Cyberbullying Messages Detection: A Comparative Study of Machine Learning Algorithms
Philippe Ea, Paul Vidart, Osman Salem, Ahmed Mehaoua · 2023
Cyberbullying is a growing concern, with serious consequences especially for children. In this paper, we propose a machine learning approach to detect cyberbullying messages accurately and distinguish them from regular ones. We used a public dataset of social media messages to fit a binary classification of either cyberbullying or non-bullying messages. We compared 15 classifiers using two methods: Term Frequency-Inverse Document Frequency (TF-IDF) for traditional algorithms and word embedding for deep learning algorithms. The voting classifier, a combination of the best algorithms from the first method, achieved the highest accuracy of 96.5% during tests. This approach can be used in social media or chat applications to detect and prevent cyberbullying.