Skip to main content

Advertisement

SpringerLink
Log in

A study of machine learning-based models for detection, control, and mitigation of cyberbullying in online social media

  • Survey
  • Published:
International Journal of Information Security Aims and scope Submit manuscript

Abstract

Online social media (OSM) is an integral part of human life these days. Significantly, the young generation spends most of their time on social media in an active and passive state. The exponential growth of OSM has created an atmosphere of increased cybercrime. Although OSM provides a platform to connect people with similar thoughts and interests, it also exposes vulnerable users to mischievous elements in cyberspace. Social media connects and generates a massive amount of human activity-related data. However, the misuse of OSM introduces a novel way of expressing aggression and violence that exclusively happens online. In this research paper, we briefly discuss the background of Cyberbullying and the various machine and deep learning-based models incorporated to deal with it effectively. We also highlight the main challenges in designing a cyberbullying prediction model and address them.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

Data availability statement

Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.

References

  1. Cénat, J.M., Hébert, M., Blais, M., Lavoie, F., Guerrier, M., Derivois, D.: Cyberbullying, psychological distress and self-esteem among youth in Quebec schools. J. Affect. Disord. 169, 7–9 (2014)

    Article  Google Scholar 

  2. Al-Garadi, M.A., Hussain, M.R., Khan, N., Murtaza, G., Nweke, H.F., Ali, I., Gani, A.: 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 (2019)

    Article  Google Scholar 

  3. Zhao, R., Mao, K.: Cyberbullying detection based on semantic-enhanced marginalized denoising auto-encoder. IEEE Trans. Affect. Comput. 8(3), 328–339 (2016)

    Article  Google Scholar 

  4. "UNICEF POLL:More than a third of young people in 30 countries report being a victim of online bullying",2019,[online] Available: https://www.unicef.org/press-releases/unicef-poll-more-third-young-people-30-countries-report-being-victim-online-bullying

  5. "Cyberbullying Statistics", 2019,[online] Available: https://enough.org/stats_cyberbullying

  6. Summary of Our Cyberbullying Research. 2007–2019,[online]https://cyberbullying.org/summary-of-our-cyberbullying-research

  7. Sargar, B., Kattimani, R.: Emerging trends and issues in social sciences (2020)

  8. Sampasa-Kanyinga, H., Roumeliotis, P., Xu, H.: Associations between cyberbullying and school bullying victimization and suicidal ideation, plans and attempts among Canadian schoolchildren. PLoS ONE 9(7), e102145 (2014)

    Article  Google Scholar 

  9. Hinduja, S., Patchin, J.W.: Bullying, cyberbullying, and suicide. Arch. Suicide Res. 14(3), 206–221 (2010)

    Article  Google Scholar 

  10. Hosseinmardi, H., Li, S., Yang, Z., Lv, Q., Rafiq, R.I., Han, R., & Mishra, S.: A comparison of common users across instagram and ask. fm to better understand cyberbullying. In 2014 IEEE Fourth International Conference on Big Data and Cloud Computing (pp. 355–362). IEEE (2014)

  11. Maher, D.: Cyberbullying: an ethnographic case study of one Australian upper primary school class. Youth Stud. Austr. 27(4), 50 (2008)

    MathSciNet  Google Scholar 

  12. Willard, N.E.: Cyberbullying and Cyberthreats: Responding to the Challenge of Online Social Aggression, Threats, and Distress. Research Press, Delhi (2007)

    Google Scholar 

  13. Patchin, J.W., Hinduja, S.: Bullies move beyond the schoolyard: A preliminary look at cyberbullying. Youth Violence Juvenile Justice 4(2), 148–169 (2006)

    Article  Google Scholar 

  14. Bayzick, J., Kontostathis, A., Edwards, L.: Detecting the presence of cyberbullying using computer software (2011)

  15. Bishop, J.: Tackling Internet abuse in Great Britain: Towards a framework for classifying severities of'flame trolling'. In: Proceedings of the International Conference on Security and Management (SAM) (p. 1). The Steering Committee of The World Congress in Computer Science, Computer Engineering and Applied Computing (WorldComp) (2012)

  16. Bishop, J.: Representations of “trolls” in mass media communication: a review of media-texts and moral panics relating to “internet trolling.” Int. J. Web Based Commun. 10(1), 7–24 (2014)

    Article  Google Scholar 

  17. Bhat, A.: An analysis of crime data under apache pig on big data. In: 2019 Third International conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC) (pp. 330–335). IEEE (2019)

  18. "Cyberbullying Statistics, Facts and Trend. [online] https://firstsiteguide.com/cyberbullying-stats/ (2020)

  19. González-Bailón, S., Wang, N., Rivero, A., Borge-Holthoefer, J., Moreno, Y.: Assessing the bias in samples of large online networks. Social Netw. 38, 16–27 (2014)

    Article  Google Scholar 

  20. Akhter, S.: Social media bullying detection using machine learning on Bangla text. In: 2018 10th International Conference on Electrical and Computer Engineering (ICECE) (pp. 385–388). IEEE (2018)

  21. Monika, A., Bhat, A.: Automatic Twitter crime prediction using hybrid wavelet convolutional neural network with world cup optimization. Int. J. Pattern Recognit. Artif. Intell. 36(05), 2259005 (2022)

  22. Chavan, V.S., & Shylaja, S.S.: 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 (2015)

  23. Eshan, S.C., & Hasan, M.S.: An application of machine learning to detect abusive bengali text. In: 2017 20th International Conference of Computer and Information Technology (ICCIT) (pp. 1–6). IEEE (2017). https://doi.org/10.1109/ICCITECHN.2017.8281787

  24. Lu, N., Wu, G., Zhang, Z., Zheng, Y., Ren, Y., Choo, K.K.R.: Cyberbullying detection in social media text based on character-level convolutional neural network with shortcuts. Concurr. Comput. Pract. Exp. 32(23), e5627 (2020)

    Article  Google Scholar 

  25. Zhang, J., Otomo, T., Li, L., Nakajima, S.: Cyberbullying detection on twitter using multiple textual features. In: 2019 IEEE 10th International Conference on Awareness Science and Technology (iCAST) (pp. 1–6). IEEE (2019)

  26. Mangaonkar, A., Hayrapetian, A., & Raje, R.: Collaborative detection of cyberbullying behavior in Twitter data. In 2015 IEEE International Conference on Electro/Information Technology (EIT) (pp. 611–616). IEEE (2015)

  27. Xue, B., Fu, C., Shaobin, Z.: A study on sentiment computing and classification of sina weibo with word2vec. In: 2014 IEEE International Congress on Big Data (pp. 358–363). IEEE (2014)

  28. Bozyiğit, A., Utku, S., Nasibov, E.: Cyberbullying detection: utilizing social media features. Expert Syst. Appl. 179, 115001 (2021)

    Article  Google Scholar 

  29. Isaac, A., Kumar, R., Bhat, A.: Hate speech detection using machine learning techniques. In: Sustainable Advanced Computing (pp. 125–135). Springer, Singapore (2022)

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

  31. Nalini, K., Sheela, L.J.: Classification using latent dirichlet allocation with Naive Bayes classifier to detect cyber bullying in Twitter. Indian J. Sci. Technol. 9(28), 1–5 (2016)

    Article  Google Scholar 

  32. Balakrishnan, V., Khan, S., Arabnia, H.R.: Improving cyberbullying detection using Twitter users' psychological features and machine learning. Comput. Secur. 101710 (2020)

  33. Chen, Y., Zhou, Y., Zhu, S., & Xu, H.: 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 (2012)

  34. Galán-García, P., Puerta, J.G.D.L., Gómez, C.L., Santos, I., Bringas, P.G.: Supervised machine learning for the detection of troll profiles in twitter social network: Application to a real case of cyberbullying. Logic J. IGPL 24(1), 42–53 (2016)

    MathSciNet  Google Scholar 

  35. Dinakar, K., Reichart, R., Lieberman, H.: Modeling the detection of textual cyberbullying. In: Fifth International AAAI Conference on Weblogs and Social Media (2011)

  36. Sanchez, H., Kumar, S.: Twitter bullying detection. Ser. NSDI 12(2011), 15 (2011)

    Google Scholar 

  37. Joachims, T.: Text categorization with support vector machines: Learning with many relevant features. In: European Conference on Machine Learning (pp. 137–142). Springer, Berlin, Heidelberg (1998)

  38. Andriansyah, M., Akbar, A., Ahwan, A., Gilani, N. A., Nugraha, A. R., Sari, R. N., & Senjaya, R. (2017, November). Cyberbullying comment classification on Indonesian Selebgram using support vector machine method. In 2017 Second International Conference on Informatics and Computing (ICIC) (pp. 1–5). IEEE.

  39. Ahmed, M., Goel, M., Kumar, R., & Bhat, A.: Sentiment analysis on Twitter using ordinal regression. In: 2021 International Conference on Smart Generation Computing, Communication and Networking (SMART GENCON) (pp. 1–4). IEEE (2021)

  40. León-Paredes, G.A., Palomeque-León, W.F., Gallegos-Segovia, P.L., Vintimilla-Tapia, P.E., Bravo-Torres, J.F., Barbosa-Santillán, L.I., Paredes-Pinos, M.M.: Presumptive detection of cyberbullying on twitter through natural language processing and machine learning in the Spanish language. In: 2019 IEEE CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies (CHILECON) (pp. 1–7). IEEE (2019)

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

  42. 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: Proceedings of the International Conference Recent Advances in Natural Language Processing (pp. 672–680).

  43. García-Recuero, Á.: 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). International World Wide Web Conferences Steering Committee (2016)

  44. Fazil, M., Abulaish, M.: A hybrid approach for detecting automated spammers in twitter. IEEE Trans. Inf. Forensics Secur. 13(11), 2707–2719 (2018)

    Article  Google Scholar 

  45. Soucy, P., Mineau, G.W.: A simple KNN algorithm for text categorization. In: Proceedings 2001 IEEE International Conference on Data Mining (pp. 647–648). IEEE (2001)

  46. Wolpert, D.H., Macready, W.G.: No free lunch theorems for optimization. IEEE Trans. Evol. Comput. 1(1), 67–82 (1997)

    Article  Google Scholar 

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

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

  49. Al-Ajlan, M.A., Ykhlef, M.: Optimized Twitter cyberbullying detection based on deep learning. In: 2018 21st Saudi Computer Society National Computer Conference (NCC) (pp. 1–5). IEEE (2018). https://doi.org/10.1109/NCG.2018.8593146

  50. Chu, T., Jue, K., Wang, M.: Comment abuse classification with deep learning. Von https://web.stanford.edu/class/cs224n/reports/2762092.pdfabgerufen. (2016)

  51. Mahlangu, T., Tu, C.: Deep learning cyberbullying detection using stacked embbedings approach. In: 2019 6th International Conference on Soft Computing & Machine Intelligence (ISCMI) (pp. 45–49). IEEE (2019)

  52. Haidar, B., Chamoun, M., Serhrouchni, A.: Arabic cyberbullying detection: using deep learning. In: 2018 7th International Conference on Computer and Communication Engineering (ICCCE) (pp. 284–289). IEEE (2018)

  53. Kumari, K., Singh, J.P., Dwivedi, Y.K., Rana, N.P.: Towards Cyberbullying-free social media in smart cities: a unified multimodal approach. Soft. Comput. 24(15), 11059–11070 (2020)

    Article  Google Scholar 

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

    Article  Google Scholar 

  55. Iwendi, C., Srivastava, G., Khan, S., Maddikunta, P.K.R.: Cyberbullying detection solutions based on deep learning architectures Multimed. Syst. 1–14 (2020)

  56. Kumar, A., & Sachdeva, N.: Multi-input integrative learning using deep neural networks and transfer learning for cyberbullying detection in real-time code-mix data. Multimed Syst. 1–15 (2020)

  57. Wang, K., Xiong, Q., Wu, C., Gao, M., & Yu, Y.: Multimodal cyberbullying detection on social networks. In: 2020 International Joint Conference on Neural Networks (IJCNN) (pp. 1–8). IEEE (2020)

  58. Paul, S., Saha, S., Hasanuzzaman, M.: Identification of cyberbullying: a deep learning based multimodal approach. Multimed. Tools Appl., 1–20 (2020). https://doi.org/10.1007/s11042-020-09631-w

  59. Pawar, R., & Raje, R.R.: Multilingual cyberbullying detection system. In: 2019 IEEE International Conference on Electro Information Technology (EIT) (pp. 040–044). IEEE (2019)

  60. Wang, H., Castanon, J.A.: Sentiment expression via emoticons on social media. In: 2015 IEEE International Conference on Big Data (Big Data) (pp. 2404–2408). IEEE (2015)

  61. Singh, V.K., Huang, Q., Atrey, P.K.: Cyberbullying detection using probabilistic socio-textual information fusion. In: 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) (pp. 884–887). IEEE (2016). https://doi.org/10.1109/ASONAM.2016.7752342

  62. Grasso, G., Furche, T., Schallhart, C.: Effective web scraping with oxpath. In: Proceedings of the 22nd International Conference on World Wide Web (pp. 23–26) (2013). https://doi.org/10.1145/2487788.2487796

  63. Van Hee, C., Lefever, E., Verhoeven, B., Mennes, J., Desmet, B., De Pauw, G., Hoste, V.: Automatic detection and prevention of cyberbullying. In: International Conference on Human and Social Analytics (HUSO 2015) (pp. 13–18). IARIA (2015)

  64. Haidar, B., Chamoun, M., Serhrouchni, A.: Arabic cyberbullying detection: enhancing performance by using ensemble machine learning. In: 2019 International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData) (pp. 323–327). IEEE (2019)

  65. Gupta, P., Kaushik, B.: Suicidal tendency on social media: a case study. In: 2019 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COMITCon) (pp. 273–276). IEEE (2019)

  66. Miller, G.A., Beckwith, R., Fellbaum, C., Gross, D., Miller, K.J.: Introduction to WordNet: an online lexical database. Int. J. Lexicogr. 3(4), 235–244 (1990)

    Article  Google Scholar 

  67. AlMaayah, M., Sawalha, M., Abushariah, M.A.: Towards an automatic extraction of synonyms for Quranic Arabic WordNet. Int. J. Speech Technol. 19(2), 177–189 (2016)

    Article  Google Scholar 

  68. Fryling, M., Cotler, J.L., Rivituso, J., Mathews, L., Pratico, S.: Cyberbullying or normal game play? Impact of age, gender, and experience on cyberbullying in multiplayer online gaming environments: perceptions from one gaming forum. J. Inf. Syst. Appl. Res. 8(1), 4 (2015)

    Google Scholar 

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

  70. Kontostathis, A., Reynolds, K., Garron, A., Edwards, L.: Detecting cyberbullying: query terms and techniques. In: Proceedings of the 5th Annual acm Web Science Conference (pp. 195–204) (2013)

  71. Yin, D., Xue, Z., Hong, L., Davison, B.D., Kontostathis, A., Edwards, L.: Detection of harassment on web 2.0. Proc. Content Anal. WEB 2, 1–7 (2009)

    Google Scholar 

  72. Bosse, T., & Stam, S.: A normative agent system to prevent cyberbullying. In: Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology-Volume 02 (pp. 425–430). IEEE Computer Society (2011)

  73. Al-garadi, M.A., Varathan, K.D., Ravana, S.D.: Cybercrime detection in online communications: the experimental case of cyberbullying detection in the Twitter network. Comput. Hum. Behav. 63, 433–443 (2016)

    Article  Google Scholar 

  74. Zhang, X., Tong, J., Vishwamitra, N., Whittaker, E., Mazer, J.P., Kowalski, R., & Dillon, E.: Cyberbullying detection with a pronunciation based convolutional neural network. In: 2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA) (pp. 740–745). IEEE (2016)

  75. Kumar, R., & Bhat, A.: An analysis on sarcasm detection over twitter during COVID-19. In: 2021 2nd International Conference for Emerging Technology (INCET) (pp. 1–6). IEEE (2021)

  76. Rendalkar, S., & Chandankhede, C.: Sarcasm detection of online comments using emotion detection. In: 2018 International Conference on Inventive Research in Computing Applications (ICIRCA) (pp. 1244–1249). IEEE (2018)

  77. Shrivastava, M., Kumar, S.: A pragmatic and intelligent model for sarcasm detection in social media text. Technol. Soc. 64, 101489 (2021). https://doi.org/10.1016/j.techsoc.2020.101489

    Article  Google Scholar 

  78. Cheng, L., Li, J., Silva, Y.N., Hall, D.L., Liu, H.: Xbully: cyberbullying detection within a multimodal context. In: Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining (pp. 339–347) (2019)

  79. Suryawanshi, S., Chakravarthi, B.R., Arcan, M., Buitelaar, P.: Multimodal meme dataset (MultiOFF) for identifying offensive content in image and text. In: Proceedings of the Second Workshop on Trolling, Aggression and Cyberbullying (pp. 32–41) (2020).

  80. Yuvaraj, N., Srihari, K., Dhiman, G., Somasundaram, K., Sharma, A., Rajeskannan, S., Masud, M.: Nature-inspired-based approach for automated cyberbullying classification on multimedia social networking. Math. Probl. Eng. 2021 (2021)

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

  82. Karimvand, A.N., Chegeni, R.S., Basiri, M.E., Nemati, S.: Sentiment analysis of persian instagram post: a multimodal deep learning approach. In: 2021 7th International Conference on Web Research (ICWR) (pp. 137–141). IEEE (2021)

  83. Sangwan, S., Akhtar, M.S., Behera, P., Ekbal, A.: I didn't mean what I wrote! Exploring Multimodality for Sarcasm Detection. In: 2020 International Joint Conference on Neural Networks (IJCNN) (pp. 1–8). IEEE (2020)

  84. Yao, F., Sun, X., Yu, H., Zhang, W., Liang, W., Fu, K.: Mimicking the brain's cognition of sarcasm from multidisciplines for Twitter sarcasm detection. IEEE Trans. Neural Netw. Learn. Syst. (2021)

  85. Wu, Y., Zhao, Y., Lu, X., Qin, B., Wu, Y., Sheng, J., Li, J.: Modeling incongruity between modalities for multimodal sarcasm detection. IEEE MultiMed. (2021)

  86. Amrutha, B.R., Bindu, K.R.: Detecting hate speech in tweets using different deep neural network architectures. In: 2019 International Conference on Intelligent Computing and Control Systems (ICCS) (pp. 923–926). IEEE (2019)

  87. Jain, D., Kumar, A., Garg, G.: Sarcasm detection in mash-up language using soft-attention based bi-directional LSTM and feature-rich CNN. Appl. Soft Comput. 91, 106198 (2020)

    Article  Google Scholar 

  88. Rezvani, N., Beheshti, A., Tabebordbar, A.: Linking textual and contextual features for intelligent cyberbullying detection in social media. In: Proceedings of the 18th International Conference on Advances in Mobile Computing & Multimedia (pp. 3–10) (2020). https://doi.org/10.1145/3428690.3429171

  89. Das, D., Clark, A.J.: Sarcasm detection on facebook: a supervised learning approach. In: Proceedings of the 20th International Conference on Multimodal Interaction: Adjunct (pp. 1–5) (2018)

  90. Li, L., Levi, O., Hosseini, P., Broniatowski, D.A.: A multi-modal method for satire detection using textual and visual cues. arXiv:2010.06671. (2020). https://doi.org/10.48550/arXiv.2010.06671

  91. El Asam, A., Samara, M.: Cyberbullying and the law: a review of psychological and legal challenges. Comput. Hum. Behav. 65, 127–141 (2016). https://doi.org/10.1016/j.chb.2016.08.012

    Article  Google Scholar 

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

  93. Kumar, A., Garg, G.: Empirical study of shallow and deep learning models for sarcasm detection using context in benchmark datasets. J. Ambient Intell. Human. Comput. 1–16 (2019)

  94. Pei, Z., Sun, Z., Xu, Y.: Slang detection and identification. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL) (pp. 881–889) (2019)

  95. Wilson, S., Magdy, W., McGillivray, B., Garimella, K., Tyson, G.: Urban dictionary embeddings for slang NLP applications. In: Proceedings of the 12th Language Resources and Evaluation Conference (pp. 4764–4773) (2020)

  96. Gopika, N.: Correlation based feature selection algorithm for machine learning. In: 2018 3rd International Conference on Communication and Electronics Systems (ICCES) (pp. 692–695). IEEE (2018). https://doi.org/10.1109/CESYS.2018.8723980

  97. Dash, M., Liu, H.: Feature selection for classification. Intell. Data Anal. 1(3), 131–156 (1997). https://doi.org/10.1016/S1088-467X(97)00008-5

    Article  Google Scholar 

  98. Koller, D., Sahami, M.: Toward Optimal Feature Selection. Stanford InfoLab, Stanford (1996)

    Google Scholar 

  99. Dadvar, M., Trieschnigg, D., Ordelman, R., de Jong, F.: Improving cyberbullying detection with user context. In: European conference on information retrieval (pp. 693–696). Springer, Berlin, Heidelberg (2013)

  100. Zhong, H., Li, H., Squicciarini, A.C., Rajtmajer, S.M., Griffin, C., Miller, D.J., Caragea, C.: Content-driven detection of cyberbullying on the instagram social network. In: IJCAI (pp. 3952–3958) (2016)

  101. Li, H.: Image Analysis of Cyberbullying using Machine Learning Techniques (2015)

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

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

Download references

Funding

Any agency does not fund this study.

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Delhi Technological University, Delhi, India

    Raju Kumar & Aruna Bhat

Authors
  1. Raju Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Aruna Bhat
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Aruna Bhat.

Ethics declarations

Conflict of interest

All the authors of this paper declare that he/she has no conflict of interest.

Ethical approval

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

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 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

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kumar, R., Bhat, A. A study of machine learning-based models for detection, control, and mitigation of cyberbullying in online social media. Int. J. Inf. Secur. 21, 1409–1431 (2022). https://doi.org/10.1007/s10207-022-00600-y

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10207-022-00600-y

Keywords

Search

Navigation