Cyberbullying Detection: Exploring Datasets, Technologies, and Approaches on Social Media Platforms

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 cyberbullying detection. It explores existing studies, proposed solutions, identified gaps, datasets, technologies, approaches, challenges, and recommendations, and then proposes effective solutions to address research gaps in future studies.