Cyberbullying Detection: Exploring Datasets, Technologies, and Approaches on Social Media Platforms
Adamu Gaston Philipo, Doreen Sebastian Sarwatt, Jianguo Ding, Mahmoud Daneshmand, Huansheng Ning

TL;DR
This paper systematically reviews cyberbullying detection on social media, analyzing datasets, technologies, and approaches, highlighting gaps and proposing future research directions.
Contribution
It provides a comprehensive overview of existing studies, identifies research gaps, and suggests effective solutions for cyberbullying detection and prevention.
Findings
Reviewed multiple datasets and detection approaches
Identified key challenges and gaps in current research
Proposed future directions for effective solutions
Abstract
Cyberbullying has been a significant challenge in the digital era world, given the huge number of people, especially adolescents, who use social media platforms to communicate and share information. Some individuals exploit these platforms to embarrass others through direct messages, electronic mail, speech, and public posts. This behavior has direct psychological and physical impacts on victims of bullying. While several studies have been conducted in this field and various solutions proposed to detect, prevent, and monitor cyberbullying instances on social media platforms, the problem continues. Therefore, it is necessary to conduct intensive studies and provide effective solutions to address the situation. These solutions should be based on detection, prevention, and prediction criteria methods. This paper presents a comprehensive systematic review of studies conducted on…
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