Arabic hate speech detection using deep learning: a state-of-the-art survey of advances, challenges, and future directions (2020–2024)

The proliferation of social media has intensified concerns about the societal and psychological impacts of hate speech, particularly in Arabic-speaking communities, where dialectal diversity, morphological complexity, and sociopolitical factors complicate detection. Despite platform efforts, the automated detection of Arabic hate speech remains challenging owing to limited annotated datasets and linguistic nuances. This survey reviews the advances (2020–2024) in deep learning approaches, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), transformer-based models ( e.g ., bidirectional encoder representations from transformers (BERT) and AraBERT), and hybrid architectures for Arabic hate speech detection. It further examines the dataset constraints involving dialectal variation, annotation inconsistencies, and scarcity. The analysis identified critical research gaps and proposed future directions: expanding multilingual datasets, enhancing contextual modeling, and developing ethically grounded frameworks. This review consolidates state-of-the-art methodologies to guide effective countermeasures against Arabic online hate speech.