cyberbullying research paper conclusion

Advertisement

Cyberbullying detection and machine learning: a systematic literature review

• Published: 24 July 2023
• Volume 56 , pages 1375–1416, ( 2023 )

Cite this article

• Vimala Balakrisnan 1 &
• Mohammed Kaity 1  

1050 Accesses

Explore all metrics

The rise in research work focusing on detection of cyberbullying incidents on social media platforms particularly reflect how dire cyberbullying consequences are, regardless of age, gender or location. This paper examines scholarly publications (i.e., 2011–2022) on cyberbullying detection using machine learning through a systematic literature review approach. Specifically, articles were sought from six academic databases (Web of Science, ScienceDirect, IEEE Xplore, Association for Computing Machinery, Scopus, and Google Scholar), resulting in the identification of 4126 articles. A redundancy check followed by eligibility screening and quality assessment resulted in 68 articles included in this review. This review focused on three key aspects, namely, machine learning algorithms used to detect cyberbullying, features, and performance measures, and further supported with classification roles, language of study, data source and type of media. The findings are discussed, and research challenges and future directions are provided for researchers to explore.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save.

• Get 10 units per month
• Download Article/Chapter or eBook
• 1 Unit = 1 Article or 1 Chapter
• Cancel anytime

Price includes VAT (Russian Federation)

Instant access to the full article PDF.

Rent this article via DeepDyve

Institutional subscriptions

Similar content being viewed by others

Can Machine Learning Really Detect Cyberbullying?

Harnessing the Power of Interdisciplinary Research with Psychology-Informed Cyberbullying Detection Models

Cyber Analyzer—A Machine Learning Approach for the Detection of Cyberbullying—A Survey

Explore related subjects.

• Artificial Intelligence

https://trends.google.com/trends/explore?date=2011-01-01%202022-12-31&q=deep%20learning&hl=en .

Agrawal S, Awekar A (2018) Deep learning for detecting cyberbullying across multiple social media platforms. In European Conference on Information Retrieval (pp. 141–153). Springer, Cham

Aizenkot D, Kashy-Rosenbaum G (2018) Cyberbullying in WhatsApp classmates’ groups: evaluation of an intervention program implemented in israeli elementary and middle schools. New Media & Society 20(12):4709–4727

Article   Google Scholar  

Akhter MP, Zheng JB, Naqvi IR, Abdelmajeed M, Sadiq MT (2020) Automatic Detection of Offensive Language for Urdu and Roman Urdu. IEEE Access 8:91213–91226.

Aldhyani TH, Al-Adhaileh MH, Alsubari SN (2022) Cyberbullying identification system based deep learning algorithms. Electronics 11(20):3273

Al-Garadi MA, Hussain MR, Khan N, Murtaza G, Nweke HF, Ali I, …, Gani A (2019) Predicting cyberbullying on social media in the big data era using machine learning algorithms: review of literature and open challenges. IEEE Access 7:70701–70718

Al-garadi MA, Varathan KD, Ravana SD (2016) Cybercrime detection in online communications: the experimental case of cyberbullying detection in the Twitter network. Comput Hum Behav 63:433–443

Al-Harigy LM, Al-Nuaim HA, Moradpoor N, Tan Z (2022) Building towards Automated Cyberbullying Detection: A Comparative Analysis. Computational Intelligence and Neuroscience, 2022

Alom Z, Carminati B, Ferrari E (2020) A deep learning model for Twitter spam detection. Online Social Networks and Media 18:100079

Alpaydin E (2010) Introduction to machine learning, 2nd edn. MIT Press

Ates EC, Bostanci E, Guzel MS (2021) Comparative performance of machine learning algorithms in cyberbullying detection: using turkish language preprocessing techniques. arXiv preprint arXiv :2101.12718

Ayo FE, Folorunso O, Ibharalu FT, Osinuga IA (2020) Machine learning techniques for hate speech classification of twitter data: state-of-the-art, future challenges and research directions. Comput Sci Rev 38:100311

Balakrishnan V (2015) Cyberbullying among young adults in Malaysia: the roles of gender, age and internet frequency. Comput Hum Behav 46:149–157

Balakrishnan V, Khan S, Arabnia HR (2020a) Improving cyberbullying detection using Twitter users’ psychological features and machine learning. Computers & Security 90:101710

Balakrishnan V, Khan S, Arabnia HR (2020b) Improving cyberbullying detection using Twitter users’ psychological features and machine learning. Computers & Security 90:101710

Balakrishnan V, Khan S, Fernandez T, Arabnia HR (2019) Cyberbullying detection on twitter using Big Five and Dark Triad features. Pers Individ Differ 141, 252–257.

Bretschneider U, Wöhner T, Peters R (2014) Detecting online harassment in social networks.

Buan TA, Ramachandra R (2020) Automated Cyberbullying Detection in Social Media Using an SVM Activated Stacked Convolution LSTM Network. In Proceedings of the 2020 the 4th International Conference on Compute and Data Analysis (pp. 170–174)

Camerini AL, Marciano L, Carrara A, Schulz PJ (2020) Cyberbullying perpetration and victimization among children and adolescents: a systematic review of longitudinal studies. Telematics Inform 49:101362

Chatzakou D, Kourtellis N, Blackburn J, De Cristofaro E, Stringhini G, Vakali A (2017) Mean birds: Detecting aggression and bullying on twitter. In Proceedings of the 2017 ACM on web science conference (pp. 13–22)

Chavan VS, Shylaja SS (2015) Machine learning approach for detection of cyber-aggressive comments by peers on social media network. In 2015 International Conference on Advances in Computing, Communications and Informatics (ICACCI) (pp. 2354–2358). IEEE

Cheng L, Guo R, Silva YN, Hall D, Liu H (2021) Modeling temporal patterns of cyberbullying detection with hierarchical attention networks. ACM/IMS Trans Data Sci 2(2):1–23

Cheng L, Li J, Silva YN, Hall DL, Liu H (2019) Xbully: Cyberbullying detection within a multi-modal context. In Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining (pp. 339–347)

Chen Y, Zhou Y, Zhu S, Xu H (2012) Detecting offensive language in social media to protect adolescent online safety. In 2012 International Conference on Privacy, Security, Risk and Trust and 2012 International Confernece on Social Computing (pp. 71–80). IEEE

Dadvar M, De Jong F (2012) Cyberbullying detection: a step toward a safer internet yard. In Proceedings of the 21st International Conference on World Wide Web (pp. 121–126)

Dadvar M, Jong FD, Ordelman R, Trieschnigg D (2012) Improved cyberbullying detection using gender information. In Proceedings of the Twelfth Dutch-Belgian Information Retrieval Workshop (DIR 2012) . University of Ghent

Dadvar M, Trieschnigg D, Ordelman R, de Jong F (2013) Improving cyberbullying detection with user context. In European Conference on Information Retrieval (pp. 693–696). Springer, Berlin, Heidelberg

Dey R, Bag S, Sarkar RR (2021) Identification of stable housekeeping genes for normalization of qPCR data in a pathogenic fungus. J Microbiol Methods 180:106106

Google Scholar  

Dinakar K, Picard R, Lieberman H (2015) Common sense reasoning for detection, prevention, and mitigation of cyberbullying. In IJCAI International Joint Conference on Artificial Intelligence .

Dinakar K, Reichart R, Lieberman H (2011) Modeling the detection of textual cyberbullying. In Proceedings of the International Conference on Weblog and Social Media 2011

Divyashree VH, Deepashree NS (2016) An effective approach for cyberbullying detection and avoidance. International Journal of Innovative Research in Computer and Communication Engineering , 14

Djuraskovic O, Cyberbullying Statistics F (2020) and Trends with Charts: First Site Guide; 2020. Available from: https://firstsiteguide.com/cyberbullying-stats/

Elmezain M, Malki A, Gad I, Atlam ES (2022) Hybrid deep learning model–based prediction of images related to Cyberbullying. Int J Appl Math Comput Sci 32(2):323–334

MATH   Google Scholar  

Fahrnberger G, Nayak D, Martha VS, Ramaswamy S (2014) SafeChat: A tool to shield children’s communication from explicit messages. In 2014 14th International Conference on Innovations for Community Services (I4CS) (pp. 80–86). IEEE

Fang Y, Yang S, Zhao B, Huang C (2021) Cyberbullying detection in social networks using bi-gru with self-attention mechanism. Information 12(4):171

Foong YJ, Oussalah M (2017), September Cyberbullying system detection and analysis. In 2017 European Intelligence and Security Informatics Conference (EISIC) (pp. 40–46). IEEE

Galán-García P, Puerta JGDL, Gómez CL, Santos I, Bringas PG (2016) Supervised machine learning for the detection of troll profiles in twitter social network: application to a real case of cyberbullying. Log J IGPL 24(1):42–53

MathSciNet   Google Scholar  

García-Recuero Á (2016) Discouraging abusive behavior in privacy-preserving online social networking applications. In Proceedings of the 25th International Conference Companion on World Wide Web (pp. 305–309)

Ge S, Cheng L, Liu H (2021) Improving cyberbullying detection with user interaction. In Proceedings of the Web Conference 2021 (pp. 496–506)

Goodboy AK, Martin MM (2015) The personality profile of a cyberbully: examining the Dark Triad. Comput Hum Behav 49:1–4

Goodfellow I, Bengio Y, Courville A (2016) Deep learning. MIT Press

Haidar B, Chamoun M, Serhrouchni A (2017a) Multilingual cyberbullying detection system: Detecting cyberbullying in Arabic content. In 2017 1st Cyber Security in Networking Conference (CSNet) (pp. 1–8). IEEE

Haidar B, Chamoun M, Serhrouchni A (2017b) A multilingual system for cyberbullying detection: arabic content detection using machine learning. Adv Sci Technol Eng Syst J 2(6):275–284

Hani J, Nashaat M, Ahmed M, Emad Z, Amer E, Mohammed A (2019) Social media cyberbullying detection using machine learning. Int J Adv Comput Sci Appl 10(5):703–707

Hinduja S, Patchin JW (2010) Bullying, cyberbullying, and suicide. Archives of suicide research 14(3):206–221

Hosmer DW Jr, Lemeshow S, Sturdivant RX (2013) Applied Logistic Regression. Wiley

Hosseinmardi H, Mattson SA, Rafiq RI, Han R, Lv Q, Mishra S (2015b) Detection of cyberbullying incidents on the instagram social network. arXiv preprint arXiv:1503.03909

Hosseinmardi H, Mattson SA, Rafiq RI, Han R, Lv Q, Mishr S (2015a) Prediction of cyberbullying incidents on the instagram social network. arXiv preprint arXiv:1508.06257

Hosseinmardi H, Rafiq RI, Han R, Lv Q, Mishra S (2016) Prediction of cyberbullying incidents in a media-based social network. In 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) (pp. 186–192). IEEE

Huang Q, Singh VK, Atrey PK (2014) Cyber bullying detection using social and textual analysis. In Proceedings of the 3rd International Workshop on Socially-Aware Multimedia (pp. 3–6)

Hutter F, Kotthoff L, Vanschoren J (2019) Automated machine learning: methods, systems, challenges. Springer

Kaity M, Balakrishnan V (2019) An automatic non-english sentiment lexicon builder using unannotated corpus. J Supercomputing 75(4):2243–2268

Kelleher JD, Tierney B, Tierney B (2018) Data science: an introduction. CRC Press

Kitchenham B, Charters S (2007) Guidelines for performing systematic literature reviews in software engineering. Tech Rep EBSE 1:1–57

Koutsou A, Tjortjis C (2018) Predicting hospital readmissions using random forests. IEEE J Biomedical Health Inf 22(1):122–130

Kumar A, Nayak S, Chandra N (2019) Empirical analysis of supervised machine learning techniques for Cyberbullying detection. In International Conference on Innovative Computing and Communications (pp. 223–230). Springer, Singapore

Kumar A, Sachdeva N (2020) Multi-input integrative learning using deep neural networks and transfer learning for cyberbullying detection in real-time code-mix data. Multimedia Systems

Kumar A, Sachdeva N (2021) Multimodal cyberbullying detection using capsule network with dynamic routing and deep convolutional neural network. Multimedia Syst, 1–10

LeCun Y, Bengio Y, Hinton G (2015) Deep Learn Nat 521(7553):436–444

Li W, Li X (2021) Cyberbullying among college students: the roles of individual, familial, and cultural factors. Int J Environ Res Public Health 18(11):1–17

López-Vizcaíno MF, Nóvoa FJ, Carneiro V, Cacheda F (2021) Early detection of cyberbullying on social media networks. Future Generation Computer Systems 118:219–229

Lu N, Wu G, Zhang Z, Zheng Y, Ren Y, Choo KKR (2020) Cyberbullying detection in social media text based on character-level convolutional neural network with shortcuts. Concurrency and Computation: Practice and Experience, e5627

Maity K, Sen T, Saha S, Bhattacharyya P (2022) MTBullyGNN: a graph neural network-based Multitask Framework for Cyberbullying Detection. IEEE Transactions on Computational Social Systems

Malik CI, Radwan RB (2020) Adolescent victims of cyberbullying in Bangladesh- prevalence and relationship with psychiatric disorders. Asian J Psychiatr 48:101893

Mangaonkar A, Hayrapetian A, Raje R (2015) Collaborative detection of cyberbullying behavior in Twitter data. In 2015 IEEE international conference on electro/information technology (EIT) (pp. 611–616). IEEE

Manning CD, Raghavan P, Schütze H (2008) Introduction to Information Retrieval. Cambridge University Press

McEvoy MP, Williams MT (2021) Quality Assessment of systematic reviews and Meta-analyses of physical therapy interventions: a systematic review. Phys Ther 101(4):pzaa226

Mercado RNM, Chuctaya HFC, Gutierrez EGC (2018) Automatic cyberbullying detection in spanish-language social networks using sentiment analysis techniques. Int J Adv Comput Sci Appl 9(7):228–235

Monteiro RP, Santana MC, Santos RM, Pereira FC (2022) Cyberbullying victimization and mental health in higher education students: the mediating role of perceived social support. J interpers Violence, 1–23

Nahar V, Al-Maskari S, Li X, Pang C (2014) Semi-supervised learning for cyberbullying detection in social networks. In Australasian Database Conference (pp. 160–171). Springer, Cham

Nahar V, Unankard S, Li X, Pang C (2012) Sentiment analysis for effective detection of cyber bullying. Asia-Pacific Web Conference

Nandhini BS, Sheeba JI (2015) Online social network bullying detection using intelligence techniques. Procedia Comput Sci 45:485–492

Niu M, Yu L, Tian S, Wang X, Zhang Q (2020) Personal-bullying detection based on Multi-Attention and Cognitive Feature. Autom Control Comput Sci 54(1):52–61

Noviantho, Isa SM, Ashianti L (2018) Cyberbullying classification using text mining. In Proceedings - 2017 1st International Conference on Informatics and Computational Sciences , ICICoS 2017

Patil C, Salmalge S, Nartam P (2020) Cyberbullying detection on multiple SMPs using modular neural network. Advances in Cybernetics, Cognition, and machine learning for Communication Technologies. Springer, Singapore, pp 181–188

Chapter   Google Scholar  

Pawar R, Raje RR (2019) Multilingual Cyberbullying Detection System. In 2019 IEEE International Conference on Electro Information Technology (EIT) (pp. 040–044). IEEE

Pires TM, Nunes IL (2019) Support vector machine for human activity recognition: a comprehensive review. Artif Intell Rev 52(3):1925–1962

Pradhan A, Yatam VM, Bera P (2020) Self-Attention for Cyberbullying Detection. In 2020 International Conference on Cyber Situational Awareness, Data Analytics and Assessment (CyberSA) (pp. 1–6). IEEE

Pérez PJC, Valdez CJL, Ortiz MDGC, Barrera JPS, Pérez PF (2012) MISAAC: Instant messaging tool for cyberbullying detection. In Proceedings of the 2012 International Conference on Artificial Intelligence , ICAI 2012 (pp. 1049–1052)

Rafiq RI, Hosseinmardi H, Han R, Lv Q, Mishra S (2018) Scalable and timely detection of cyberbullying in online social networks. In Proceedings of the 33rd Annual ACM Symposium on Applied Computing (pp. 1738–1747)

Raisi E, Huang B (2018) Weakly supervised cyberbullying detection with participant-vocabulary consistency. Social Netw Anal Min 8(1):38

Reynolds K, Kontostathis A, Edwards L (2011) Using machine learning to detect cyberbullying. In 2011 10th International Conference on Machine learning and applications and workshops (Vol. 2, pp. 241–244). IEEE

Rosa H, Matos D, Ribeiro R, Coheur L, Carvalho JP (2018) A “deeper” look at detecting cyberbullying in social networks. In 2018 International Joint Conference on Neural Networks (IJCNN) (pp. 1–8). IEEE

Rosa H, Pereira N, Ribeiro R, Ferreira PC, Carvalho JP, Oliveira S, Coheur L, Paulino P, Veiga Simão AM, Trancoso I (2019) Automatic cyberbullying detection: A systematic review. Computers in Human Behavior, 93, 333–345

Salawu S, He Y, Lumsden J (2017) Approaches to automated detection of cyberbullying: a survey. IEEE Trans Affect Comput.

Sanchez H, Kumar S (2011) Twitter bullying detection. ser. NSDI , 12 (2011), 15

Shah N, Maqbool A, Abbasi AF (2021) Predictive modeling for cyberbullying detection in social media. J Ambient Intell Humaniz Comput 12(6):5579–5594

Singh A, Kaur, M (2020) Intelligent content-based cybercrime detection in online social networks using cuckoo search metaheuristic approach [Article]. J Supercomput 76(7):5402–5424

Singh VK, Ghosh S, Jose C (2017) Toward multimodal cyberbullying detection. In Proceedings of the 2017 CHI Conference Extended Abstracts on Human Factors in Computing Systems (pp. 2090–2099)

Soni D, Singh VK (2018) See no evil, hear no evil: Audio-visual-textual cyberbullying detection. Proceedings of the ACM on Human-Computer Interaction , 2 (CSCW), 1–26

Squicciarini A, Rajtmajer S, Liu Y, Griffin C (2015) Identification and characterization of cyberbullying dynamics in an online social network. In Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2015 (pp. 280–285)

Sugandhi R, Pande A, Agrawal A, Bhagat H (2016) Automatic monitoring and prevention of cyberbullying. Int J Comput Appl 8:17–19

Tahmasbi N, Rastegari E (2018) A socio-contextual approach in automated detection of public cyberbullying on Twitter. ACM Trans Social Comput 1(4):1–22

Tan SH, Zou W, Zhang J, Zhou Y (2020) Evaluation of machine learning algorithms for prediction of ground-level PM2.5 concentration using satellite-derived aerosol optical depth over China. Environ Sci Pollut Res 27(29):36155–36170

Tarwani S, Jethanandani M, Kant V (2019) Cyberbullying Detection in Hindi-English Code-Mixed Language Using Sentiment Classification. In International Conference on Advances in Computing and Data Sciences (pp. 543–551). Springer, Singapore

Tomkins S, Getoor L, Chen Y, Zhang Y (2018) A socio-linguistic model for cyberbullying detection. In 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) (pp. 53–60). IEEE

van Geel M, Goemans A, Toprak F, Vedder P (2017) Which personality traits are related to traditional bullying and cyberbullying? A study with the big five, Dark Triad and sadism. Pers Indiv Differ 106:231–235

Van Hee C, Jacobs G, Emmery C, Desmet B, Lefever E, Verhoeven B, …, Hoste V (2018) Automatic detection of cyberbullying in social media text. PLoS ONE, 13(10), e0203794

Van Hee C, Lefever E, Verhoeven B, Mennes J, Desmet B, De Pauw G, …, Hoste V (2015) Detection and fine-grained classification of cyberbullying events. In International Conference Recent Advances in Natural Language Processing (RANLP) (pp. 672–680)

Wang W, Xie X, Wang X, Lei L, Hu Q, Jiang S (2019) Cyberbullying and depression among chinese college students: a moderated mediation model of social anxiety and neuroticism. J Affect Disord 256:54–61

Whiting P, Savović J, Higgins JP et al (2016) ROBIS: a new tool to assess risk of bias in systematic reviews was developed. J Clin Epidemiol 69:225–234

Witten IH, Frank E, Hall MA (2016) Data Mining: practical machine learning tools and techniques, 4th edn. Morgan Kaufmann Publishers

Wright MF (2017) Cyberbullying in cultural context. J Cross-Cult Psychol 48(8):1136–1137

Wu J, Wen M, Lu R, Li B, Li J (2020) Toward efficient and effective bullying detection in online social network. Peer-to-Peer Netw Appl, 1–10

Wu T, Wen S, Xiang Y, Zhou W (2018) Twitter spam detection: survey of new approaches and comparative study. Computers & Security 76:265–284

Yin D, Xue Z, Hong L, Davison BD, Kontostathis A, Edwards L (2009) Detection of harassment on web 2.0. Proceedings of the Content Analysis in the WEB , 2 , 1–7

Zhang X, Tong J, Vishwamitra N, Whittaker E, Mazer JP, Kowalski R, Hu H, Luo F, Macbeth J, Dillon E (2017) Cyberbullying detection with a pronunciation based convolutional neural network. In Proceedings - 2016 15th IEEE International Conference on Machine Learning and Applications, ICMLA 2016

Zhao R, Mao K (2017) Cyberbullying detection based on semantic-enhanced marginalized denoising auto-encoder. IEEE Trans Affect Comput 8(3), 328–339. Article 7412690

Zhao R, Zhou A, Mao K (2016) Automatic detection of cyberbullying on social networks based on bullying features. In Proceedings of the 17th international conference on distributed computing and networking (pp. 1–6)

Zhong H, Li H, Squicciarini AC, Rajtmajer SM, Griffin C, Miller DJ, Caragea C (2016) Content-Driven Detection of Cyberbullying on the Instagram Social Network. In IJCAI (pp. 3952–3958)

Download references

Author information

Authors and affiliations.

Faculty of Computer Science and Information Systems, Universiti Malaya, Kuala Lumpur, 50603, Malaysia

Vimala Balakrisnan & Mohammed Kaity

You can also search for this author in PubMed   Google Scholar

Contributions

VB wrote the original draft; performed analysis; revised the article; MK performed data collection; performed analysis; revised the article; All authors reviewed the manuscript.

Corresponding author

Correspondence to Vimala Balakrisnan .

Ethics declarations

Competing interests.

The authors declare no competing interests.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Balakrisnan, V., Kaity, M. Cyberbullying detection and machine learning: a systematic literature review. Artif Intell Rev 56 (Suppl 1), 1375–1416 (2023). https://doi.org/10.1007/s10462-023-10553-w

Download citation

Accepted : 08 July 2023

Published : 24 July 2023

Issue Date : October 2023

DOI : https://doi.org/10.1007/s10462-023-10553-w

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Cyberbullying
• Machine learning
• Systematic literature review
• Find a journal
• Publish with us
• Track your research

Home — Essay Samples — Social Issues — Cyber Bullying — Conclusion of Cyber Bullying

Conclusion of Cyber Bullying

• Categories: Conformity Cyber Bullying

About this sample

Words: 526 |

Published: Mar 20, 2024

Words: 526 | Page: 1 | 3 min read

Table of contents

The impact of cyberbullying, the need for action.

Cite this Essay

To export a reference to this article please select a referencing style below:

Let us write you an essay from scratch

• 450+ experts on 30 subjects ready to help
• Custom essay delivered in as few as 3 hours

Get high-quality help

Dr. Karlyna PhD

Verified writer

• Expert in: Psychology Social Issues

+ 120 experts online

By clicking “Check Writers’ Offers”, you agree to our terms of service and privacy policy . We’ll occasionally send you promo and account related email

No need to pay just yet!

Related Essays

4 pages / 1869 words

5 pages / 2267 words

4 pages / 1757 words

1 pages / 616 words

Remember! This is just a sample.

You can get your custom paper by one of our expert writers.

121 writers online

Still can’t find what you need?

Browse our vast selection of original essay samples, each expertly formatted and styled

Related Essays on Cyber Bullying

In today's digital age, social networking has become an integral part of our lives. From connecting with friends and family to sharing our thoughts and experiences, social networks have revolutionized the way we communicate. [...]

Imagine a world where communication and information exchange are limited to physical boundaries and geographical distances. In such a world, the free flow of knowledge, ideas, and innovation would be severely hindered. [...]

Social media platforms have become an integral part of our lives, allowing us to express ourselves freely and connect with others. However, with this freedom comes responsibility. The rise of online hate speech, fake news, and [...]

In today's digital age, where social media platforms and online communication have become an integral part of our lives, cyberbullying has emerged as a pressing issue that needs urgent attention. Cyberbullying refers to the act [...]

Bullying has become a major problem, and the use of the internet has just made it worse. Cyber bullying is bullying done by using technology; it can be done with computers, phones, and the biggest one social media. Children need [...]

Cyberbullying is a complex issue that manifests differently across various cultural contexts. This essay has explored the manifestations of cyberbullying in Western and non-Western cultures, identified global challenges in [...]

Related Topics

By clicking “Send”, you agree to our Terms of service and Privacy statement . We will occasionally send you account related emails.

Where do you want us to send this sample?

By clicking “Continue”, you agree to our terms of service and privacy policy.

Be careful. This essay is not unique

This essay was donated by a student and is likely to have been used and submitted before

Download this Sample

Free samples may contain mistakes and not unique parts

Sorry, we could not paraphrase this essay. Our professional writers can rewrite it and get you a unique paper.

Please check your inbox.

We can write you a custom essay that will follow your exact instructions and meet the deadlines. Let's fix your grades together!

Get Your Personalized Essay in 3 Hours or Less!

We use cookies to personalyze your web-site experience. By continuing we’ll assume you board with our cookie policy .

• Instructions Followed To The Letter
• Deadlines Met At Every Stage
• Unique And Plagiarism Free

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• HHS Author Manuscripts

Cyberbullying and Adolescents

Vidhya lakshmi kumar.

MassGeneral Hospital for Children, Department of Pediatrics, Harvard Medical School

Mark A. Goldstein

Division of Adolescent and Young Adult Medicine, MassGeneral Hospital for Children, Department of Pediatrics, Harvard Medical School, 175 Cambridge Street, Room 508, Boston, MA 02114

Purpose of Review

Cyberbullying is an aggressive behavior involving a type of electronic communication intending to harm a victim that can have profound effects on adolescents. This review examines the epidemiology, issues from cyberbullying, presentation to care of its victims and proposed interventions to this behavior.

Recent Findings

There are a variety of physical and psychological effects on victims of cyberbullying that can include recurrent abdominal pain, headaches and difficulty with sleep. In addition, victims have higher rates of anxiety, depression, suicidal ideation and a lower level of well-being. Unfortunately, victims may remain silent, so screening for cyberbullying is encouraged in a variety of settings. Interventions can be designed at the level of the victim (and perpetrator), family, school and other support networks. Prevention of cyberbullying can be a focus for providers of healthcare.

Cyberbullying can have profound biopsychosocial effects on its victims. There are strategies currently in use and under development to identify and intervene on behalf of those affected by these behaviors.

Introduction

Michelle Carter, age 20, was convicted of involuntary manslaughter and sentenced in 2017 to prison for her role in the 2014 suicide of her then 18-year-old boyfriend, Conrad Roy Jr. The case against Carter, according to prosecutors, rested on text messages that she sent to Roy that encouraged him to end his life which he did by carbon monoxide poisoning. Phoebe Prince, a 15-year-old immigrant from Ireland, committed suicide in 2010 by hanging after bullying online and in school by her peers.

Bullying has been a well-documented phenomenon across the United States and internationally as well. Within Massachusetts, the stories of Michelle Carter, Conrad Roy Jr and Phoebe Prince serve as powerful reminders of the impact of cyberbullying, verbal bullying and intimidation.

Though there is not one standard definition, in the state of Massachusetts, bullying is defined by the Department of Education as “ the severe or repeated use by one or more students of a written, verbal, or electronic expression, or a physical act or gesture, or any combination thereof, directed at another student that has the effect of: (i) causing physical or emotional harm to the other student or damage to the other student’s property; (ii) placing the other student in reasonable fear of harm to himself or of damage to his property; (iii) creating a hostile environment at school for the other student; (iv) infringing on the rights of the other student at school; or (v) materially and substantially disrupting the education process or the orderly operation of a school” ( 1 ). It is this electronic expression, in particular, that has catapulted in recent years with the advancement in technology, the ease of communication via social media, as well as the dissemination and access to technology among grade school children and beyond.

Definition of Cyberbullying

Cyberbullying has evolved in many forms, which has created difficulty in establishing a unified definition that is widely accepted by clinicians. The definition of bullying itself does not easily translate to the cyber arena, but at its core, primarily refers to “an intentional act of aggression, carried out to harm another individual using electronic forms of contacts or devices” ( 2 ). Though initially limited to electronic mail, cyberbullying has slowly begun to incorporate a wider array of forms of electronic communication, ranging from personal blogs, text messaging, video content posted to streaming websites, such as You Tube, and more recently, social media formats including Instagram, SnapChat and TikTok.

Further exacerbating the potential for a severe impact of cyberbullying is access to smartphone technology, the audience involved in cyberbullying efforts, the opportunity for “anonymity by perpetrators,” the “permanency of bullying displays on the internet,” as well as the ability of bullying to occur regardless of distance from the victim and with “minimal constraints on time ( 3 ).” Cyberbullying can take on the following forms: flaming (online fights using electronic messages with angry and vulgar language), harassment, cyber stalking, denigration, impersonation, outing, trickery and exclusion ( 4 ). In the case of Michelle Carter, she used text messages to Conrad Roy to encourage him to end his life.

Epidemiology

Given the lack of consensus on a definition for cyberbullying, it has been difficult to easily quantify its true prevalence in the United States and the global arena. In a small sample of global studies, prevalence of middle and high school cyberbullying ranged from 1–30% for suspected perpetrators, and from 3–72% for suspected victims ( 3 ). The prevalence has been thought to vary due to a multitude of factors including varying definitions for what constitutes an act of cyberbullying, cross-cultural differences in victim reporting, as well as access to technology, which could limit the ability to participate in cyberbullying. Studies available across the U.S. and internationally identify vulnerable populations of adolescents for whom special attention should be made, including females, LGBTQ youth, younger adolescents and youth with disabilities ( 5 , 6 ).

Studies have also demonstrated gender differences in the prevalance of cyberbullying vicitimization, with female adolescents reporting a higher prevalence of victimization (9.4% for single encounter, 13.3% with two or more encounters) than their male counterparts (8.3% for single encounter, 7.8% with two more encounters) ( 7 ). Being bullied is further associated with increased suicidal ideation, delinquency and global psychological distress among both male and female adolescents, though more marked in females and more pronounced with repeated cyberbullying encounters or incidences ( 7 ).

Surveys of cyberbullying victims population further identify a large proportion of youth who identified as a part of the LGBTQ community, as well as youth with disabilities. In a Taiwanese study reviewing 500 homosexual or bisexual men between the ages of 20 and 25, there were reported significant associations between low family support, early coming out and traditional bullying victimization with cyberbullying ( 8 ).

In addition, adolescents and young adults with mental health needs or disabilities have often been targets of cyberbullying efforts. A Chinese study examining associations between cyberbullying and social impairment, attention-deficit-hyperactivity disorder (ADHD) and oppositional defiant disorder (ODD) in adolescents with high functioning autism spectrum disorder demonstrated that older adolescents and those with more severe ODD symptoms were more likely to be victims of cyberbullying. The victims of cyberbullying in this population were more likely to report symptoms associated with depression, anxiety and suicidality ( 9 ).

Issues from Cyberbullying

Cyberbullying has been associated with a variety of psychological and physical effects on its victims ( Table 1 ) ( 10 – 12 ). Victims of cyberbullying have higher rates of depression when compared to other forms of traditional bullying. In addition, victims may have more anxiety and suicidal ideation compared to peers who do not face victimization ( 3 , 8 ). A varying percentage of cyberbullying victims pursue suicide. Some studies suggest that children and adolescents who are both victims and perpetrators of cyberbullying constitute a distinct group with the highest risk for psychosocial problems, such as depressive and anxiety symptoms, as well as for lower levels of well-being in general. Victims of cyberbullying have also shown impacts in their family dynamics and relationships with friends, with many demonstrating increasing isolation and loneliness as well as decreased trust in their support groups ( 13 ). Some studies have indicated that reactions to cyberbullying may depend on the form of media (video vs. text conversation vs. phone calls) with some suggestion that pictures and video were the most negatively impactful on adolescents ( 14 ).

Signs and Symptoms of Cyberbullying ( 10 – 12 )

There have been relatively few studies examining the effect of cyberbullying on adolescents’ physical health. Grade school adolescent cyberbullying victims are often more likely to report somatic symptoms including difficulty sleeping, recurrent non-specific abdominal pain and frequent headaches ( 3 ). However, certain studies indicate that cyberbullies might be better off than victims with some studies finding no relation between the role of perpetrator and depressive symptoms ( 2 ). Other studies have focused on health impact as opposed to specific health problems by examining self-reported health-related quality of life (HRQOL). Survey data collected from college students have demonstrated long term impacts on physical health due to pre-college bullying experiences with lower HRQOL, likely mediated through depression ( 15 ). Furthermore, the study proposed that precollege exposure to cyberbullying might have latent effects that could be triggered by future bullying-related traumatization, including reduced confidence in social situations as well as isolation ( 15 ).

In addition, there have been links between cyberbullying and increased risky behaviors including substance abuse across a variety of substances. In a study examining a population of Greek national undergraduates, both male and female late adolescents who were victims of bullying during middle and high school were less likely to use condoms during college years when compared to non-victimized students ( 16 ). Furthermore, men who were bullies or victims of bullying were twice as likely to experience excessive drunkness and three times as likely to pay for sex. In addition, for males, cyberbullies and cybervictims were more likely to report smoking ( 16 ). Compared with traditional bullying, cyberbullying may have a stronger link to substance abuse, with one longitudinal study demonstrating that cyberbullying victimization predicted depression and substance abuse six months later ( 17 ). In addition, both victims and perpetrators of cyberbullying have been linked with increased use of marijuana with an implication that this may be indicative of a larger substance abuse problem among this population ( 18 ). This highlights the emergence of gender specific risks and behaviors associated with cyberbullying that require further evaluation.

The relationship between cyberbullying and an adolescent’s use of the internet has also been explored. A study of 845 adolescents with a median age of 15 years demonstrated that cyberbullying victims were at increased risk for having problematic internet use (PUI), which included a preoccupation with the internet, an inability to control their use of the internet, as well as continued use despite negative consequences ( 19 ). However, it remains unclear whether the increased time spent on the internet is deleterious or protective, as victims may be using the internet as an escape mechanism to mitigate anxiety and reduce negative feelings of isolation. Nevertheless, increased time on the internet by cyberbullying victims does place them at risk for harassment, invasion of privacy and exploitation ( 19 ).

Presentation to Care

Unfortunately, despite the deleterious effects of cyberbullying on a victim’s mental and physical health, many victims remain silent and hesitate to reach out for help. The onus, therefore, remains on others: educators, providers, family members and social supports to recognize common signs and symptoms of cyberbullying. Most often, individuals will notice that such victims begin to avoid school, a primary setting in which they face the effects of cyberbullying. In addition, a large majority of perpetrators may be members of the victim’s school community.

Accordingly, the victim may have increased school absenteeism due to somatic symptoms (frequent stomachaches, headaches, sleeping disruption or nightmares) or academic difficulties due to lack of school attendance or problems with concentration. Victims may demonstrate lower self-esteem, increased depressive symptoms and anxiety with detachment from friends or sudden withdrawal at home or school. On the contrary, these affected youth may show sudden bursts of anger or demonstrate increased self-destructive behaviors, such as cutting, or acts of truancy ( 10 – 12 ). Ultimately, since a victim may not come forward to seek help, it is important that support groups bring the individual to care.

The ability to prevent or intervene in cyberbullying most effectively hinges upon screening to detect and identify victims, as well as perpetrators. There is difficulty in determining the best method to screen for bullying in the medical setting, whether this is in the emergency department or at a primary care visit. Though direct questioning may be effective, studies have posited that it may be more effective to use a questionnaire to elicit accurate responses from patients. The “Guidelines for Adolescent Preventive Services” form includes screening across a variety of health behaviors and experiences, including bullying ( 20 ). Couching inquiries about bullying in the setting of assessing adolescent behavior may serve to normalize questioning about bullying and in turn allow adolescents to open up to providers about their experiences. These screens can focus on questions such as ( 21 ):

- How often do you get bullied or bully others?

- How long have you been bullied or bullied others?

- Where are you bullied or bully others?

- How are you bullied or how do you bully others?

Screening for cyberbullying should be an important element of adolescent care. Furthermore, screening should not be limited to non-urgent scenarios. Studies have shown that adolescents report exposure to cyberbullying and violence in a variety of urgent medical situations as well, including emergency rooms, inpatient hospital stays and school-based clinics. This underscores the importance screening for cyberbullying during any patient interaction.

Though victims may present to their pediatrician’s office for assistance, often these youth present to the emergency department. These encounters may be due to mental health needs, in the setting of suicidal ideation or attempts at self-harm, previously identified as significant symptomatology in cyberbullying victims. Studies demonstrate that over three quarters of victims of cyberbullying will present to the emergency department with a mental health need as their chief complaint and that more than three quarters of adolescents presenting with suicidal ideation as their chief complaint have endorsed previous incidences of cyberbullying ( 22 ). Cyberbullying was also found to be the strongest predictor of suicidal ideation, while controlling for other important factors, such as age, gender and psychiatric diagnosis ( 22 ). Therefore, it remains important that providers caring for adolescents and young adults presenting with suicidal ideation pointedly ask about bullying and cyberbullying in the patient’s life. In a Canadian population of adolescents, cyberbullying victims were more likely to attempt, or complete suicide compared to those who had not been bullied ( 18 ). It is further postulated that cyberbullying victims may seek help less frequently or underreport incidences compared to those who have been traditionally bullied and that increases their risk of suicidal ideation ( 22 ).

Types of Interventions

Interventions designed to target and mitigate cyberbullying remain as important as attempts to intervene and provide support for victims. These efforts should not solely focus on victims; they should also work with perpetrators. Programs need to reinforce positive values in school age children to reduce the number of cyberbullying perpetrators.

Though these interventions may occur in a multitude of settings, many studies have primarily focused on school-based interventions. This seems appropriate given that a large proportion of cyberbullying incidents take place amongst school classmates. Social support has been shown to be an important buffer when adolescents experience cyberbullying ( 23 ). As previously suggested by the efficacy of school-based interventions, perceived social support from family and teachers has been shown to potentially ameliorate the association between cyberbullying and several outcomes at the psychosocial level. A study of 131 pupils with developmental disorders who had received social support from parents and teachers demonstrated reduced depressive symptoms one year after a cyberbullying experience ( 24 ).

A viable intervention program and cyberbullying prevention mechanism may rely on specific strategies such as improved access to resources, as well as efforts to increase the potential protective effects of social support figures in an adolescent’s life, including family members, friends and teachers ( 2 ). This study in particular suggested that there may be differences between male and female victims as to which form of social support is more efficacious with an implication that girls may benefit more from social supports than their male counterparts ( 2 ). However, the efficacy of social support in preventing cyberbullying or supporting its victims is often contingent upon adolescents seeking help or divulging their victim status.

Some studies suggest that effective interventions focus on enhancing an adolescent’s empathy, promoting positive social relationships with family and decreasing screen time ( 13 ). In particular, given the lack of nonverbal cues inherent in the nature of cyberbullying, it is postulated that adolescents who serve as cyberbullying perpetrators may demonstrate little empathy for their cyber victims. Furthermore, given that poor self-esteem has been shown to be a significant factor among victims and perpetrators alike, both educators and health care providers should focus on an adolescent’s emotional status, particularly with those who seem to demonstrate not only a decline in their self-esteem but also who are showing more troublesome behaviors such as truancy and substance use ( 18 ).

Another potential focus of intervention may hinge on coping strategies for adolescents ( 25 ). Coping strategies are divided into two types: emotion-focused and problem-focused. There are two emotion-based strategies that victims of cyberbullying can utilize: self-control and escape-avoidance. The self-control strategy employs inhibitions of emotional expressions and spontaneous behavior ( 26 ). The desire to regulate emotions brought on by a stressful situation is usually carried out when there is a belief that nothing can be done to change the unfavorable conditions ( 27 ). This may lead to increased avoidance and depression-based coping in a cyberbullying victim’s day-to-day activities with increased depressive symptoms and health complaints.

Problem-focused strategies may be particularly helpful to cyberbullying victims, as they often cannot face (or identify) their aggressor or stand up to the bully ( 28 ). As a result, coping strategies that attempt to either manage or solve the problem may be more beneficial to victims of cyberbullying, motivating them to implement changes, both internally and environmentally. Although there is no one right way to cope, adolescents employing “more approach and problem solving” as opposed to avoidance strategies, and assessing a stressor to be a challenge were shown to have more adaptive outcomes ( 29 ). Such strategies teach the importance of standing up for oneself as well as using methods to not only deal with cyberbullying but manage the daily stress ( 30 ).

A validated tool, such as the Utrecht Coping List for Adolescents, has been a long-standing tool used to help adolescents work through their current emotional coping-based mechanisms and transition to thinking in a more pro-active problem-based fashion. This underscores the importance of both social skills and assertiveness training which inspire victims to adopt more active problem-based strategies, such as telling someone about their bullying or making new friends ( 31 ). These coping strategies, in conjunction with school, peer group and teacher-based efforts to prevent bullying, may bolster the prevention and resiliency efforts currently underway.

Prevention of cyberbullying should be a focus for healthcare providers. Anticipatory guidance remains a cornerstone of the well child and well adolescent visit, and should include strategies conveyed to both patients and their parents on how to identify signs of cyberbullying, In addition, discussion of stigma and myths about cyberbullying should occur. This could include discussions about the use of technology in the home, as well as the best and safest social media practices for the adolescent. Furthermore, taking a history about the signs and symptoms of cyberbullying from caregivers independently of the adolescent may be helpful in determining the patient’s source of distress and to appropriately plan interventions.

A variety of screening tools have been developed ( Table 2 ) that represent the potential to identify victimization as well as serve as an opportunity to respond and intervene ( 32 ). However, these tools address the larger umbrella phenomenon of bullying and are not specific to cyberbullying. Therefore, instruments and tools that can be used adequately to identify victims and aggressors of cyberbullying still remain a large area of need.

Current Bullying Assessment Tools ( 32 )

Many states have responded to the surge of cyberbullying with legislation focusing on prevention, intervention and consequences. In Massachusetts, as a response to the deaths of Phoebe Prince and others, legislation was enacted so that all school staff (including educators, nurses, custodians, athletic coaches, advisors to extracurricular activities, administrators, cafeteria workers, bus drivers, and paraprofessionals) must report bullying to the school administration ( 1 ). These individuals are also required to receive training on bullying prevention and intervention ( 1 ). That stated, effective interventions to prevent cyberbullying-related suicide or suicidal ideation have not yet been identified or vetted through research.

Currently, there are a variety of school-based interventions focused on adolescent suicide awareness, typically presented between the ages of 12 and 18. Preventative interventions focus on suicide awareness campaigns or screening as primary preventative measures, or secondary approaches to provide support to those affected by suspected suicides. Some schools have implemented psychologic interventions in those who have already demonstrated attempts at self-harm, including cognitive behavioral therapy (CBT), dialectic behavioral therapy (DBT) and home-based family interventions ( 33 ). However, these services are not routinely available in school systems and their efficacy in identifying cyberbullying victims and pro-actively preventing attempts at suicide are not well understood. Ultimately, though there are school-based interventions in place for suicide awareness, only a few are evidenced-based and there is little to demonstrate the true efficacy of these interventions for preventing suicide and suicide attempts in the adolescent population. Therefore, the adolescent population serves as an untapped area of research into evidence-based interventions and policies, potentially to be extrapolated from other high-risk populations and proven efficacious efforts.

Much of the current literature focuses on an older adolescent population (i.e. high school and undergraduate). It may, therefore, behoove the community to understand the effects of cyberbullying in younger adolescents (less than 12 years of age) and how this may inform prevention efforts. This is a particularly important focus given the ubiquity of technology and internet access in a young child’s life. The large majority of children regularly use the internet ( 17 ). Some studies have demonstrated similarly negative effects on psychological well-being of younger adolescents secondary to cyberbullying victimization, poor self-esteem and decreased peer socialization ( 34 ). The ability to identify these negative effects at a younger age may allow us to build more effective programs and coping strategies at an earlier age to ultimately foster a population of adolescents with increased resiliency and skills to face the stressors of life.

Ultimately, the prevention of cyberbullying rests not only on the shoulders of victims and their families, but on educators, providers and researchers. More focused studies and evaluations of interventions may not only reduce the prevalence of cyberbullying but also lower the mental health sequelae seen in the short and long term. The serious consequences of cyberbullying, particularly surrounding mental health issues and suicidal ideation, underscore the importance of effective and evidence-based bullying prevention programs and support groups in school-based settings. In addition, the multitude of factors associated with victimization in cyber sexuality-related bullying as well should be factored into developing prevention and intervention strategies.

Acknowledgments

Funding information : This paper was funded in part by NIH grant 5 R01 MH103402.

The authors wish to thank Dr. Karen Sadler for reviewing their manuscript.

Compliance with Ethics Guidelines

Conflict of Interest

The authors declare no conflict of interest.

Human and Animal Rights and Informed Consent

This article does not contain any studies with human or animal subjects performed by any of the authors.

Publisher's Disclaimer: This Author Accepted Manuscript is a PDF file of a an unedited peer-reviewed manuscript that has been accepted for publication but has not been copyedited or corrected. The official version of record that is published in the journal is kept up to date and so may therefore differ from this version.

Contributor Information

Vidhya Lakshmi Kumar, MassGeneral Hospital for Children, Department of Pediatrics, Harvard Medical School.

Mark A. Goldstein, Division of Adolescent and Young Adult Medicine, MassGeneral Hospital for Children, Department of Pediatrics, Harvard Medical School, 175 Cambridge Street, Room 508, Boston, MA 02114.

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

1. Ang RP. Adolescent cyberbullying: a review of characteristics, prevention and intervention strategies. Aggress Violent Behav. (2015) 25:35–42. doi: 10.1016/j.avb.2015.07.011

CrossRef Full Text | Google Scholar

2. Reyna VF, Farley F. Risk and rationality in adolescent decision making: implications for theory, practice, and public policy. Psychol Sci Public Interest. (2006) 7:1–44. doi: 10.1111/j.1529-1006.2006.00026.x

PubMed Abstract | CrossRef Full Text | Google Scholar

3. UNICEF ed. Children in a Digital World . New York, NY: UNICEF (2017).

Google Scholar

4. Thomas HJ, Connor JP, Scott JG. Integrating traditional bullying and cyberbullying: challenges of definition and measurement in adolescents - a review. Educ Psychol Rev. (2015) 27:135–52. doi: 10.1007/s10648-014-9261-7

5. Baldry AC, Farrington DP, Sorrentino A. “Am I at risk of cyberbullying”? A narrative review and conceptual framework for research on risk of cyberbullying and cybervictimization: the risk and needs assessment approach. Aggress Violent Behav. (2015) 23:36–51. doi: 10.1016/j.avb.2015.05.014

6. Olweus D. Bullying at School: What We Know and What We Can Do . Oxford; Cambridge, MA: Blackwell (1993).

PubMed Abstract | Google Scholar

7. Dooley JJ, Pyzalski J, Cross D. Cyberbullying versus face-to-face bullying: a theoretical and conceptual review. J Psychol. (2009) 217:182–8. doi: 10.1027/0044-3409.217.4.182

8. Smith PK, Mahdavi J, Carvalho M, Fisher S, Russell S, Tippett N. Cyberbullying: its nature and impact in secondary school pupils. J Child Psychol Psychiatry. (2008) 49:376–85. doi: 10.1111/j.1469-7610.2007.01846.x

9. Kowalski RM, Giumetti GW, Schroeder AN, Lattanner MR. Bullying in the digital age: a critical review and meta-analysis of cyberbullying research among youth. Psychol Bull. (2014) 140:1073–137. doi: 10.1037/a0035618

10. León Vicente I. Cybervictimization by cyberbullying: children at risk and children as risk (dissertation). University of the Basque Country, Leioa, Spain (2016).

11. Hinduja S, Patchin JW. Identification, Prevention, and Response. (2020).

12. Jadambaa A, Thomas HJ, Scott JG, Graves N, Brain D, Pacella R. Prevalence of traditional bullying and cyberbullying among children and adolescents in Australia: a systematic review and meta-analysis. Aust N Z J Psychiatry. (2019) 53:878–88. doi: 10.1177/0004867419846393

13. Pereira F, Matos M. Cyber-stalking victimization: what predicts fear among Portuguese adolescents? Eur J Crim Policy Res. (2016) 22:253–70. doi: 10.1007/s10610-015-9285-7

14. Reed LA, Ward LM, Tolman RM, Lippman JR, Seabrook RC. The association between stereotypical gender and dating beliefs and digital dating abuse perpetration in adolescent dating relationships. J Interpers Violence . (2018). doi: 10.1177/0886260518801933

15. Huang CL, Yang SC, Hsieh LS. The cyberbullying behavior of Taiwanese adolescents in an online gaming environment. Children Youth Serv Rev. (2019) 106:104461. doi: 10.1016/j.childyouth.2019.104461

16. Raskauskas J, Huynh A. The process of coping with cyberbullying: a systematic review. Aggress Violent Behav. (2015) 23:118–25. doi: 10.1016/j.avb.2015.05.019

17. Bradshaw J, Crous G, Rees G, Turner N. Comparing children's experiences of schools-based bullying across countries. Children Youth Serv Rev. (2017) 80:171–80. doi: 10.1016/j.childyouth.2017.06.060

18. Hutson E, Kelly S, Militello LK. Systematic review of cyberbullying interventions for youth and parents with implications for evidence-based practice. Worldviews Evid Based Nurs. (2018) 15:72–9. doi: 10.1111/wvn.12257

19. Larranaga E, Yubero S, Ovejero A, Navarro R. Loneliness, parent-child communication and cyberbullying victimization among Spanish youths. Comp Hum Behav. (2016) 65:1–8. doi: 10.1016/j.chb.2016.08.015

20. van Geel M, Vedder P, Tanilon J. Relationship between peer victimization, cyberbullying, and suicide in children and adolescents: a meta-analysis. JAMA Pediatr. (2014) 168:435–42. doi: 10.1001/jamapediatrics.2013.4143

21. Zych I, Ortega-Ruiz R, Del Rey R. Systematic review of theoretical studies on bullying and cyberbullying: facts, knowledge, prevention, and intervention. Aggress Violent Behav. (2015) 23:1–21. doi: 10.1016/j.avb.2015.10.001

22. Selkie EM, Kota R, Chan Y-F, Moreno M. Cyberbullying, depression, and problem alcohol use in female college students: a multisite study. Cyberpsychol Behav Soc Netw. (2015) 18:79–86. doi: 10.1089/cyber.2014.0371

23. Brochado S, Soares S, Fraga S. A scoping review on studies of cyberbullying prevalence among adolescents. Trauma Violence Abus. (2017) 18:523–31. doi: 10.1177/1524838016641668

24. Nocentini A, Calmaestra J, Schultze-Krumbholz A, Scheithauer H, Ortega R, Menesini E. Cyberbullying: labels, behaviours and definition in three European Countries. Aust J Guid Couns. (2010) 20:129–42. doi: 10.1375/ajgc.20.2.129

25. Willard NE. Cyberbullying and Cyberthreats: Responding to the Challenge of Online Social Aggression, Threats, and Distress . Champaign: Research Press (2007).

26. Aizenkot D, Kashy-Rosenbaum G. Cyberbullying victimization in whatsapp classmate groups among Israeli Elementary, Middle, and High School Students. J Interpers Violence . (2019). doi: 10.1177/0886260519842860

27. Anderson CA, Bushman BJ. Human aggression. Ann Rev Psychol. (2002) 53:27–51. doi: 10.1146/annurev.psych.53.100901.135231

28. Higgins JP, Green S. Cochrane Handbook for Systematic Reviews of Interventions . (2011). Available online at: http://handbook-5-1.cochrane.org/ (accessed January 17, 2021).

29. Alhajji M, Bass S, Dai T. Cyberbullying, mental health, and violence in adolescents and associations with sex and race: data from the 2015 youth risk behavior survey. Global Pediatr Health . (2019) 6:2333794X19868887. doi: 10.1177/2333794X19868887

30. Grinshteyn E, Yang YT. The association between electronic bullying and school absenteeism among high school students in the United States. J School Health. (2017) 87:142–9. doi: 10.1111/josh.12476

31. Mesch GS. Parent-child connections on social networking sites and cyberbullying. Youth Soc. (2018) 50:1145–62. doi: 10.1177/0044118X16659685

32. Sam J, Wisniewski P, Xu H, Rosson MB, Carroll JM. How are social capital and parental mediation associated with cyberbullying and cybervictimization among youth in the United States? In: Stephanidis C, editor. HCI International 2017 – Posters' Extended Abstracts Communications in Computer Information Science Cham: Springer International Publishing. p. 638–644.

33. Hong JS, Kim DH, Thornberg R, Kang JH, Morgan JT. Correlates of direct and indirect forms of cyberbullying victimization involving South Korean adolescents: an ecological perspective. Comput Hum Behav. (2018) 87:327–36. doi: 10.1016/j.chb.2018.06.010

34. Lee C, Shin N. Prevalence of cyberbullying and predictors of cyberbullying perpetration among Korean adolescents. Comp Hum Behav. (2017) 68:352–8. doi: 10.1016/j.chb.2016.11.047

35. You S, Lim SA. Longitudinal predictors of cyberbullying perpetration: evidence from Korean middle school students. Person Ind Differ. (2016) 89:172–6. doi: 10.1016/j.paid.2015.10.019

36. Holt TJ, Fitzgerald S, Bossler AM, Chee G, Ng E. Assessing the risk factors of cyber and mobile phone bullying victimization in a nationally representative sample of Singapore Youth. Int J Offend Ther Comp Criminol. (2016) 60:598–615. doi: 10.1177/0306624X14554852

37. Marret MJ, Choo WY. Factors associated with online victimisation among Malaysian adolescents who use social networking sites: a cross-sectional study. BMJ Open. (2017) 7:e014959. doi: 10.1136/bmjopen-2016-014959

38. Tesler R, Nissanholtz-Gannot R, Zigdon A, Harel-Fisch Y. The association of cyber-bullying and adolescents in religious and secular schools in Israel. J Relig Health. (2019) 58:2095–109. doi: 10.1007/s10943-019-00938-z

39. Beran T, Mishna F, McInroy LB, Shariff S. Children's experiences of cyberbullying: a Canadian National Study. Child School. (2015) 37:207–14. doi: 10.1093/cs/cdv024

40. World Bank Country and Lending Groups – World Bank Data Help Desk. Available online at: https://datahelpdesk.worldbank.org/knowledgebase/articles/906519-world-bank-country-and-lending-groups [accessed January 25, 2021).

41. Marco JH, Tormo-Irun P. Cyber victimization is associated with eating disorder psychopathology in adolescents. Front Psychol. (2018) 9:987. doi: 10.3389/fpsyg.2018.00987

42. Olenik-Shemesh D, Heiman T. Cyberbullying victimization in adolescents as related to body esteem, social support, and social self-efficacy. J Genet Psychol. (2017) 178:28–43. doi: 10.1080/00221325.2016.1195331

43. Rao J, Wang H, Pang M, Yang J, Zhang J, Ye Y, et al. Cyberbullying perpetration and victimisation among junior and senior high school students in Guangzhou, China. Inj Prev. (2019) 25:13–9. doi: 10.1136/injuryprev-2016-042210

44. Lee C. Weak Commitment to School, Deviant Peers, and Cyberbullying Victimization-Strain in Adolescent Cyberbullying . (2017). Available online at: https://search.proquest.com/pqdtglobal/docview/1937915933/abstract/9D85437600564444PQ/30 (accessed June 17, 2020).

45. Lin MT. Risk factors associated with cyberbullying victimization and perpetration among Taiwan children (dissertation). The University of Texas, Austin, TX, United States (2019).

46. Pieschl S, Porsch T. The complex relationship between cyberbullying and trust. Int J Dev Sci. (2017) 11:9–17. doi: 10.3233/DEV-160208

47. Brighi A, Menin D, Skrzypiec G, Guarini A. Young, bullying, and connected. Common pathways to cyberbullying and problematic internet use in adolescence. Front Psychol. (2019) 10:1467. doi: 10.3389/fpsyg.2019.01467

48. Festl R. Perpetrators on the internet: analyzing individual and structural explanation factors of cyberbullying in school context. Comp Hum Behav. (2016) 59:237–48. doi: 10.1016/j.chb.2016.02.017

49. Olumide AO, Adams P, Amodu OK. Prevalence and correlates of the perpetration of cyberbullying among in-school adolescents in Oyo State, Nigeria. Int J Adolesc Med Health. (2016) 28:183–91. doi: 10.1515/ijamh-2015-0009

50. Baldry AC, Sorrentino A, Farrington DP. Cyberbullying and cybervictimization versus parental supervision, monitoring and control of adolescents' online activities. Child Youth Serv Rev. (2019) 96:302–7. doi: 10.1016/j.childyouth.2018.11.058

51. Garaigordobil M. Cyberbullying in adolescents and youth in the Basque Country: prevalence of cybervictims, cyberaggressors, and cyberobservers. J Youth Stud. (2015) 18:569–82. doi: 10.1080/13676261.2014.992324

52. Morin HK, Bradshaw CP, Kush JM. Adjustment outcomes of victims of cyberbullying: the role of personal and contextual factors. J School Psychol. (2018) 70:74–88. doi: 10.1016/j.jsp.2018.07.002

53. Baraldsnes D. The prevalence of cyberbullying and the views of 5-12 grade pupils and teachers on cyberbullying prevention in Lithuanian Schools. Uinv J Educ Res. (2015) 3:949–59. doi: 10.13189/ujer.2015.031201

54. Razjouyan K, Mobarake AH, Sadr SS, Ardestani SMS, Yaseri M. The relationship between emotional intelligence and the different roles in cyberbullying among high school students in Tehran. Iran J Psychiatry Behav Sci. (2018) 12:UNSP e11560. doi: 10.5812/ijpbs.11560

55. Alvarez-Garcia D, Carlos Nunez J, Garcia T, Barreiro-Collazo A. Individual, family, and community predictors of cyber-aggression among adolescents. Eur J Psychol Appl Legal Context. (2018) 10:79–88. doi: 10.5093/ejpalc2018a8

56. Horzum MB, Ayas T, Randler C, Dusunceli B. The effects of empathy and circadian preference on cyberbullying of adolescents in Turkey. Biol Rhythm Res . (2019). doi: 10.1080/09291016.2019.1603839

57. Carmen Martinez-Monteagudo M, Delgado B, Manuel Garcia-Fernandez J, Rubio E. Cyberbullying, aggressiveness, and emotional intelligence in adolescence. Int J Environ Res Public Health. (2019) 16:5079. doi: 10.3390/ijerph16245079

58. Sasson H, Mesch G. The role of parental mediation and peer norms on the likelihood of cyberbullying. J Genet Psychol. (2017) 178:15–27. doi: 10.1080/00221325.2016.1195330

59. Wang X, Lei L, Liu D, Hu H. Moderating effects of moral reasoning and gender on the relation between moral disengagement and cyberbullying in adolescents. Person Ind Differ. (2016) 98:244–9. doi: 10.1016/j.paid.2016.04.056

60. Simsek N, Sahin D, Evli M. Internet addiction, cyberbullying, and victimization relationship in adolescents a sample from Turkey. J Addict Nurs. (2019) 30:201–10. doi: 10.1097/JAN.0000000000000296

61. McQuillan BE. Ecological Factors Associated with Middle School Students' Experiences of Cyberbullying . (2016). Available online at: https://search.proquest.com/pqdtglobal/docview/1794167537/abstract/9D85437600564444PQ/4 (accessed June 17, 2020).

62. Rose CA, Tynes BM. Longitudinal associations between cybervictimization and mental health among U.S. adolescents. J Adolesc Health. (2015) 57:305–12. doi: 10.1016/j.jadohealth.2015.05.002

63. Stockdale LA, Coyne SM, Nelson DA, Erickson DH. Borderline personality disorder features, jealousy, and cyberbullying in adolescence. Pers Individ Differ. (2015) 83:148–53. doi: 10.1016/j.paid.2015.04.003

64. Chen Q, Lo Camilla KM, Yuhong Z, Anne C, Ling CK, Patrick I. Family poly-victimization and cyberbullying among adolescents in a Chinese school sample. Child Abuse Negl. (2018) 77:180–7. doi: 10.1016/j.chiabu.2018.01.015

65. Landoll RR, La Greca AM, Lai BS, Chan SF, Herge WM. Cyber victimization by peers: prospective associations with adolescent social anxiety and depressive symptoms. J Adolesc. (2015) 42:77–86. doi: 10.1016/j.adolescence.2015.04.002

66. Iranzo B, Buelga S, Cava M-J, Ortega-Baron J. Cyberbullying, psychosocial adjustment, and suicidal ideation in adolescence. Psychosoc Interv. (2019) 28:75–81. doi: 10.5093/pi2019a5

67. Buelga S, Cava MJ, Musitu G, Torralba E. Cyberbullying aggressors among Spanish secondary education students: an exploratory study. Interact Tech Smart Ed. (2015) 12:100–15. doi: 10.1108/ITSE-08-2014-0025

68. Katz I, Lemish D, Cohen R, Arden A. When parents are inconsistent: parenting style and adolescents' involvement in cyberbullying. J Adolesc. (2019) 74:1–2. doi: 10.1016/j.adolescence.2019.04.006

69. Cénat JM, Blais M, Lavoie F, Caron P-O, Hébert M. Cyberbullying victimization and substance use among Quebec high schools students: the mediating role of psychological distress. Comp Hum Behav. (2018) 89:207–12. doi: 10.1016/j.chb.2018.08.014

70. Hoareau N, Bages C, Allaire M, Guerrien A. The role of psychopathic traits and moral disengagement in cyberbullying among adolescents. Crim Behav Ment Health. (2019) 29:321–31. doi: 10.1002/cbm.2135

71. Khurana A, Bleakley A, Jordan A, Romer D. The protective effects of parental monitoring and internet restriction on adolescents' risk of online harassment. J Youth Adolesc. (2015) 44:1039–47. doi: 10.1007/s10964-014-0242-4

72. Martínez I, Murgui S, Garcia OF, Garcia F. Parenting in the digital era: protective and risk parenting styles for traditional bullying and cyberbullying victimization. Comp Hum Behav. (2019) 90:84–92. doi: 10.1016/j.chb.2018.08.036

73. Yusuf S, Salleh H, Bahaman A, Shamsul M, Ramli N, Ramli AN, et al. Parental attachment and cyberbullying experiences among Malaysian children. Pertanika J Scholarly Res Rev . (2018) 4:67–80.

74. Martinez-Ferrer B, Leon-Moreno C, Musitu-Ferrer D, Romero-Abrio A, Callejas-Jeronimo JE, Musitu-Ochoa G. Parental socialization, school adjustment and cyber-aggression among adolescents. Int J Environ Res Public Health. (2019) 16:4005. doi: 10.3390/ijerph16204005

75. Moreno–Ruiz D, Martínez–Ferrer B, García–Bacete F. Parenting styles, cyberaggression, and cybervictimization among adolescents. Comp Hum Behav. (2019) 93:252–9. doi: 10.1016/j.chb.2018.12.031

76. Ho SS, Chen L, Ng APY. Comparing cyberbullying perpetration on social media between primary and secondary school students. Comp Educ. (2017) 109:74–84. doi: 10.1016/j.compedu.2017.02.004

77. Gómez-Ortiz O, Romera EM, Ortega-Ruiz R, Del Rey R. Parenting practices as risk or preventive factors for adolescent involvement in cyberbullying: contribution of children and parent gender. Int J Environ Res Public Health. (2018) 15:2664. doi: 10.3390/ijerph15122664

78. Wright MF, Kamble SV, Soudi SP. Indian adolescents' cyber aggression involvement and cultural values: the moderation of peer attachment. Sch Psychol Int. (2015) 36:410–27. doi: 10.1177/0143034315584696

79. Holfeld B, Leadbeater BJ. Concurrent and longitudinal associations between early adolescents' experiences of school climate and cyber victimization. Comput Hum Behav. (2017) 76:321–8. doi: 10.1016/j.chb.2017.07.037

80. Álvarez-García D, Núñez JC, González-Castro P, Rodríguez C, Cerezo R. The effect of parental control on cyber-victimization in adolescence: the mediating role of impulsivity and high-risk behaviors. Front Psychol. (2019) 10:1159. doi: 10.3389/fpsyg.2019.01159

81. Modecki KL, Minchin J, Harbaugh AG, Guerra NG, Runions KC. Bullying prevalence across contexts: a meta-analysis measuring cyber and traditional bullying. J Adolesc Health. (2014) 55:602–11. doi: 10.1016/j.jadohealth.2014.06.007

82. Hamm MP, Newton AS, Chisholm A, Shulhan J, Milne A, Sundar P, et al. Prevalence and effect of cyberbullying on children and young people: a scoping review of social media studies. JAMA Pediatr. (2015) 169:770. doi: 10.1001/jamapediatrics.2015.0944

83. Gaffney H, Farrington DP, Espelage DL, Ttofi MM. Are cyberbullying intervention and prevention programs effective? A systematic and meta-analytical review. Aggress Violent Behav. (2019) 45:134–53. doi: 10.1016/j.avb.2018.07.002

84. Selkie EM, Fales JL, Moreno MA. Cyberbullying prevalence among US middle and high school-aged adolescents: a systematic review and quality assessment. J Adolesc Health. (2016) 58:125–33. doi: 10.1016/j.jadohealth.2015.09.026

85. Ybarra ML, Boyd D, Korchmaros JD, Oppenheim J. Defining and measuring cyberbullying within the larger context of bullying victimization. J Adolesc Health. (2012) 51:53–8. doi: 10.1016/j.jadohealth.2011.12.031

86. Kowalski RM, Limber SP, McCord A. A developmental approach to cyberbullying: prevalence and protective factors. Aggress Violent Behav. (2019) 45:20–32. doi: 10.1016/j.avb.2018.02.009

87. Dilmac B, Yurt E, Aydin M, Kasarci I. Predictive relationship between humane values of adolescents cyberbullying and cyberbullying sensibility. Electron J Res Educ Psychol. (2016) 14:3–22. doi: 10.14204/ejrep.38.14123

88. Reed KP, Cooper RL, Nugent WR, Russell K. Cyberbullying: a literature review of its relationship to adolescent depression and current intervention strategies. J Hum Behav Soc Environ. (2016) 26:37–45. doi: 10.1080/10911359.2015.1059165

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

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

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

Reviewed by:

Copyright © 2021 Zhu, Huang, Evans and Zhang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Wei Zhang, weizhanghust@hust.edu.cn

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

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

78 Cyber Bullying Essay Topic Ideas & Examples

🏆 best cyber bullying topic ideas & essay examples, 💡 interesting topics to write about cyber bullying, 👍 good essay topics on cyber bullying, ❓ questions about cyberbullying research.

• Cyber Bullying Issue Therefore, the goal of this paper is to analyse who the victims of cyber bullying are and the influence it has on them.
• Is Cyber Bullying Against Teenagers More Detrimental Than Face-To-Face Bullying? Social networking has also contributed greatly to the issue of cyber bullying especially in making it more harmful as compared to face-to-face bullying.
• Cyber Bullying and Positivist Theory of Crime Learning theory approaches to the explanation of criminal behavior have been associated with one of the major sociological theories of crime, the differential association theory.
• Cyber Bullying Reduction Program Table of Activities Activity Significance Assembling parents/guardians, students and teachers to announce and explain the program in the institution To enlighten parents/guardians, students and teachers about the rules and regulation enacted due to the threat […]
• Cyber Bullying Prevention in Learning Institutions: Systematic Approach To start with, the students are provided with ways of reporting their concern to the educational institution, and when the staff members of the institution receive the report, they evaluate the information together with the […]
• Discouraging and Eliminating Cyber Bullying Resources Role of the resource/input Statement forms To facilitate information transfer to the staff Counseling Personnel To arm students against the problem Bullying report system To create efficient internet enhance report system Regulation implementation documents […]
• Cyber Bullying and Its Forms The difference between the conventional way of bullying and cyber bullying is that in conventional bullying, there is contact between the bully and the victim.
• Ethics in Technology: Cyber Crimes Furthermore, the defendant altered the data, which compromised the integrity of the information to the detriment of the organizations involved. In this litigation, Aleksey Vladimirovich Ivanov was the defendant while the American government was the […]
• Cyber Bullying as a Virtual Menace The use of information and communication technologies to support a deliberate and most of the time repeated hostile behavior by an individual or groups of people with the sole intention of harming others, one is […]
• Ethical Case: Facebook Gossip or Cyberbullying? The best option to Paige is to apologize publicly and withdraw her comments. The final stage is to act and reflect the outcome of the choice made.
• Freedom Of Speech In The Era Of Cyber Bullying
• The Negative Impacts of Technology on Social Skills: Anxiety, Awkward Conversations, Cyber Bullying, and Lack of Awareness
• Different Consequences of Cyber Bullying in School
• The Study Of Cyber Bullying Victimization On Children Who Are Addicted To The Internet
• The Causes and Harmful Effects of Cyber Bullying
• Why Do Cyber Bullying Laws Need to Be Enforced
• Unsecured Privacy Settings, Cyber Bullying, And Facebook Crime
• Bullying Carried too Far: Cyber Bullying and Violent Bullying
• Cyber Bullying: Misuse of Information and Communications Technology
• Cyber Bullying and Why Parents Need to Monitor Their Children’s Activity
• The Detrimental Effects of Cyber Bullying
• Cyber Bullying, Its Forms, Impact, and Relationship to Juvenile Delinquency
• How Cyber Bullying Affects Our Lives Negatively
• The Effects Of Cyber Bullying On Substance Use And Mental Health
• Cyberbullying : Causes And Dangers Of Cyber Bullying
• The Effects Of Cyber Bullying On The Mental Health Of Middle School Aged Youth
• Is Cyber Bullying Morally Justifiable
• Cyber Bullying And Its Effect On Our Youth
• An Analysis of Cyber Bullying in Today’s World
• Cyber Bullying And Its Effect On The Lives Of The American
• Bullying And The Potential Motives Behind Cyber Bullying
• Cyber Bullying And Its Various Forms
• Bullying In The Digital Age: Electronic Or Cyber Bullying
• Information Technology – Role of Social Networking Cites in Cyber Bullying
• Cyber Bullying : A Consistent Problem For Young People
• Cause And Effect Of Cyber Bullying
• Cyber Bullying, Creating a Culture of Respect
• Cyber Bullying And Its Effect On Adolescents
• Prevention And Intervention Of Cyber Bullying
• Investigating Cyber Bullying Using Social Media
• Cyber Bullying Affects People ‘s Lives More Than One Might Think
• The Cyber Crime and the Cyber Bullying
• The Cause of Cyber Bullying and the Effect of the Mental Development of Teenagers
• Cyber Bullying: An Uncontrollable Epidemic
• The Psychological Impact of Cyber Bullying
• The Eternal Effects Of Cyber Bullying
• Cyber Bullying : Bullying Through Technology
• Why Does Online Anonymity Increase Cyberbullying Among Teenagers?
• Are Laws Effective Strategy Address Issue Cyberbullying?
• Are Schools Doing Enough About Cyberbullying?
• What Are the Causes of Cyberbullying?
• What Is the Prevention of Cyberbullying?
• Is Cyberbullying Related to a Lack of Empathy and Social-Emotional Problems?
• How Often Do Celebrities Suffer From Cyberbullying?
• What Are the Characteristics of Cyberbullying Among Students?
• How Does Social Integration of Children Help to Combat Cyberbullying?
• What Is the Correlation Between Suicide Rates and Cyberbullying?
• How Does Cyberbullying Affect Society?
• What Is the Correlation Between Depression, Bullying and Cyberbullying?
• Are There Gender Differences in Cyberbullying?
• What Is the Criminal Penalty for Cyberbullying?
• What International Associations Prevent Cyberbullying?
• What Is the Role of Affective and Cognitive Empathy in Cyberbullying?
• What Are the Solutions to Cyberbullying?
• Can Cyberbullying Be Called Cyber Crime?
• What Is the Role of Teachers in Preventing Cyberbullying?
• Can Internet Privacy Be Enough to Prevent Cyberbullying?
• How Does Cyberbullying Affect Children?
• How Many American Teenagers Are Cyberbullied?
• How Does Cyberbullying Affect Mental Health?
• How Is Cyberbullying Different From Physical Bullying?
• Is Cyberbullying an Example of Psychological Abuse?
• Can School Policies Reduce Cyberbullying?
• How Does Cyberbullying Affect Teenagers’ Self-Esteem?
• What Are the Consequences of Cyberbullying?
• Has the Proliferation of Social Media Led to an Increase in Cyberbullying?
• Is Cyberbullying Less Criminal Than Traditional Bullying?
• Cyber Security Topics
• Cyberspace Topics
• Crime Ideas
• Mental Health Essay Ideas
• Fake News Research Ideas
• Internet Research Ideas
• Freedom of Speech Ideas
• Online Community Essay Topics
• Chicago (A-D)
• Chicago (N-B)

IvyPanda. (2023, October 26). 78 Cyber Bullying Essay Topic Ideas & Examples. https://ivypanda.com/essays/topic/cyber-bullying-essay-examples/

"78 Cyber Bullying Essay Topic Ideas & Examples." IvyPanda , 26 Oct. 2023, ivypanda.com/essays/topic/cyber-bullying-essay-examples/.

IvyPanda . (2023) '78 Cyber Bullying Essay Topic Ideas & Examples'. 26 October.

IvyPanda . 2023. "78 Cyber Bullying Essay Topic Ideas & Examples." October 26, 2023. https://ivypanda.com/essays/topic/cyber-bullying-essay-examples/.

1. IvyPanda . "78 Cyber Bullying Essay Topic Ideas & Examples." October 26, 2023. https://ivypanda.com/essays/topic/cyber-bullying-essay-examples/.

Bibliography

IvyPanda . "78 Cyber Bullying Essay Topic Ideas & Examples." October 26, 2023. https://ivypanda.com/essays/topic/cyber-bullying-essay-examples/.