Revue d'Information Scientifique et Technique

Arabic Hate speech and social networks offensive language detection

The containment measures caused by the coronavirus pandemic have stimulated the use of social networks as a means of exchanging information, communication, and combating social distancing. This paper presents our participation in the NLP Challenge2022 competition initiated by RESEARCH CENTRE FOR SCIENTIFIC AND TECHNICAL INFORMATION (CERIST). The competition focuses on the task of detecting Arabic hate speech and offensive language on social networks, specifically analyzing Twitter messages related to the COVID-19 pandemic and classifying users’ sentiments as either hateful or not. In the present work, we propose a model based on recurrent neural networks, more precisely the Bidirectional long-term memory (Bi-LSTM). We trained the model using a dataset constructed by the authors of this challenge. As a result, we achieves an accuracy of 96.35 %.

Auteurs : Hakim Bouchal , Ahror BELAID

Téléchargement : PDF

Catégorie : Non classé