Self-Attention Enhanced Dual BiGRU for Arabic Fake News Detection

The rapid proliferation of social media platforms has greatly amplified the dissemination of fake news, representing significant obstacles to public trust and evidence-based decision-making, particularly for the Arabic-speaking population. Meeting the challenge of Arabic fake news detection is a problem compounded by the complex morphological nature of the language, as well as limited resources. This study presents a hybrid deep learning framework that integrates two Bidirectional Gated Recurrent Units (BiGRUs) along with an attention mechanism for efficiently detecting misinformation in Arabic news. The method leverages FastText word embeddings for disambiguating the intricate semantics of the Arabic language. The model is meticulously crafted to account for the morphological variability and contextual sensitivities of Arabic, with the extensive Arabic fake news dataset (AFND) being used for training and testing. Experimental findings show that our model performs better with an accuracy of 91.92%, outperforming current state-of-the-art approaches. The findings highlight the effectiveness of integrating advanced neural architectures and tailored preprocessing for Arabic, paving the way for more robust and interpretable fake news detection systems in low-resource languages.