Advancements in Transformer-Based Models for Enhanced Hate Speech Detection in Arabic: Addressing Dialectal Variations and Cross-Platform Challenges

The rise of social media platforms has greatly amplified the spread of hate speech, which poses serious societal risks. The automated detection of hate speech on social media, especially in low-resource Arabic language, presents unique challenges owing to linguistic diversity, dialectal differences, and regional nuances. However, most current research efforts primarily focus on a single social media platform, which hinders the ability to address dialect differences, as residents of Arab regions often favor one platform over another. This study provides an in-depth analysis of Arabic hate speech detection using advanced transformer-based models across three datasets collected from diverse social media platforms. Our models include multilingual, monolingual models pretrained in Arabic, and models that employ transfer learning from rich-resource English. To provide a thorough evaluation, we also compared the performance of our transformer-based models with two baseline models: LR and NBSVM, highlighting their relative effectiveness in detecting hate speech across multi-dialect and multi-platforms. Our analysis includes the effects of oversampling, data augmentation, and model interpretability using the LIME method. The monolingual transformer-based models, in particular, demonstrated significant performance improvements, setting new benchmarks for F1-scores and surpassing traditional models, our best classifier achieved an F1-score of 98.82%. Additionally, we conducted thorough cross-validation across datasets to evaluate the models’ generalization capabilities. Our research significantly advances Arabic hate speech detection by tackling these complexities and laying a solid foundation for future research. However, challenges remain in detecting subtle forms of hate speech, such as implicit hate speech and fine-grained distinctions between offensive content.