# Cyberbullying in Text Content Detection: An Analytical Review

arXiv: https://arxiv.org/abs/2303.10502 · HTML: https://mlsanity.com/paper/2303.10502/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Mar 2023.

## Built on by (most cited first, 2 of 2)

- [CautionSuicide: A Deep Learning Based Approach for Detecting Suicidal Ideation in Real Time Chatbot Conversation](https://mlsanity.com/paper/2401.01023/) (2024)
- [ToxiTrace: Gradient-Aligned Training for Explainable Chinese Toxicity Detection](https://mlsanity.com/paper/2604.12321/) (2026)

## Abstract (arXiv)

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.