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A Review of Machine Learning Techniques in Cyberbullying Detection

Daniyar Sultan1,2,*, Batyrkhan Omarov3, Zhazira Kozhamkulova4, Gulnur Kazbekova5, Laura Alimzhanova1, Aigul Dautbayeva6, Yernar Zholdassov1, Rustam Abdrakhmanov3

1 Al-Farabi Kazakh National University, Almaty, Kazakhstan
2 International Information Technology University, Almaty, Kazakhstan
3 International University of Tourism and Hospitality, Turkistan, Kazakhstan
4 Almaty University of Power Engineering and Telecommunications, Almaty, Kazakhstan
5 Khoja Akhmet Yassawi International Kazakh-Turkish University, Turkistan, Kazakhstan
6 Korkyt Ata Kyzylorda State University, Kyzylorda, Kazakhstan

* Corresponding Author: Daniyar Sultan. Email: email

Computers, Materials & Continua 2023, 74(3), 5625-5640. https://doi.org/10.32604/cmc.2023.033682

Abstract

Automatic identification of cyberbullying is a problem that is gaining traction, especially in the Machine Learning areas. Not only is it complicated, but it has also become a pressing necessity, considering how social media has become an integral part of adolescents’ lives and how serious the impacts of cyberbullying and online harassment can be, particularly among teenagers. This paper contains a systematic literature review of modern strategies, machine learning methods, and technical means for detecting cyberbullying and the aggressive command of an individual in the information space of the Internet. We undertake an in-depth review of 13 papers from four scientific databases. The article provides an overview of scientific literature to analyze the problem of cyberbullying detection from the point of view of machine learning and natural language processing. In this review, we consider a cyberbullying detection framework on social media platforms, which includes data collection, data processing, feature selection, feature extraction, and the application of machine learning to classify whether texts contain cyberbullying or not. This article seeks to guide future research on this topic toward a more consistent perspective with the phenomenon’s description and depiction, allowing future solutions to be more practical and effective.

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APA Style
Sultan, D., Omarov, B., Kozhamkulova, Z., Kazbekova, G., Alimzhanova, L. et al. (2023). A review of machine learning techniques in cyberbullying detection. Computers, Materials & Continua, 74(3), 5625-5640. https://doi.org/10.32604/cmc.2023.033682
Vancouver Style
Sultan D, Omarov B, Kozhamkulova Z, Kazbekova G, Alimzhanova L, Dautbayeva A, et al. A review of machine learning techniques in cyberbullying detection. Comput Mater Contin. 2023;74(3):5625-5640 https://doi.org/10.32604/cmc.2023.033682
IEEE Style
D. Sultan et al., "A Review of Machine Learning Techniques in Cyberbullying Detection," Comput. Mater. Contin., vol. 74, no. 3, pp. 5625-5640. 2023. https://doi.org/10.32604/cmc.2023.033682



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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