with Turkish adolescents found that those adolescents who were less empathic were more at risk for engaging in cyberbullying. Their study results demonstrated that the combined effect of affective (ie, experiencing someone else’s feelings) and cognitive (ie, taking another’s perspective) empathy played a vital role in influencing adolescents’ participation in cyberbullying. Specifically, activating adolescents’ empathy was related to less negative bystander behavior. Results from this study suggest that future prevention and intervention efforts be targeted towards increasing adolescents’ affective (for example, “My friends’ feelings don’t affect me”) and cognitive empathy (for example, “I can understand why my friend might be upset when that happens”) in an effort to reduce participation in cyberbullying. 99 Empathy training seems particularly important given the nature of cyberspace and the lack of nonverbal cues available. For example, adolescents may be less inclined to experience empathy for targets online in part because they are not privy to the targets’ facial expressions. Subsequently, prevention efforts may need to explicitly demonstrate the hurt targets’ experience in order to activate adolescents’ empathic responses. 94

Recent findings also suggest that prevention efforts directed towards reducing cyberbullying should address adolescents’ self-esteem, as well as specific problem behaviors. Findings from a recent study revealed that developmental decreases in adolescents’ self-esteem predicted their subsequent involvement in cyberbullying both as a perpetrator and as a target. 81 Additionally, developmental increases in adolescents’ problem behaviors (for example, substance use, delinquency, and aggressive behaviors) also predicted their involvement in cyberbullying in subsequent grades. Building on the work of Patchin and Hinduja, 76 these results direct educators and health care professionals to focus on adolescents’ emotional well-being during the early high school years, paying particular attention to those adolescents who experience steep declines in their self-esteem, as well as adolescents who experience steep inclines in problem behaviors including substance use and delinquency.

In terms of parental relationships, study findings suggest that health care professionals and educators should work toward helping adolescents and their parents establish warm, nurturing relationships that include close adult monitoring. This is consistent with recent suggestions by the American Academy of Pediatrics that encourage parents to participate in open discussions with children and adolescents about their online behavior, as well as to implement the necessary safeguards to protect youth from engaging in cyberbullying behaviors. 100 Clearly, meaningful social connection is key to effective prevention and intervention efforts. 101 Finally, results from a recent study conducted by Hinduja and Patchin 102 suggest that adolescents’ socializing agents (ie, friends, family, and adults at school) play an important role in whether or not adolescents choose to cyberbully others. Surveying a random sample of 4,441 adolescents, the study results showed that adolescents who believed that several of their friends were involved with cyberbullying were more likely to cyberbully others themselves. These results suggest the need for prevention efforts designed around correcting the “misperceived” norm of cyberbullying. Additionally, the results also indicated that adolescents who believed that the adults in their lives would hold them accountable for their involvement with cyberbullying were less likely to participate in cyberbullying, thus suggesting the important role that adults play in the lives of adolescents in terms of reducing cyberbullying behaviors.

Beliefs about cyberbullying

Adolescents’ beliefs are important motivators of their behaviors. 103 Past work has shown that youths’ normative beliefs and attitudes about aggression are related to subsequent physical aggression, 104 , 105 as well as relational aggression. 106 More recently, research has been conducted to investigate how adolescents’ beliefs about aggression influence their involvement in cyberbullying behaviors. 107 , 108 Study results have indicated that youth who endorse attitudes supporting aggressive behaviors (for example, that it is okay to call some kids nasty names) are significantly more likely to report higher rates of cyberbullying compared to their peers. 107 , 108 A recent study conducted among American middle school students found that students who engaged in cyberbullying were more likely to endorse supportive attitudes related to aggressive behavior. 108 In addition to individual attitudes, classroom-level attitudes (although with somewhat weaker effects) were also predictive of cyberbullying behavior. 107 These results at the classroom level suggest the importance of establishing and maintaining positive classroom climates, reflecting respectful treatment of all individuals. Overall, these results suggest that prevention work in the school setting is important in order to reduce cyberbullying behavior.

Finally, past studies have shown that the frequency of online communication increases the risk of cyberbullying victimization and perpetration. 6 , 13 , 23 , 24 , 26 , 48 , 63 , 67 , 109 Subsequently, helping adolescents to self-regulate their time spent online may decrease their involvement with cyberbullying behaviors. This is particularly important given adolescents’ struggles to manage their impulses. 110

Past research has suggested that social support may be a powerful protective factor in mitigating the negative effects associated with cyberbullying. 70 , 93 In order for adolescents to receive the necessary support they need to reduce the associated harmful effects of cyberbullying, they must be willing to seek help. However, several studies suggest that targets of cyberbullying rarely seek help from adults at school (for example, from teachers). 19 , 26 , 111 Instead, the majority of adolescents are silent 111 and are not likely to tell adults when they are victimized via cyberbullying. 6 , 9 There are at least four possible reasons why adolescents are not likely to tell adults about their cyberbullying experiences. First, it could be that adolescents do not feel connected to adults, and subsequently do not seek their help when in distress. If this is true, then it is imperative that adults at school intentionally reach out to adolescents in an effort to establish trusting, caring relationships. This can be done through a variety of strategies including the development of engaging classroom activities, as well as activities designed around special adolescent interests. Prevention efforts could include helping adolescents establish and maintain meaningful social relationships with their peers. Adults at school can be trained to connect older peers with adolescents who are at risk for having fewer peer connections. A recent study conducted by Burton et al 108 found that adolescents who were more attached to their peers were less likely to be involved in cyberbullying. Effective mentoring programs could be another strategy used to increase positive peer attachments among adolescents. School mentoring programs can be developed to connect adolescents to caring mentors and/or adults. Health care providers and educators can routinely screen adolescents to identify those who do not have at least one meaningful relationship with a peer and/or an adult.

Another reason that adolescents may be reluctant to tell adults about their experiences related to cyberbullying may be that youth tend to tend to think that cyberbullying is not a serious issue, and thus, they do not need help. Research has found some support for this claim. For example, Agatston et al 112 found that adolescent males living in the US were less likely to view cyberbullying as a serious problem. A third reason why adolescents may not tell adults about cyberbullying may be that they do not consider the adults in their school to be helpful resources in addressing cyberbullying. 112 These results suggest that additional training may be needed for school personnel to identify effective ways to address cyberbullying in the school setting. Several good resources have been provided online for educators. 113 A fourth reason why adolescent targets may not be willing to seek help could be related to their increased feelings of shame and helplessness. 40 Letting targeted youth know it is not their fault may be one promising cognitive strategy that may increase adolescents’ likelihood to seek help. Recent findings from the Youth Voice Project 114 suggest that adolescents’ use of cognitive reframing strategies are effective tools that are likely to lead to positive outcomes for targeted youth.

Individual treatment is needed for all involved to effectively address cyberbullying. For example, adolescents can be trained to develop effective strategies to increase their self-control 115 and empathy towards others. 99 Recent research has also demonstrated the need for targets of cyberbullying to be trained in effective coping strategies. 116 Importantly, Bauman 117 suggests that counseling for the perpetrator needs to be restorative in nature and not punitive. Too often, schools tend to punish and isolate the perpetrator without any consideration for restoration with the target – a needed ingredient for optimizing adolescents’ subsequent outcomes. Given the associated feelings of isolation, it is important for counselors to help targets of cyberbullying establish and maintain meaningful connections with others.

Bystanders are an important part of intervention efforts. Similar to face-to-face bullying, there are often many peers who witness or are exposed to cyberbullying. Recent findings from the Youth Voice Project compared strategy effectiveness among adolescents’ self-strategies, peer strategies, and adult strategies in response to various forms of peer mistreatment. 114 Results from this large-scale study showed that peer strategies (or bystander actions) were much more effective in terms of leading to positive outcomes for targeted youth than were self- or adult strategies. 114 This was true for both traditional bullying and cyberbullying. Interestingly, the bystander actions that were most likely to lead to positive outcomes for targeted youth were not confrontational, but instead were quiet acts of support (ie, spent time with the targeted student, talked to them, encouraged them, listened to them, and called or messaged them at home). However, the Youth Voice Project data also revealed that over half (51%) of the mistreated youth reported that their peers “did nothing” about the situation and “ignored what was going on”. 114 Clearly, more research is needed to better understand the processes underlying positive bystander behavior.

What predicts positive bystander behavior?

A recent study conducted with Czech adolescents examined whether adolescents’ age, sex, self-esteem, tendency toward prosocial behavior, and problematic peer relationships influenced their support of cyberbullied peers. 35 The results showed that only adolescents’ tendency towards prosocial behavior positively predicted supportive bystander behavior. 35 This study also examined how contextual variables might influence adolescents’ bystander support of cyberbullied peers. Study findings showed that existing relationships with the target, distress experienced by witnessing the victimization, and direct appeal for help predicted positive, supportive bystander behavior. On the other hand, having a strong relationship with the perpetrator repressed supportive bystander behavior. These results are consistent with past work documenting the importance of empathy, as well as the importance of training adolescents to ask for help from their peers. Importantly, these results also underscore the significance of developing and maintaining prosocial relationships among adolescents. Recent researchers in Belgium used an experimental paradigm to investigate the effect of contextual variables on bystander actions in response to a hypothetical cyberbullying incident. 118 Their study results showed that among Flemish adolescents, bystanders were more likely to help the target when they perceived the cyberbullying to be more severe, which suggests that we need to help adolescents understand the seriousness of cyberbullying.

What predicts negative bystander behavior?

In a recent study conducted in Poland, researchers used an experimental paradigm to examine the individual factors that might influence adolescents’ negative bystander behavior in response to cyberbullying. 119 The results indicated that negative bystander behavior (as measured by the decision to forward a negative message about someone) was more likely to occur in private contexts, as compared to public contexts. For example, adolescents were likely to behave in more antisocial ways when they thought only one or a few observers would see their behavior (ie, private conditions). These findings suggest that it is important for adolescents to understand that in reality, their online behavior is seen by a wide audience and is, in fact, “public”. The results also showed that negative bystander behavior was more likely among adolescents who had previous experiences with cyberbullying perpetration. Finally, consistent with past work, study findings demonstrated that both affective and cognitive empathy reduces negative bystander behavior. Overall, the results suggest that educators, health care professionals, and caring adults should continue to promote adolescents’ prosocial relationships, affective and cognitive empathy, as well as help adolescents to seek out positive forms of social support. Although initial research has begun to examine the effect of bystanders in the context of cyberbullying, more work is needed to understand how bystander actions may influence the relationship between cyberbullying and associated health outcomes. Another recent study using an experimental paradigm to examine individual factors related to negative bystander behavior was conducted in Belguim. 118 Results from this study indicated that bystanders were more likely to “join in” on the bullying when the other bystanders were good friends as opposed to acquaintances. Consistent with past work, 114 sex-related effects were found, such that females were more likely to comfort and defend the target, give advice to the target, and report the incident. On the other hand, males were more likely to reinforce the cyberbullying by telling the perpetrator that they thought it was funny. 118 These sex-related effects indicate that adolescent males may require extra training related to providing positive support to peers who have been victimized via cyberbullying.

In sum, raising awareness among educators, health care professionals, parents, and adolescents regarding the serious nature of cyberbullying may be a first step in addressing the harmful effects of cyberbullying. Moreover, it is important for caring adults and mentors to proactively reach out to adolescents and establish meaningful relationships with them that persist over time. Additionally, training adults and adolescents in effective strategies to address cyberbullying is needed to mitigate the associated negative effects of cyberbullying. Finally, addressing adolescents’ beliefs around cyberbullying both at the individual and classroom level should be at the core of prevention and intervention efforts. 108 School counselors and health care providers may be in a prime position to initiate training for school personnel, parents, and adolescents alike. 120

When should prevention and intervention efforts begin?

It is important for researchers to begin looking at how younger children interface with technology. Cyberbullying prevention and intervention programs should target all grade levels. 121 The research is clear that cyberbullying begins before adolescence. 122 To date, however, the majority of studies investigating cyberbullying have primarily included teenagers ( Table 1 and Table 2 ). Although teenagers are an important population to study given their salient developmental concerns, 110 more work is needed to examine how younger adolescents (for example, 9–11-year-olds) are affected by cyberbullying experiences. Englander, from the MA Aggression Reduction Center (MARC; http://marccenter.webs.com/ ), has begun to study the prevalence of technology among younger children. Her work has shown that over 90% of children are already immersed online by the time they are 8 years old. This has implications for involvement in subsequent cyberbullying. For example, research has demonstrated that owning a “Smartphone” in elementary school increases a child’s risk for being involved with cyberbullying both as the target, as well as the perpetrator. 122 Devine and Lloyd 30 examined Internet use and psychological well-being among 10- and 11-year-old children living in Northern Ireland. Their results showed a moderate, significant relationship between cybervictimization and psychological well-being. Specifically, children who experienced more victimization online were likely to experience more negative affect, more loneliness, and poorer relationships with their parents and peers. Similarly, Jackson and Cohen 122 found a positive relationship between loneliness and cyberbullying victimization among 3rd through 6th graders. Further, cyberbullying victimization was related to fewer friendships, lower rates of optimism in describing peer relationships, and lower peer acceptance. Additional work is needed with this younger age group to help increase our understanding of the impact of cyberbullying on adolescent health.

In sum, research has demonstrated that cyberbullying victimization and perpetration have a significant detrimental impact on adolescents’ health ( Table 1 and Table 2 ). In fact, the studies reviewed herein suggest that cyberbullying is an emerging international public health concern, related to serious mental health concerns, with significant impact on adolescents’ depression, anxiety, self-esteem, emotional distress, substance use, and suicidal behavior. Moreover, cyberbullying is also related to adolescents’ physical health concerns.

It is important to note that the majority of studies investigating the relationship between cyberbullying behaviors and adolescent health have been correlational in nature. While correlational studies are an important first step to understanding the impact of cyberbullying, longitudinal studies are now needed to increase our understanding of how cyberbullying experiences affect adolescents’ health over time. By using longitudinal designs, we are able to test whether adolescents’ depressive symptoms, social anxiety, or suicidal tendencies related to cyberbullying are antecedents or consequences. For example, it is possible that depressive symptomology could either be an antecedent or an effect of cyberbullying victimization. Longitudinal study designs permit us to examine both of these possibilities with more clarity. As discussed in the section titled, “How do the developmental changes in risk factors affect subsequent cyberbullying?”, an emerging body of work has begun to use longitudinal designs to examine the risk factors related to increased involvement with cyberbullying perpetration and victimization over time. However, more longitudinal work is needed to increase our understanding of the temporal nature of variables related to cyberbullying experiences.

Findings from the current literature have significant implications for health care professionals, educators, and caring adults. First and foremost, the studies described throughout urge educators, counselors, and health care professionals to address cyberbullying when assessing adolescents’ physical and psychological health concerns. It is clear that adolescents who are involved in cyberbullying experiences require support. However, evidence suggests that the majority of adolescents do not seek help from adults when involved in cyberbullying. Therefore, it is important to take a proactive approach. Support could come from multiple professional communities that serve youth: educational (for example, professionals working in the schools); behavioral health (for example, clinicians treating adolescents with mental health concerns); and medical (for example, pediatricians asking about cyberbullying experiences during sick and well visits). Sensitive probing about cyberbullying experiences is warranted when addressing adolescent health issues such as depression, substance use, suicidal ideation, as well as somatic concerns. Routine screening techniques can be developed to assist in uncovering the harm endured through cyberbullying to help support adolescents recovering from associated trauma. Finally, the study findings described above also suggest a strong need for comprehensive, school-based programs directed at cyberbullying prevention and intervention. Education about cyberbullying could be integrated into school curriculums and the community at large, for example, by engaging adolescents in scholarly debates and community discussions related to cyberbullying legislation, accountability, and character.

The author reports no conflicts of interest in this work.

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COMMENTS

  1. Cyberbullying on social networking sites: A literature review and future research directions

    1. Introduction Cyberbullying is an emerging societal issue in the digital era [ 1, 2 ]. The Cyberbullying Research Centre [ 3] conducted a nationwide survey of 5700 adolescents in the US and found that 33.8 % of the respondents had been cyberbullied and 11.5 % had cyberbullied others.

  2. Cyberbullying and its influence on academic, social, and emotional

    1. Introduction Cyberbullying is defined as the electronic posting of mean-spirited messages about a person (such as a student) often done anonymously ( Merriam-Webster, 2017 ). Most of the investigations of cyberbullying have been conducted with students in elementary, middle and high school who were between 9 and 18 years old.

  3. Associations between social media and cyberbullying: a review of the

    Cyberbullying, a growing problem associated with social media use, has become a significant public health concern that can lead to mental and behavioral health issues and an increased risk of suicide. Cyberbullying has been associated with face-to-face confrontations, concern about going to school, and physical altercations ( 4 ).

  4. Cyberbullying Among Adolescents and Children: A Comprehensive Review of

    Results: The prevalence rates of cyberbullying preparation ranged from 6.0 to 46.3%, while the rates of cyberbullying victimization ranged from 13.99 to 57.5%, based on 63 references. Verbal violence was the most common type of cyberbullying. Fourteen risk factors and three protective factors were revealed in this study.

  5. Social Media and Cyberbullying

    Kowalski RM, et al. Bullying in the digital age: a critical review and meta-analysis of cyberbullying research among youth. Psychol Bull. 2014;140(4):1073-137. ... Craig W, et al. Social media use and cyber-bullying: a cross-national analysis of young people in 42 countries. J Adolesc Health. 2020;66(6s):S100-s108.

  6. Early detection of cyberbullying on social media networks

    Abstract. Cyberbullying is an important issue for our society and has a major negative effect on the victims, that can be highly damaging due to the frequency and high propagation provided by Information Technologies. Therefore, the early detection of cyberbullying in social networks becomes crucial to mitigate the impact on the victims.

  7. Cyberbullying: Concepts, theories, and correlates informing evidence

    In prior research, cyberbullying was characterized as occurring outside of school with negative interactions continuing into the next school day ... Social media and cyberbullying: Implementation of school-based prevention efforts and implications for social media approaches ... Paper presented at 'Etmaal van de Communicatiewetenschap ...

  8. Cyberbullying detection and machine learning: a systematic ...

    The rise in research work focusing on detection of cyberbullying incidents on social media platforms particularly reflect how dire cyberbullying consequences are, regardless of age, gender or location.

  9. (PDF) Cyberbullying in the World of Teenagers and Social Media:: A

    Independent Researcher in Social Media. Areas include research ethics, education, cyberbullying, health and privacy Abstract Cyberbullying amongst teenagers is a major issue, due to their...

  10. (PDF) Cyberbullying: A Review of the Literature

    20 TABLES AND FIGURES: Consequence Explanation References Consequence for victims Self-esteem/ emotional problems Cyberbullying leads to lower self-esteem and causes emotional problems amongst...

  11. An Improved Detection of Cyberbullying on Social Media Using ...

    According to social media cyberbullying statistics, ... It may seem this research paper is concentrated only on the Twitter dataset. The choice of the dataset was due to its easy availability and high popularity of the platform among other research works which makes it easier to compare the studies. Nevertheless, the methods discussed in this ...

  12. Cyberbullying on social media platforms among university students in

    The purpose of this paper is to explore the pervasiveness of cyberbullying among university students in an Arab community, its nature and venues, and their attitudes towards reporting cyberbullying in contrast to remaining silent. ... A 2016 report of the Cyberbullying Research Centre indicates that 33.8% of middle-and high-school students aged ...

  13. Adolescents on Social Media: Aggression and Cyberbullying

    This paper deals with the study of secondary school students' behavior on social media. The parameters characterizing teenagers' usage of social media — their activity, intensity, motives, and self-presentation — are analyzed with respect to gender, age, and social psychological factors. ... Cyberbullying Via Social Media. Journal of ...

  14. (PDF) Cyber Bullying

    Cyberbullying is the usage of computerized transmission to threaten an individual, typically by forwarding messages of an intimidating or menacing nature. Digital devices and electronic media have ...

  15. Teens and Cyberbullying 2022

    Report December 15, 2022 Teens and Cyberbullying 2022 Nearly half of U.S. teens have been bullied or harassed online, with physical appearance being seen as a relatively common reason why. Older teen girls are especially likely to report being targeted by online abuse overall and because of their appearance By Emily A. Vogels How we did this

  16. Cyberbullying detection: advanced preprocessing techniques & deep

    Social media have become a very viable medium for communication, collaboration, exchange of information, knowledge, and ideas. However, due to anonymity preservation, the incidents of hate speech and cyberbullying have been diversified across the globe. This intimidating problem has recently sought the attention of researchers and scholars worldwide and studies have been undertaken to ...

  17. Frontiers

    Results: The prevalence rates of cyberbullying preparation ranged from 6.0 to 46.3%, while the rates of cyberbullying victimization ranged from 13.99 to 57.5%, based on 63 references. Verbal violence was the most common type of cyberbullying. Fourteen risk factors and three protective factors were revealed in this study.

  18. Accurate Cyberbullying Detection and Prevention on Social Media

    This paper describes a system for automatic detection and prevention cyberbullying considering the main characteristics of cyberbullying such as Intention to harm an individual, Repeatedly and over time and using abusive curl language or hate speech using supervised machine learning.

  19. Cyberbullying: What is it and how can you stop it?

    Social Media and Internet Children Teens 51 Cite This Article Abramson, A. (2022, September 7). Cyberbullying: What is it and how can you stop it? https://www.apa.org/topics/bullying/cyberbullying-online-social-media Cyberbullying can happen anywhere with an internet connection.

  20. Cyberbullying on social media under the influence of COVID‐19

    Cyberbullying on social media under the influence of COVID‐19 Daisy Mui Hung Kee, 1 * Maryam Ammar Lutf Al‐Anesi, 1 , * and Sarah Ammar Lutf Al‐Anesi 1 , * Author information Copyright and License information PMC Disclaimer Associated Data Data Availability Statement Go to: Abstract

  21. 'It depends': what 86 systematic reviews tell us about what strategies

    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.

  22. Cyberbullying Experience Through The Social Media

    Research done by BBC has found that this lack of natural light hinders the body's production of the hormone melanin, which enables sleep. ... Social Media Cyberbullying and Its Effects On Mental Health Pages: 3 ... top-notch essay and term paper samples on various topics. Additional materials, such as the best quotations, synonyms and word ...

  23. Cyberbullying and its influence on academic, social, and emotional

    The data were collected using the Revised Cyber Bullying Survey, which evaluates the frequency and media used to perpetrate cyberbullying, and the College Adjustment Scales, which evaluate three aspects of development in college students. ... Fraping - where a person accesses the victim's social media account and impersonates them in an attempt ...

  24. Current perspectives: the impact of cyberbullying on adolescent health

    Adolescents in the United States culture are moving from using the Internet as an "extra" in everyday communication (cyber utilization) to using it as a "primary and necessary" mode of communication (cyber immersion). 1 In fact, 95% of adolescents are connected to the Internet. 2 This shift from face-to-face communication to online communication...