Abstract The proliferation of misinformation and fake news on digital platforms has emerged as a critical challenge, undermining public trust and exacerbating societal polarization. Traditional verification methods are inadequate for rapid information dissemination on social media, necessitating advanced automated solutions. This paper proposes a novel fake news detection model, BCGH-Net, which integrates BERT embeddings with a multi-level feature extraction framework. The model employs Convolutional Neural Networks (CNN) for word-level feature extraction and Bidirectional Gated Recurrent Units (Bi-GRU) for sequential sentence-level dependencies, fused via a Hierarchical Attention Network (HAN) to prioritize salient textual elements. Evaluated on four benchmark datasets (FakeNewsNet, ISOT, CoAID, and ReCOVery), BCGH-Net achieves superior performance across four benchmark datasets, demonstrating its robustness and generalizability, with accuracies of 95.8%, 99.3%, 98.7%, and 92.4%, respectively. The results demonstrate its robustness across diverse domains, including political and health-related misinformation. Key contributions include a two-stage feature extraction approach, hierarchical attention fusion, and superior performance metrics (precision, recall, F1-score) compared to existing models. Despite its computational complexity, BCGH-Net offers a scalable solution for real-world deployment, addressing the urgent need for reliable fake news detection systems.
Al-Kaabi et al. (Thu,) studied this question.