cyberbullying research paper example

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• v.22(1); 2023 Feb

Cyberbullying: next‐generation research

Elias aboujaoude.

1 Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford CA, USA

Matthew W. Savage

2 School of Communication, San Diego State University, San Diego CA, USA

Cyberbullying, or the repetitive aggression carried out over elec­tronic platforms with an intent to harm, is probably as old as the Internet itself. Research interest in this behavior, variably named, is also relatively old, with the first publication on “cyberstalking” ap­pearing in the PubMed database in 1999.

Over two decades later, the broad contours of the problem are generally well understood, including its phenomenology, epidemiology, mental health dimensions, link to suicidality, and disproportionate effects on minorities and individuals with developmental disorders 1 . Much remains understudied, however. Here we call for a “next generation” of research addressing some important knowledge gaps, including those concerning self‐­cyberbullying, the bully‐victim phenomenon, the bystander role, the closing age‐based digital divide, cyberbullying subtypes and how they evolve with technology, the cultural specificities of cyberbullying, and especially the management of this behavior.

Defined as the anonymous online posting, sending or otherwise sharing of hurtful content about oneself, “self‐cyberbullying” or “digital self‐harm” has emerged as a new and troubling manifestation of cyberbullying. Rather than a fringe phenomenon, self‐cyberbullying is thought to affect up to 6% of middle‐ and high‐school students 2 . Is this a cry for help by someone who might attempt “real” self‐harm or even suicide if not urgently treated? Is it “attention‐seeking” in nature, meant to drive Internet traffic in a very congested social media landscape where it can be hard to get noticed and where “likes” are the currency of self‐worth? Research is needed to better characterize self‐cyberbullying, including how it relates to depression and offline self‐harm and suicide.

The bully‐victim phenomenon refers to the permeable boundaries between roles that can make it relatively easy for a cyberbullying victim to become a cyberbully and vice versa. Unlike traditional bullying, visible markers of strength are not a requirement in cyberbullying. Assuming the identity of the cyberbully is known, all that the victims need to attack back and become cyberbullies themselves is a digital platform and basic digital know‐how. Do cyberbullying victims feel in any way “empowered” by this permeability, as some do express in clinical settings? And does knowledge that perpetrators can be attacked back have any deterrent effect on them, or is the bi‐directional violence that can ensue an unmitigated race to the bottom that further impairs well‐being?

What of the bystander role? Depending on the platform, the audience witnessing a cyberbullying attack can potentially be limitless – attacks that go viral are an extreme example of this. While this can magnify the humiliation inflicted on the victim, it also introduces the possibility of enlisting bystanders to protect victims and push back against perpetrators. Research examining how to leverage bystanders as part of anti‐cyberbullying interventions would have significant management and public health utility.

Recent scholarship has brought attention to cyberbullying beyond the young age group. What had been called the “digital divide”, which in this context refers to the notion that children and adolescents are more active online and therefore at higher risk, has narrowed to the point where a significant risk of cyberbully­ing now appears to exist among college students and perhaps adults overall. Cyberbullying is no longer a middle‐ and high‐school problem, as suggested by a 30‐country United Nations‐sponsored survey that recruited nearly 170,000 youth up to 24 years of age and found that 33% of them had been victims of that behavior 3 . To better protect against cyberbullying and implement age‐appropriate interventions, new research should better delineate the upper limits of the high‐risk cyberbullying age bracket, if they exist.

There is also insufficient research into the culturally‐specific dimensions of cyberbullying. Co‐authoring analyses reveal that the most influential cyberbullying scholarship comes from the US, and that the top 5 universities in publication productivity are in the European Union 4 . Given the different relationship to violence across cultures and the diverging definitions of, and reactions to, trauma worldwide, a broader culturally‐centered research perspective is essential for a more thorough understanding of cyberbullying's global impact.

As we “zoom out” and investigate across cultures, we should also “zoom in” on the specific cyberbullying behavior. Are all cy­berbullying attacks similar in terms of prevalence, perpetrator and victim profiles, short‐ and long‐term consequences, and manage­­ment strategies? Several forms of cyberbullying have been iden­tified 5 , but their similarities and differences require elucidation, es­­­pecially as technology continues to change and new forms emerge. Therefore, future research should compare diverse behaviors, such as cyberstalking, “excluding” (deliberately leaving someone out), “doxing” (revealing sensitive information about the victim), “fraping” (using the victim's social media account to post inappropriate content under the victim's name), “masquerading” (creating a fake identity with which to attack the victim), “flaming” (posting insults against the victim), and sex‐based cyberbullying through the non‐consensual sending of sexual text messages or imagery. To better understand and address cyberbullying, we must explore its existing subtypes – some of which have only been described in blogs – and, as technology evolves, its emerging forms.

Most urgently, the lack of agreement upon “best practices” for the management of cyberbullying must be remedied. Expanding access to psychiatric and psychological care – given the mental health dimension of cyberbullying – is imperative, as is a better understanding of school‐based interventions, which remain the most popular management approach.

Data from school‐based studies suggest that programs which adopt a broad, ecological approach to the school‐wide climate and which include specific actions at the student, teacher and family levels are more effective than those delivered solely through classroom curricula or social skills trainings 6 . However, the best meta‐analytic evidence for school‐based programs demonstrates mostly short‐term effects 7 , while long‐term data suggest small benefits 8 . Further, success appears more likely when programs target cyberbullying specifically as opposed to general violence prevention 7 , and when they are delivered by technology‐savvy content experts as opposed to teachers 8 . Evidence also suggests that programs are most successful when they provide informational support through interactive modalities (e.g., peer tutoring, role playing, group discussion), and when they nurture stakeholder agency (e.g., offer quality teacher training programs, engage parents in program implementation) 9 .

Future research into cyberbullying management should expand on these findings and examine how management interfa­ces with the legislative process and with law enforcement when it comes to illegal behavior, including privacy breeches and serious threats.

Much has been learned about cyberbullying, but much remains to be explored. The knowledge gaps are all the more challenging given that Internet‐related technologies evolve at a breakneck pace and in a way that reveals new exploitable vulnerabilities. A­long with the previously cited statistic that no less than 33% of young people worldwide have been victimized 3 , this should give the field added urgency to “keep up” and investigate some under‐studied areas that are critical to a more nuanced understanding of cyberbullying and its effective management.

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A review on deep-learning-based cyberbullying detection.

1. Introduction

• We present a DL-based cyberbullying defense ecosystem with the help of a taxonomy. We also discuss data representation, models and frameworks for DL techniques.
• We compare several RNN, CNN, attention, and their fusion-based cyberbullying detection studies in the existing literature.
• We analyze several text and image datasets extracted from social media and virtual platforms related to cyberbullying detection.
• We identify the challenges and open issues related to cyberbullying.

2. Related Works

3. methodology, 4. data representation techniques, 4.1. text data representation, 4.1.1. one-hot encoding, 4.1.2. tf-idf, 4.1.3. word2vec, 4.1.4. glove, 4.1.5. elmo, 4.1.6. fasttext, 4.1.7. bert, 4.1.8. efficacy of various embeddings for detecting cyberbullying, 4.2. image data representation, 4.2.1. cognitive image representation, 4.2.2. bsp representation, 4.2.3. bio-inspired model representation, 4.2.4. mps representation, 4.2.5. deep neural networks-based image representation [ 83 ], 4.2.6. optical character recognition (ocr), 5. deep-learning-based models, 5.1. deep neural network (dnn), 5.2. boltzmann machines (bms), 5.3. deep belief network (dbn), 5.4. deep autoencoder (dae), 5.5. generative adversarial network (gan), 5.6. recurrent neural network (rnn), 5.7. long short-term memory.

• ⊗ —element wise multiplication;
• ⊕ —element wise addition;
• C t = current cell memory ;
• C t − 1 = previous cell memory ;
• o t = output gate ;
• f t = forget gate ;
• σ = sigmoid function ;
• w , b = weight vectors ;
• h t − 1 = previous cell output ;
• x t = input vector ;
• h t = current cell output .

5.8. Convolutional Neural Network (CNN)

5.9. hybrid models (lstm-cnn, cnn-lstm), 5.10. attention-based model, 5.10.1. transformers, 5.10.2. bert (bidirectional encoder representations from transformers), 5.10.3. hierarchical attention networks (han), 5.10.4. convolutional neural networks with attention (cnn-att), 5.10.5. long short-term memory networks with attention (lstm-att), 5.10.6. gated recurrent units with attention (gru-att).

5.11. Performance Comparison of DL Models in Cyberbullying Detection

6. dl in cyberbullying detection, 7. deep learning frameworks, applicability of different dl frameworks, 8. datasets for experiments, 9. challenges, open issues, and future trends, 9.1. issues in dl.

• Require a large amount of dataset: Large volumes of labeled data are required for DL. For example, the creation of self-driving cars involves millions of photos and hundreds of hours of video [ 198 ]. It is commonly known that data preparation consumes 80–90% of the time spent on ML development. Furthermore, even the strongest DL algorithms will struggle to function without good data and present weak performance to handle biased and unclean data during model training [ 199 ].
• High computational power: DL takes a lot of computational power. The parallel design of high-performance GPUs is ideal for DL. When used in conjunction with clusters or cloud computing, this allows development teams to cut DL network time for training from weeks to hours or less [ 198 ].
• Reasoning of prediction unexplainable: DL result prediction follows the Black-Box testing approach. Thus, it is not capable of making any explainable predictions. Since DL’s hidden weight and activation are non-interpretable, its predictions are considered as non-explainable [ 200 ].
• Security issue: Preventing the DL models from security attacks is the biggest challenge nowadays. Based on the occurring time, there are two types of security attacks. One is poisoning attack, which occurs during the training period, and another one is evasion attack, which occurs during interference (after training). By corrupting the data with malicious examples, poisoning attacks compromise the training process. On the other hand, evasion attacks use adversarial examples to confuse the entire classification process [ 201 ].
• Models are not adaptive: In the present world, data are very dynamic. Data are changing due to various factors, which may be constantly changing, such as location, time, and many other factors. However, DL models are built using a defined set, which is called the training dataset. Later, the performance of the model is measured by the data, which also comes from the same distribution of the training data, and eventually, the model performs well. Later, the same model may start performing poorly due to the changing the characteristics of the data, which are not entirely different, but have some variations from the training data. This is difficult to manage in DL to retrain the old models.

9.2. Challenges in Cyberbullying detection

• Cultural diversity for cyberbullying: Language is one of the important parts of the culture of a nation. Since cyberbullying has become a common problem among different nations, we may not expect a good prediction model by using a dataset of one nation and testing over the dataset of another culturally varied nation.
• Language challenge: Capturing context and analyzing the sentiment from different types of sentences is a difficult task and challenging work for cyberbullying detection. For example, “The image that you have sent so irritated me and I would rather not contact with you any longer!” is not easy to detect as cyberbullying without investigating from a rationale factor, albeit that model shows negative sentiment [ 26 ].
• Dataset challenge: Retrieving data from social media is not an easy task, as it relates to private information. Moreover, social media sites do not share user data publicly. Due to these issues, it is hard to gather quality data from social sites, which causes the lack of quality data to improve learning. Another challenging task is to annotate or label the data because they require a domain expert to label the corpus [ 202 ].
• Data representation challenge: Setting up an effective cyberbullying-detection system is difficult due to the need for human interaction and the nature of cyberbullying. Furthermore, the nature of cyberbullying is challenging to identify in the cyberbullying detection problem. The vast majority of the exploratory works directly identified bullying words in social media. However, separating content-based features have their own difficulties. For the absence of appropriate information, the performance of the model might decay [ 203 ].
• Natural Language Processing (NLP) challenges: The biggest challenge in natural language processing is understanding the meaning of the text. The relevant task is to build the right vocabulary, link the various components of the vocabulary, establish context, and extract semantic meaning from the data [ 204 ]. Misspelling and ambiguous expressions are other challenges that are very difficult to solve for the machine.
• Reusability of pre-trained model for sentiment analysis and cyberbullying: Although cyberbullying detection and sentiment analysis are related tasks, these two tasks have significant differences from each other; therefore, the pre-trained model of one task is likely to be difficult to use to predict another task. Sentiment analysis involves determining the overall emotional tone of a text, where the sentence is positive, negative, or neutral. On the contrary, cyberbullying detection involves identifying specific patterns of harmful words. Yet, there are some sentiment analysis approaches that can be used to identify cyberbullying. Atoum et al. [ 205 ] proposed an approach for detecting cyberbullying using sentiment analysis techniques. Nahar et al. [ 206 ] presented a novel method for identifying online bullying on social media sites from sentiment analysis. Dani et al. [ 207 ] presented a novel framework for supervised learning that uses sentiment analysis to identify cyberbullying. Overall, while sentiment analysis models may be helpful for cyberbullying detection, they cannot be directly reused without significant modifications and additional training. Cyberbullying detection (i.e., yes/no classes) largely needs to identify negative words, which are used to harass a person, while sentiment analysis has three different classes (i.e., negative, positive, and neutral) where negative patterns are part of the problem. In this case, positive and neutral categories are also dominant class labels. Since the nature of the outputs is different in two different problems, we cannot completely reuse one pre-trained model for other cases.

9.3. Future Trends

• Multilingual and multimedia content: In current times, social media and other virtual platforms are widely used among different levels of users in terms of age group, culture, language, taste, education, etc. Since social media is a vital platform for propagating cyber harassment, users may use multilingual and multimedia content; therefore, we may put more attention on building efficient cyberbullying detection systems for multilingual and multimedia content.
• Cyberbullying detection-specific word embedding: In recent times, researchers are introducing different domain specific word-embedding techniques, because these platforms produce accurate results for relevant sets of vocabularies. For example, Med-BERT is used for health-domain-based BERT-aware embedding systems. In this connection, researchers may propose a specialized word-embedding system for cyberbullying detection problems.
• Cyberbullying detection in SMS and email: Users are concerned with combating cyberbullying problems, which largely propagate through social media platforms. However, future researchers may put more attention on investigating Short Message Service (SMS)- and email-based cyberbullying detection methods.
• Cyberbullying impact on mental health: Cyberbullying may leave a long-term impact on the mental status of an individual. Some may take a life-threatening step or commit self-injury to curb the severity of the harassment and take death for granted. Therefore, mental health researchers can consider this issue as a timely topic and introduce different methods to fight against cyber harassment.
• Use of cutting-edge deep learning: With the advancement of deep-learning-based methods, we may introduce more subtle and delicate techniques to detect cyberbullying problems. For example, stacked and multi-channel CNN or Bi-LSTM-based cyberbullying-based frameworks or their advanced version or hybridization of these models may produce more sophisticated solutions to counter the problems.

10. Conclusions

Author contributions, data availability statement, conflicts of interest.

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Hasan, M.T.; Hossain, M.A.E.; Mukta, M.S.H.; Akter, A.; Ahmed, M.; Islam, S. A Review on Deep-Learning-Based Cyberbullying Detection. Future Internet 2023 , 15 , 179. https://doi.org/10.3390/fi15050179

Hasan MT, Hossain MAE, Mukta MSH, Akter A, Ahmed M, Islam S. A Review on Deep-Learning-Based Cyberbullying Detection. Future Internet . 2023; 15(5):179. https://doi.org/10.3390/fi15050179

Hasan, Md. Tarek, Md. Al Emran Hossain, Md. Saddam Hossain Mukta, Arifa Akter, Mohiuddin Ahmed, and Salekul Islam. 2023. "A Review on Deep-Learning-Based Cyberbullying Detection" Future Internet 15, no. 5: 179. https://doi.org/10.3390/fi15050179

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Understanding Bullying and Cyberbullying Through an Ecological Systems Framework: the Value of Qualitative Interviewing in a Mixed Methods Approach

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• Faye Mishna   ORCID: orcid.org/0000-0003-2538-826X 1 ,
• Arija Birze   ORCID: orcid.org/0000-0002-1988-8383 1 &
• Andrea Greenblatt   ORCID: orcid.org/0000-0002-6964-8193 1  

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Recognized as complex and relational, researchers endorse a systems/social-ecological framework in examining bullying and cyberbullying. According to this framework, bullying and cyberbullying are examined across the nested social contexts in which youth live—encompassing individual features; relationships including family, peers, and educators; and ecological conditions such as digital technology. Qualitative inquiry of bullying and cyberbullying provides a research methodology capable of bringing to the fore salient discourses such as dominant social norms and otherwise invisible nuances such as motivations and dilemmas, which might not be accessed through quantitative studies. Through use of a longitudinal and multi-perspective mixed methods study, the purpose of the current paper is to demonstrate the ways qualitative interviews contextualize quantitative findings and to present novel discussion of how qualitative interviews explain and enrich the quantitative findings. The following thematic areas emerged and are discussed: augmenting quantitative findings through qualitative interviews, contextualizing new or rapidly evolving areas of research, capturing nuances and complexity of perspectives, and providing moments for self-reflection and opportunities for learning.

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Introduction

Bullying and cyberbullying are increasingly recognized as complex phenomena that are considered relationship problems (Mishna et al., 2021a ; Pepler et al., 2010 ; Pepler, 2006 ; Spears et al., 2009 ). Appreciating that individuals are embedded in and both shape and are shaped by systems of relationships (Bronfenbrenner & Morris, 2007 ), researchers often endorse an ecological systems framework as paramount and comprehensive in examining bullying and cyberbullying phenomena Footnote 1 (Espelage, 2014 ; Newman et al., 2018 ; Thornberg, 2015 , 2018 ). According to this approach, individuals are embedded in and affected by interconnected and layered systems (Bronfenbrenner, 1979 , 1992 ). Children’s social-emotional development at school is consequently shaped not only by children’s relationships with their teachers and peers, but also by the interconnections between these relationships and the other layers of social ecology, all of which are considered to contribute to social behavioral patterns (O'Moore & Minton, 2005 ). Bullying and cyberbullying are examined across the nested social contexts in which youth live—encompassing individual features, peer relationships, school, family, and ecological climate such as societal norms and conditions as well as online technology (Cross et al., 2015 ; Johnson, 2010 ; Nesi et al., 2018 ). An ecological systems framework is considered an overarching approach that many theories complement and within which they fit (Bauman & Yoon, 2014 ).

The purpose of the current paper is to demonstrate the contributions of qualitative research in understanding the phenomena of bullying and cyberbullying and enriching and complementing the findings of quantitative methodology (Creswell & Creswell, 2018 ). Qualitative inquiry of bullying and cyberbullying provides a research methodology capable of bringing to the fore salient discourses and otherwise invisible nuances that might not be accessed through quantitative studies (Dennehy et al., 2020 ).

There are advantages to utilizing mixed methods in conducting research on various topics including cyberbullying (Creswell & Creswell, 2018 ). When engaging with complex phenomena such as cyberbullying, conceptual and methodological multiplicity offers distinct insights into research questions (McKim, 2017 ; Thornberg, 2011 ). When quantitative and qualitative research are used in combination, it is possible to obtain deeper as well as more comprehensive and accurate understanding of young people’s experiences, which increases the likelihood of informing strategies and responses that can effectively address the needs of children and adolescents (Crivello et al., 2009 ; Darbyshire et al., 2005 ; Fevre et al., 2010 ). The quality of findings may be strengthened when researchers use mixed methods because the data are triangulated (Crivello et al., 2009 ). Data generated through diverse research methods can both complement and contradict each other, which offers an opportunity to better understand the complexities of cyberbullying (Hemming, 2008 ). While quantitative approaches strive for objectivity by examining general concepts, such as cyberbullying, and parceling those concepts into specific, concrete, and understandable behaviors (Fevre et al., 2010 ), qualitative interviews give voice to children and youth, enabling them to express their thoughts and feelings about themselves, their relationships, environments, and the world in which they live (Mishna et al., 2004 ; Chaumba, 2013 ; Dennehy et al., 2020 ; Patton et al., 2017 ).

Through qualitative interviewing, we can step outside the bounds of adult thinking, gaining insights and discovering unanticipated differences in the perceptions of adults and children (Dennehy et al., 2020 ; O’Farrelly, 2021 ). To understand the phenomena of bullying and cyberbullying and inform effective prevention and intervention strategies, it is argued, children’s own views, “are at the heart of these efforts” (O’Farrelly, 2021 , p. 43). Thus, we present findings from the qualitative component of our Canadian federally funded mixed methods longitudinal study on cyberbullying from the perspectives of school-aged youth and their parents and teachers, entitled Motivations for Cyber Bullying: A Longitudinal and Multi-Perspective Inquiry Footnote 2 (Mishna et al., 2016 ).

Background Study Description

The objectives of our longitudinal mixed methods study were to (1) explore youth experiences and perspectives and their parents’ and teachers’ conceptions of cyberbullying; (2) explore how youth and adults view the underlying motivations for cyberbullying; (3) document the prevalence rates of cyberbullying victimization, witnessing, and perpetration; (4) identify risk and protective factors for cyberbullying involvement; and (5) explore social, mental health, and health consequences of cyberbullying among children and youth aged 9 to 18 (grades 4, 7, and 10) over 3 years.

In addressing the objectives, we use an explanatory sequential mixed methods design (Creswell & Creswell, 2018 ). The study comprised a 2-phase data collection approach in which we first collected the quantitative data and then used findings from the first phase to design and plan the qualitative data phase. The quantitative findings informed both our selection of interview participants and the focus of questions we wanted to explore further in the interviews. The overall intent of the qualitative interviews was to enrich and expand upon the quantitative findings and perhaps generate and explore similarities and contradictions (Creswell & Creswell, 2018 ). In the current paper, we briefly review key quantitative findings. We then discuss the qualitative findings and how they provide more depth and insight and demonstrate the complexities of bullying and cyberbullying motivations, behaviors, and attitudes. In so doing, we present novel discussions of how the qualitative interviews augment the quantitative findings.

Participants

Three participant groups were included in the baseline study sample: (1) students in 4th ( n  = 160), 7th ( n  = 243), and 10th ( n  = 267) grades; (2) their teachers ( n  = 103); and (3) their parents ( n  = 246). A stratified random sampling strategy was utilized to select participants. First, a random sample of 19 schools was drawn from one of the largest school boards in North America. Schools were stratified into three categories of need (low, medium, and high) based on an index developed by the school board that ranked schools on external challenges to student achievement (Toronto District School Board, 2014 ). This stratification ensured representation of ethno-cultural and socioeconomic diversity—factors that potentially impact access to Information and Communication Technologies (ICTs), experiences of cyberbullying, and the manifestation of negative outcomes (Lenhart et al., 2015 ; Steeves & Marx, 2014 ). In year 3 of the study, 10 additional schools were recruited for participation to follow those students transitioning from elementary/middle school to middle/secondary school. A total of 29 schools participated in the study. All students in the selected grades at the original participating schools were invited to participate, as were their parents and teachers.

Participating students and their parents provided data in all 3 years of the study, while matching teachers provided data in year 1 only (as student participants’ teachers changed each year). All three participant groups completed quantitative questionnaire packages, and a sub-sample of each group participated in individual interviews. Quantitative data were collected from students and parents in each year of the study, while qualitative data were collected during years 1 and 3, to allow for enough time to elapse for changes in perceptions of cyberbullying to emerge.

Quantitative Measures and Analysis

In year 1, students completed a 45–60-min quantitative questionnaire package in the school setting, while parents completed a questionnaire package by mail. Questionnaires for teachers, which took approximately 45–60 min to complete, were administered in the participating schools. This study utilized several quantitative measures, including standardized measures and measures developed specifically for the study. Student, parent, and teacher surveys obtained information related to experiences with bullying/cyberbullying (Mishna et al., 2012 ; Unpublished Survey), socio-demographics, and Information and Communication Technology (ICT) use. Standardized measures assessing student mental health, health, social, and behavioral issues included Child Behavior Check List (Achenbach, 2001a ), Teacher Report Form (Achenbach, 2001b ), Youth Self Report Form (Achenbach, 2001c ), Self-Perception Profile for Children (Harter, 1985b ), Self-Perception Profile for Adolescents (Harter, 2012 ), Social Support Scale for Children (Harter, 1985a ), and Social Support Behaviors Scale (Vaux et al., 1987 ).

Descriptive analyses were conducted to calculate frequencies for categorical variables and means and standard deviations for continuous variables. We summarized socio-demographic variables among participants in each grade level (4, 7, 10). Items for each outcome scale (e.g., Social Support Scale for Children) were summed to calculate total or subscale scores for each measure.

Findings on Prevalence and Reporting

The quantitative findings in the larger study (Mishna et al., 2015 ) show that rates of cyber witnessing were higher than cyberbullying and victimization at each assessment. In year 1, 24.2 percent reported cyber witnessing, 10.7 percent cyber victimization, and 2.9 percent cyberbullying. In year 2, 21.5 percent reported cyber witnessing, 7.6 percent cyber victimization, and 1.6 percent cyberbullying. In year 3, 25.1 percent reported cyber witnessing, 10.8 percent cyber victimization, and 2.5 percent cyberbullying. Similarly, rates of witnessing traditional bullying were higher than perpetration and victimization at each assessment. In year 1, 53.0% reported witnessing traditional bullying, 23.5% victimization, and 7.8% perpetration. In year 2, 42.6% reported witnessing traditional bullying, 17.3% victimization, and 4.3% perpetration. In year 3, 35.7% reported witnessing traditional bullying, 19.2% victimization, and 5.4% perpetration (Mishna et al., 2015 ). Of note, nearly half of all students (48.3%), who reported cyberbullying involvement in our survey, reported that they had not told an adult about what was happening online (Mishna et al., 2015 ). Moreover, 69.5% of students reported that cyberbullying and physical bullying are equally serious, and 64.5% believed that cyberbullying and “real” life verbal bullying are also equally serious (Mishna et al., 2015 ). These quantitative results serve as a springboard for the following discussion of qualitative findings, demonstrating that qualitative interviews reveal nuanced similarities and differences in the views of adults and youth, elucidating important interconnections among the levels of the ecological system (Mishna et al., 2004 , 2009 ; Dennehy et al., 2020 ).

Qualitative Interview Data Collection and Analysis

Student participants in 4th grade ( n  = 20), 7th grade ( n  = 21), and 10th grade ( n  = 16) in the qualitative sub-sample were purposively selected for interviews from the larger quantitative sample, based on gender, grade, school need level, and whether they reported bullying/cyberbullying victimization, perpetration, or witnessing. After selecting student participants, their teachers ( n  = 30) and parents ( n  = 50) were invited to participate in interviews. Interviews lasted approximately 1 h, ranging in length from 30 to 90 min. All year 1 interviews (with students, parents, and teachers) took place in the school setting and utilized a semi-structured interview guide. Following preliminary analysis, this interview guide was refined for use in the year 3 follow-up phone interviews with the students and parents. Areas explored with students comprised understanding of cyberbullying and how it compares with traditional bullying, experiences of online aggression, and others’ attitudes and responses. Questions were informed by existing literature and the research team’s considerable experience. Parent and teacher interviews included questions on their awareness and understanding of cyberbullying, their child or student’s involvement in cyberbullying, links between cyber and traditional bullying, support, and their responses to cyberbullying.

Using a grounded theory inquiry, data were concurrently analyzed and theorized through constant comparison (Birks & Mills, 2015 ; Corbin & Strauss, 2008 ). Through this iterative process, the team used initial interview data and theoretical categories to inform and refine subsequent interview guides and data collection (Charmaz, 2014 ). The team members individually coded a portion of interviews to establish preliminary analytic focuses and inductively identify preliminary themes. Consistent with a grounded theory approach, no hypotheses guided data analysis and coders sought to bracket their biases through reflexive journaling and team discussions of assumptions (Corbin & Strauss, 2008 ). During team meetings, each interview was collectively coded, building upon, revising, and/or removing codes proposed by the initial coder. Emerging categories were developed and expanded. Axial coding promoted connections within and between categories and subcategories and enabled synthesis and explanation (Birks & Mills, 2015 ; Charmaz, 2014 ; Corbin & Strauss, 2008 ). Numerous preliminary codes were identified based on emerging themes that were generated and discussed. A holistic “middle-order” approach to coding resulted in a condensed number of initial codes (Saldaña, 2015 ). Axial coding was then used to identify connections within and between themes and subthemes (Birks & Mills, 2015 ; Charmaz, 2006 , 2014 ; Corbin & Strauss, 2008 ). Through this iterative process of open, holistic, and focused coding, key themes emerged related to the understanding of traditional and cyberbullying according to the perspectives of the students, parents, and teachers. Measures were employed to ensure trustworthiness and authenticity. Prolonged engagement over the 3 years of the study ensured thick descriptions of the youth and adult narratives (Lietz & Zayas, 2010 ). Rigor was established through documentation for auditing purposes (Padgett, 2008 ). Trustworthiness and transferability were further ensured through reflexive journaling, bracketing, and dense descriptions (Corbin & Strauss, 2008 ).

While we use examples from our published manuscripts derived from our study entitled, “Motivations of Cyberbullying,” in the current manuscript, we identify new thematic areas and demonstrate how our qualitative interviews complement our quantitative findings. In analyzing findings across the study publications and datasets, we have not previously drawn the conclusions. The unique contribution of the current manuscript is the use of findings of previous publications to generate broader conclusions about the benefits of a mixed-methods approach (qualitative interviews and quantitative survey data) that makes visible the connections across ecological systems levels.

In discussing how qualitative research contributes to understanding bullying and cyberbullying and complements quantitative findings, the following new thematic areas are discussed: augmenting quantitative findings through qualitative interviews, contextualizing new or rapidly evolving areas of research, capturing nuances and complexity of perspectives, and providing moments for self-reflection and opportunities for learning.

Augmenting Quantitative Findings Through Qualitative Interviews

By examining process, context, and meaning for participants, qualitative methodology can augment quantitative findings. Quantitative methodology establishes outcomes and causal relationships and puts forth generalization and predictions (Yilmaz, 2013 ). Our background study which was a longitudinal multi-informant mixed methods study (Tashakkori et al., 1998 ) used grounded theory (Strauss & Corbin, 1998 ) and a longitudinal quantitative design to aid understanding of nuances related to cyberbullying (Mishna et al., 2009 ). In creating opportunities for the voices of young people to be heard (Carroll & Twomey, 2020 ; Gilgun & Abrams, 2002 ), qualitative methodology is especially useful for phenomena that are largely unstudied and/or rapidly evolving, such as cyberbullying, by explicating process and a holistic understanding and directions for future research (Mishna & Van Wert, 2013 ; Gilgun & Abrams, 2002 ).

In our paper, “Benchmarks and bellwethers in cyberbullying: The relational process of telling” Footnote 3 (Mishna et al., 2020 ), the qualitative analysis revealed relational processes among students that occurred when they considered whether to tell adults about their bullying and cyberbullying experiences. As noted above, almost half of the students who reported cyberbullying involvement relayed that they had not told an adult. Qualitative findings, however, exposed complex interactions that informed their decision-making processes. Reticent about speaking with adults, students turned to friends. It emerged that in addition to sharing, telling friends often served as a bellwether to gauge whether to proceed and report the situation to an adult. Often minimizing the severity of their ordeal, many students had decided against informing adults, frequently mentioning their concern about making a “big deal.” Participant interviews further revealed that media reports of high-profile cases involving cyberbullying can serve as benchmarks through which to assess the severity of their own personal experiences. The qualitative findings in our study helped to contextualize the quantitative data by unpacking and making visible the reasoning and contributing factors, thus increasing understanding of what informs youth’s decisions regarding whether and who to tell about cyberbullying involvement. By augmenting the quantitative data detailing the proportion of youth who do not tell adults, particulars attained through qualitative interview data help to inform and direct prevention and intervention strategies that are concrete and actionable for addressing the more challenging aspects of cyberbullying involvement and disclosure. In offering insights on the relational dynamics among peers and between youth and adults with respect to cyberbullying, the qualitative analysis gave voice to these interconnected layers of the youths’ ecological environment.

Contextualizing New or Rapidly Evolving Areas of Research

While cyberbullying is no longer considered a new phenomenon, the rapid development of technology is continually altering the cyber landscape, creating a need for perpetual knowledge generation (Odgers & Jensen, 2020 ; Rosa et al., 2019 ) and for evolving definitions, measurements, and responses (Spears et al., 2009 ). Moreover, rapid and ongoing technological advances create unique challenges for practitioners, policy makers, and researchers, in remaining current and responding to cyberbullying (George & Odgers, 2015 ; Jäger et al., 2010 ). With youth at the forefront of technological advances in many ways, qualitative methodology is well suited to elicit the experiences and perspectives of young people in promoting in-depth understanding of youth cultures, dynamics, and processes (Thornberg & Knutsen, 2011 ).

The data collection for our background study occurred between 2012 and 2014, during the early stages of attention to and research on sexting (sending and receiving sexually explicit images, videos, and text among youth). In the quantitative questionnaires, we included one question related to sexting for students in grades 7 and 10 and their parents and teachers. Our quantitative survey found that 15.6% of students in grades 7 and 10 had seen nude or sexual photos of friends, family, boyfriend, girlfriend, or other romantic partner online or over a cell phone. Furthermore, 27.8% of teachers had witnessed or were aware of their students viewing sexually explicit images, video, or text on cell phones at school. The data indicated that digital sending and receiving of sexually explicit images, video, or text was a new phenomenon among youth participants in grades 7 and 10 in a rapidly changing digital environment.

We did not explicitly inquire about sexting in the interviews with students, parents, and teachers. Rather, we asked participants about the students’ negative experiences with cyber technology. During analysis of the interview data, however, sexting emerged as a new and pertinent phenomenon among youth, which generated knowledge about rapidly evolving cyber dynamics that warranted further attention and inspired a paper entitled, “Gendered and sexualized bullying and cyberbullying: Spotlighting girls and making boys invisible” (Mishna et al., 2021b ). The qualitative interview data in this instance confirmed our quantitative findings on sexting among youth and allowed us to delve into the complex and nuanced ways participants articulated sexting behaviors along gender lines that both reinforced and were reinforced by gendered sociocultural norms and pressures. In student accounts, boys’ presence and participation in cyberbullying were frequently invisible, such as the non-consensual sharing of sexual images. Blamed for their poor choices, girls were spotlighted and their behavior problematized through negative characterizations. The participants’ focus on girls as responsible for the gendered cyberbullying and non-consensual sharing of images corresponds with how youth are typically educated about digital technologies through an “online safety model” with the focus on youth protecting themselves and avoiding “risky” activities (Johnson, 2015 ). As such, our findings provided context for this rapidly evolving environment that then allowed us to draw links between individual cyberbullying behaviors, understanding and articulation of these behaviors, and the broader influence of patriarchal structures (Mishna et al., 2021b ). The qualitative findings underscored the need to consider key factors that go beyond individual characteristics and behaviors and to develop education and prevention and intervention strategies that address sociocultural norms and values. The qualitative findings stimulated new research endeavors and collaborations with community organizations and academics.

Capturing Nuances and Complexity of Perspectives

Bullying and cyberbullying are exceedingly complex and must be studied within the contexts of the involved youth as well as within the larger social context of youth (Cross et al., 2015 ; Dennehy et al., 2020 ; Johnson & Puplampu, 2008 ; Sainju, 2020 ; Thornberg, 2011 ). An ecological systems framework is appropriate as it provides insight into the interconnected relationships among varying aspects and social layers of an individual’s world (Bronfenbrenner, 1979 ). While quantitative research considers and articulates context, qualitative interviews provide an occasion to engage with the richness of students’ perspectives, thoughts, and feelings about themselves and their social worlds (Mishna et al., 2004 ) and allow for a deeper understanding of youth culture and social processes from the vantage point of young people (Chaumba, 2013 ; Dennehy et al., 2020 ; Spears et al., 2009 ; Thornberg & Knutsen, 2011 ). Although qualitative studies are generally bound by a particular timeframe, participants bring their life histories and cumulative experiences to the research engagement (Phoenix et al., 2003 ), which can generate a fulsome and holistic understanding of cyberbullying, taking into consideration individual, family, peer, school, cyber, and sociocultural conditions over time.

Qualitative interview data allow for an interpretive approach that draws upon patterns of understanding, similarity, and contradiction, thereby teasing out underlying assumptions that shape how people define and assess experiences and phenomena such as bullying and cyberbullying (Mishna et al., 2020 , 2021a ). In our paper entitled “Looking Beyond Assumptions to Understand Relationship Dynamics in Bullying” Footnote 4 (Mishna et al., 2021a ), analysis of the qualitative interview data exposed persistent and pervasive assumptions about bullying linked to sociocultural norms and understanding of gender. These assumptions shaped participants’ understanding and conclusions of bullying and cyberbullying experiences, behavior, and motivations. Focusing on the visible hurt and injuries associated with physical bullying, participants tended to make comments such as “you’ll heal in a few days,” whereas they noted that with verbal bullying, the mental anguish “might stay for a long term.” This viewpoint that physical bullying was not a relationship problem appeared to be linked to gender stereotypes and social norms regarding the “natural” behavior of girls and boys. These gendered assumptions led participants to suggest that addressing bullying among girls was “complicated” and ongoing, whereas addressing physical bullying among boys was “simpler” and faster, a finding similar to that of Eriksen and Lyng ( 2018 ) who described participants’ descriptions of bullying among boys as “undramatic.” These assumptions appeared to preclude participants from discussing physical bullying among boys in a manner that acknowledged the physical bullying involvement as entrenched in relationship dynamics.

Qualitative interviewing provides an opportunity for participants to express their views and ideas when discussing the topic of interest which can elicit novel conclusions and nuances. As an example, at times, youth who claimed not to have involvement with cyberbullying may go on to describe situations that actually seemed to fit the definition of cyberbullying. In our Spotlighting Girls paper, many participant reports aligned with stereotypes regarding differences in how boys and girls bully others. These stereotypes were shared, however, even when they contradicted participants’ own experiences. For instance, similar to other research findings (Eriksen & Lyng, 2018 ), one participant described a boy as using “guilt trips” as a bullying tactic, yet described boys as only bullying physically. Consequently, relational aggression among boys often goes unnoticed and remains invisible. Similarly, the same behavior displayed by both girls and boys was discounted in boys and highlighted in girls. Boys’ behaviors were often not considered to be bullying because they were positioned as within the bounds of masculine gender norms. For example, one girl reported that “mostly girls, not boys,” bully “because boys would just go over and do some physical things... [Girls would] post embarrassing stuff about the person and do that kind of stuff” (p. 410). It is possible therefore that such actions by boys were not identified as bullying and thus underreported in the quantitative surveys while captured in the interviews. Discrepancies emerged in how cyberbullying had been reported in quantitative measures and how it was described in the interviews. This indicates that qualitative interviews can complement quantitative findings by revealing the complexities and ramifications of social experiences which are not reported in quantitative surveys.

The critical role of witnessing in bullying and cyberbullying is well documented (Salmivalli, 2010 , 2014 ; Spadafora et al., 2020 ; Volk et al., 2014 ). Social experiences related to witnessing are also complex, and bystander decision-making and responses impact both the process and outcomes of bullying incidents (Salmivalli et al., 2011 ). Qualitative research can offer youth the opportunity to explore and explain the motivations and factors they consider in determining whether to intervene, specifically the social costs and benefits of intervening (Spadafora et al., 2020 ). Our qualitative interviews similarly added youth voices concerning the dilemmas they faced in considering whether and how to respond based on emotional and contextual factors (Mishna et al., 2021b ), thus providing nuanced perspectives that serve to augment the quantitative findings related to bystander responses.

Providing Moments for Self-reflection and Opportunities for Learning

Qualitative methodologies are recognized as providing participants opportunities to self-reflect in the context of being listened to empathically (Birch & Miller, 2000 ; Wolgemuth et al., 2015 ). According to a systematic review of quantitative, qualitative, and mixed-methods studies conducted with children and adolescents, participation was mainly considered to be beneficial (Crane & Broome, 2017 ). Negative responses to participating in the research included feeling anxious and upset (Crane & Broome, 2017 ). Research indicates that despite describing negative effects of participating, children and youth reported that overall it was more positive to participate in the research (Crane & Broome, 2017 ) or described the emotional pain they experienced as beneficial in various ways, for example, as “emotionally cleansing” (Wolgemuth et al., 2015 , p. 366). The qualitative research process offers participants the opportunity to come to new understandings and can reveal evolving thoughts within participant narratives (Birch & Miller, 2000 ; Wolgemuth et al., 2015 ). Qualitative processes are iterative and involve probing questions that can prompt dynamic reflection by participants (Wolgemuth et al., 2015 ). Birch and Miller ( 2000 ) explain that they “use the term therapeutic to represent a process by which an individual reflects on, and comes to understand previous experiences in different—sometimes more positive—ways that promote a changed sense of self” (p. 190).

Recognizing the potential risks in research with children and youth (Mishna et al., 2004 ; Crane & Broome, 2017 ), we informed the students in our study of the possible risks should they decide to participate, such as the possibility that they would become upset as we were asking them about hurtful matters, and the limits to confidentiality. Anticipating that some of the questions could lead to a participant becoming distressed or disclosing potentially sensitive or upsetting information, we put in place a protocol (approved by the university and school board research ethics boards) to identify and offer support for students in distress (Mishna et al., 2016 ).

Corresponding with previous research, the reflexivity of sharing their narratives and views seemed to contribute to some participants coming to a different understanding of their experiences. Such reflection was evident in our interviews with students and their parents and teachers. When asked whether he had witnessed cyberbullying, for example, a boy reflected that only in being asked about cyberbullying in the interview did he recognize the behavior as cyberbullying: “When I think about it now, I actually did a few times. I didn’t feel that it’s cyber bullying, I wasn’t thinking that it’s a huge deal. It’s basically a few arguments between people on Facebook, like writing things about each other in public, not in private, chats.”

In another example, a parent reconsidered her views during the interview. This parent first commented that girls and women are “more vindictive” than boys and men, who, she explained, have “your spat, you get over it, and you move on.” After reflecting on her assumptions, she wondered how much of this widely held view of the behavior “is just media driven because I guess the victims that we see on the news, at least in Canada, have been girls, right?… but that doesn’t say that boys aren’t also being bullied.” Similarly, a girl contemplated her assumptions after first casting boys in a favorable light in contrast to girls. In commenting that girls bully each other because of appearance, she praised boys, “because usually they don’t tend to worry about those things...They’re proud of themselves, and they don’t pick on other people. They’re good with what they have.” After pondering these stated differences between boys and girls, this girl surmised, “I think it’s from when we were little because those Barbie dolls are super skinny. We wanted to have blonde hair, blue eyes, and be like Barbie. I think it’s just how maybe we were raised.” Another girl, who asserted that while cyberbullying occurred with equal frequency among boys and girls, added that it was not “a big thing” for boys, in contrast to girls who, “would show it off more, be like oh yah, blah, blah, blah.” Rather than concluding that this difference indicated that cyberbullying was not a big deal for boys, however, this girl attributed the difference between boys and girls to dominant masculinity norms. She asserted that “guys kind of hide it in more” and explained that “they don’t want to show that they’re weak because guys tend to be, they think that they’re very strong, kind of thing.” The evolving perspectives throughout this and the previous exchanges demonstrate the process of deepened understanding that can occur because of qualitative interviewing.

Such new understanding can inspire a desire to act and make change through community engagement. A girl explained that the research was the first time she had spoken with anyone about cyberbullying. This girl’s appraisal of her participation is consistent with findings in which participants may be motivated to take part in research for the opportunity to effect and advocate for change and help others (Cutcliffe & Ramcharan, 2002 ; Wolgemuth et al., 2015 ). She remarked that participating had been a helpful process which led her to,

think of different ways that I could help someone else if I see it happening… Just talking about it makes you think about what could cause it, what could make someone bully someone else. It makes you realize how it could make someone feel. Also, talking about how there isn’t really a support system at school. It makes me want to go and talk to someone to organize it, because it does happen a lot and I know it affects a lot of people

The inclusion of qualitative interviews in mixed methods research brings forth new information about content, process, and meaning that is otherwise not visible. By engaging youth voices as well as adult perspectives through both quantitative measures and qualitative interviews in the mixed methods study discussed in this manuscript, entitled Motivations for Cyberbullying, understanding of bullying and cyberbullying was advanced, thus enriching the quantitative methodology. The findings of the interviews extended knowledge related to bullying and cyberbullying in the following ways, which can inform “bottom-up research and intervention efforts” (Dennehy et al., 2020 , p. 10): augmenting quantitative findings, contextualizing new or rapidly evolving areas of research, capturing nuances and complexity of perspectives, and providing moments for self-reflection and opportunities for learning.

Qualitative research constitutes a significant venue through which to amplify the voices of children and youth (Dennehy et al., 2020 ) and ensures that children and youth’s experiences of the world are represented in understanding social phenomena (Mishna et al., 2004 ; Carroll & Twomey, 2020 ; Chaumba, 2013 ; Dennehy et al., 2020 ; Patton et al., 2017 ). According to Dennehy and colleagues ( 2020 ), engaging youth as co-researchers in cyberbullying research may enhance efforts to ethically and earnestly amplify youth voices. A synthesis by Elsaesser et al. ( 2017 ) supports the view that focusing on collaboratively working with youth to understand and safely navigate the cyber world through education and empowerment is more effective than interventions aimed at restricting ICT use without involving youth. Through quantitative measures and qualitative interviews, our mixed methods study examined participant perspectives regarding bullying and cyberbullying on the various ecological systems levels across the students’ lives. The use of mixed methods facilitated a dialogue between the participant responses to both methodologies, thus highlighting the salience of the overlapping influence and interactions among the systems levels. Such complex and nuanced understanding is necessary to inform meaningful prevention and intervention strategies to address bullying and cyberbullying.

According to the United Nations Convention on the Rights of the Child (Assembly UG, 1989 ), children and youth have the right to discuss their views and experiences. The Convention states that all children have the right to protections, provisions, participation, and non-discrimination (Assembly UG, 1989 ). Participation entails the right for children to express themselves and have a voice in situations that have to do with and affect them. The importance of listening to children’s voices underscores the limits of adult proxies in representing children’s emotional and social worlds (O’Farrelly, 2021 ). Bullying and cyberbullying fundamentally violate these protections, silence children’s voices, and compromise their healthy development (Greene, 2006 ). Our mixed methods study through quantitative measures and qualitative interviews facilitated a dialogue between the participant responses in both methodologies. This interaction of data types maximizes the voices of and collaboration with participants as well as knowledge generation.

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Different terms are used to describe the same approach (e.g., social-ecological framework, ecological systems framework, ecological theory, ecological perspectives). For the purposes of this paper, the term ecological systems framework is used.

All additional references to this research study will be shortened to “Motivations for Cyberbullying.”

All additional references to this paper will be shortened to “Benchmarks and Bellwethers paper.”

All additional references to the paper will be shortened to “Relationship Dynamics paper.”

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Acknowledgements

We would like to acknowledge first and foremost the Toronto District School Board for their utmost commitment to participating in the study, as well as each school for their dedication to both data collection and ensuring that the mental health needs of students that were identified through the study were addressed. We would like to thank the students, parents, and teachers for sharing their experiences with us. We would like to thank the research assistants, without whom we could not have completed this study.

This research was supported by a grant from the Social Sciences and Humanities Research Council of Canada: Grant Account Number: 410–2011-1001.

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Mishna, F., Birze, A. & Greenblatt, A. Understanding Bullying and Cyberbullying Through an Ecological Systems Framework: the Value of Qualitative Interviewing in a Mixed Methods Approach. Int Journal of Bullying Prevention 4 , 220–229 (2022). https://doi.org/10.1007/s42380-022-00126-w

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SYSTEMATIC REVIEW article

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

• 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.

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.

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.

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

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.

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.

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.

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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.

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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

Table of contents.

• Acknowledgments
• Methodology

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.

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%).

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%).

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.

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.

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.

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.

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

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.

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 .

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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