A REVIEW OF CYBERBULLYING DETECTION ON SOCIAL MEDIA
DOI:
https://doi.org/10.35631/IJLGC.1143028Keywords:
Cyberbullying, Cyberbullying Detection, Machine Learning, NLP, Evaluation MetricAbstract
The digital world is evolving quickly today. The way people communicate no longer relies on traditional methods like telegraphs, letters, and phone calls. The internet has transformed the global scene and affected social interaction, communication, and information sharing. This progress has led to the growth of digital social media platforms that enable all forms of knowledge sharing and communication, helping users. However, among these features, some users engage in risky behaviours such as cyberbullying and harassment. This paper explores cyberbullying, social media, and machine learning, outlining their definitions, categories, and functions. It reviews detection methods for cyberbullying, focusing on data sources, feature extraction, evaluation metrics, and classification techniques, especially Machine Learning and Natural Language Processing (NLP). The paper identifies gaps, assesses current approaches, and proposes future research directions.
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