How to identify Cyberbullying with Machine Learning - Fujimoto, M. L.; Gaseta, M.; Rezende, S. O.; Romero, R. A. F. (Anais do Symposium on Knowledge Discovery, Mining and Learning (KDMiLe); 2024: Anais do XII Symposium on Knowledge Discovery, Mining and LearningDO - 10.5753/kdmile.2024.244087, 2024)
Cyberbullying is a form of bullying that has emerged and is a concerning problem with the exponential increase of social media users. Social networks provide a suitable environment for those bullies to attack and cause serious psychological problems in their victims. To mitigate these issues, proactive measures are essential to detect and prevent cyberbullying before disseminating harmful content. With this concern in mind, this article proposes an approach to combine TF-IDF with machine learning models to automatically identify cyberbullying. These models are evaluated using metrics such as accuracy and F1-score to identify and classify cyberbullying instances. The research aims to contribute to the development of automated systems capable of preemptively addressing cyberbullying on social media platforms.