Cyberbullying in Text Content Detection: An Analytical Review

TL;DR

This paper provides a comprehensive review of cyberbullying detection methods in text content, highlighting challenges and advancements in identifying abusive online communications.

Contribution

It offers an analytical review of existing literature on cyberbullying detection, emphasizing textual factors and data challenges.

Findings

Most approaches focus on textual analysis for detection

Data scarcity remains a significant challenge

Advancements have improved detection accuracy

Abstract

Technological advancements have resulted in an exponential increase in the use of online social networks (OSNs) worldwide. While online social networks provide a great communication medium, they also increase the user's exposure to life-threatening situations such as suicide, eating disorder, cybercrime, compulsive behavior, anxiety, and depression. To tackle the issue of cyberbullying, most existing literature focuses on developing approaches to identifying factors and understanding the textual factors associated with cyberbullying. While most of these approaches have brought great success in cyberbullying research, data availability needed to develop model detection remains a challenge in the research space. This paper conducts a comprehensive literature review to provide an understanding of cyberbullying detection.