Machine Learning Approaches for Detecting Hate-Driven Violence on Social Media

Abstract

Cyberbullying and hate-driven behavior on social media have become increasingly prevalent, posing serious psychological and social risks. This study proposes a machine learning-based approach to detect hate-driven content by integrating temporal and behavioral features—such as message frequency, interaction duration, and user activity patterns—alongside traditional text-based features. Furthermore, we extend our evaluation to include recent neural network architectures, namely ALBERT and BiLSTM, enabling a more robust representation of semantic and sequential patterns. Building on our previous research presented at JNIC-2024, we conduct a comparative evaluation of multiple classification algorithms using both existing and engineered datasets. The results show that incorporating non-textual features significantly improves detection accuracy and robustness. This work contributes to the development of intelligent cyberbullying detection systems and highlights the importance of behavioral context in online threat analysis.