Machine Learning Approaches for Detecting Hate-Driven Violence on Social Media Abuhamda, Yousef García Teodoro, Pedro hate-driven violence social media machine learning 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. 2025-10-24T08:44:17Z 2025-10-24T08:44:17Z 2025-10 journal article Applied Sciences, 15(21), 11323, Special Issue "Novel Applications of Machine Learning and Bayesian Optimization, 2nd Edition" https://hdl.handle.net/10481/107403 https://doi.org/10.3390/app152111323 eng http://creativecommons.org/licenses/by-nc-nd/3.0/ open access Creative Commons Attribution-NonCommercial-NoDerivs 3.0 License MDPI