SMOTE for enhancing Tunisian Hate Speech detection on social media with machine learning

In the last decade, the world has witnessed remarkable technological development, especially in artificial intelligence, which helps researchers find solutions to problems of concern to the individual and society, mainly, the huge propagation of hate speech with the increased use of social media platforms. In this study, we aim to enhance the detection of Arabic hate speech on social media by addressing challenges related to imbalanced datasets through data augmentation techniques. Several machine learning algorithms and the DziriBert, a pre-trained transformer model, are implemented on the Tunisian Hate Speech and Abusive Dataset (T-HSAB). The proposed approach achieves good results, improving the detection of hateful comments on Arabic social media using the Synthetic Minority Over-sampling Technique (SMOTE). Notably, the DziriBert model exhibits remarkable proficiency in detecting hate speech, achieving an accuracy of 82%. Random Forest (RF) and Linear SVC outperform the state of the art approaches, achieving the best result.