Abstract: Cyberbullying has emerged as a pervasive and harmful phenomenon in the age of technology, affecting individuals of all ages and across various online platforms. As the prevalence of cyberbullying continues to grow, the need for effective detection systems becomes crucial to protect and support victims. A comprehensive survey on methodologies and challenges related to cyberbullying detection systems is presented in the paper. The survey explores a diverse array of techniques and approaches employed in cyberbullying detection, including machine learning, natural language processing, social network analysis, and sentiment analysis. Various data sources and features utilised for detection are examined, such as text-based content, user behaviour patterns, and social interactions. Additionally, the paper discusses the challenges faced by cyberbullying detection systems, such as the evolving nature of cyberbullying tactics, the contextual complexity of online interactions, and the ethical considerations surrounding privacy and bias. The study expands on prospective topics for further research and development and points out the shortcomings of the approaches now in use.
Keywords: cyberbullying detection systems, social network analysis, sentiment analysis, natural language processing
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DOI:
10.17148/IJARCCE.2023.12681
