Detection of Hateful Social Media Content for Arabic Language

• Published in: ACM transactions on Asian and low-resource language information processing 2023-09, Vol.22 (9), p.1-26, Article 228
• Main Authors: Al-Ibrahim, Rogayah M., Ali, Mostafa Z., Najadat, Hassan M.
• Format: Article
• Language: English
• Subjects: Computing methodologies; Machine learning; Machine learning algorithms
• ISSN: 2375-4699; 2375-4702
• DOI: 10.1145/3592792

Social media is a common medium for expression of views, discussion, sharing of content, and promotion of products and ideas. These views are either polite or obscene. The growth of hate speech is one of the negative aspects of the medium and its emergence poses risk factors for society at various levels. Although there are rules and laws for these platforms, they cannot oversee and control all types of content. Thus, there is an urgent need to develop modern algorithms to automatically detect hateful content on social media. Arab society is not isolated from the world, and the usage of social media by its members has highlighted the importance of automated systems that help build an electronic society free of hate and aggression. This article aims to detect hate speech based on Arabic context over the Twitter platform by proposing different novel deep learning architectures in order to provide a thorough analytical study. Also, a comparative study is presented with a different well-known machine learning algorithm, as well as other state-of-the-art algorithms from the literature to be used as a beacon for interested researchers. These models have been applied to the Arabic tweets dataset, which included 15K tweets and 14 features. After training these models, the results obtained for the top two models included an improved bidirectional long short-term memory with an accuracy of 92.20% and a macro F1-score of 92% and a modified convolutional neural network with an accuracy of 92.10% and a macro F1-score of 91%. The results also showed the superiority of the performance of the deep learning models over other models in terms of accuracy.