Deep learning approach for Tunisian hate Speech detection on Facebook

We have witnessed a sharp increase in violence in Tunisia over the past few years. Violence affecting households, minorities, political parties, and public figures has increased more widely on social media. As a result, it has become easier for extremist, racist, misogynistic, and offensive articles, posts, and comments to be shared. Today, various international and governmental groups vowed to fight internet hate speech. This paper proposes a deep-learning solution to find hateful and offensive speech on Arabic social media sites like Facebook. We introduce two models: a Bi-LSTM based on an attention mechanism with integrating the BERT for Facebook comment classification toward hate speech detection. For this task, we collected 2k Tunisian dialect comments from Facebook. The proposed approach has been evaluated on three datasets, and the obtained results demonstrate that the proposed models can improve Arabic hate detection with an accuracy of 98.89%.