A Literature Review of Machine Learning Techniques for Cyberbullying Detection in Arabic Social Media Text
A Literature Review of Machine Learning Techniques for Cyberbullying Detection in Arabic Social Media Text |
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| © 2025 by IJETT Journal | ||
| Volume-73 Issue-3 |
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| Year of Publication : 2025 | ||
| Author : Bader Azi Alanazi, Chin-Teng Lin |
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| DOI : 10.14445/22315381/IJETT-V73I3P106 | ||
How to Cite?
Bader Azi Alanazi, Chin-Teng Lin, "A Literature Review of Machine Learning Techniques for Cyberbullying Detection in Arabic Social Media Text," International Journal of Engineering Trends and Technology, vol. 73, no. 3, pp. 73-94, 2025. Crossref, https://doi.org/10.14445/22315381/IJETT-V73I3P106
Abstract
Social media, including platforms such as X (known as Twitter), have become an integral part of our daily lives, serving as a primary means of communication for people globally. Social media are widely used to share thoughts, viewpoints, and critiques. However, the accessibility of these platforms can sometimes lead to misuse, paving the way for cyberbullying–a damaging form of online harassment. Although much research has been conducted on cyberbullying detection in the English language, there is a noticeable research gap regarding the Arabic language. Spotting cyberbullying in Arabic posts on X (Twitter) can help make the platform safer and friendlier for Arabic users while highlighting the harm it inflicts. Cyberbullying instances can be identified and categorized using tools such as Natural Language Processing (NLP) and machine learning algorithms. This paper reviews studies that leverage Machine Learning to identify instances of cyberbullying in Arabic.
Keywords
Machine Learning, Cyberbullying, Arabic text, Twitter, Natural language processing.
References
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