Bidirectional Feature Fusion and Adaptive Decision Network for Multimodal Fake News Detection

The proliferation of multimedia content on social media platforms has rendered the detection of fake news an increasingly critical challenge. Despite the progress made in multimodal fake news detection methods, significant challenges remain: information loss during the cross-modal feature fusion process and restricted analysis due to single-perspective decision mechanisms. We propose the Bidirectional Feature Fusion and Adaptive Decision Network (BFF-ADN), which introduces two key innovations: a bidirectional token-level feature fusion mechanism that captures key correlations between visual and textual modalities while reducing information loss, and an Adaptive Multi-perspective Decision Integration Network (AMDI-Net) that integrates multiple specialized detectors for comprehensive analysis. Experiments on three real-world datasets (Weibo, GossipCop, and PolitiFact) demonstrate that BFF-ADN achieves state-of-the-art performance. Ablation studies and visualizations confirm the effectiveness of our proposed components, particularly highlighting the advantages of the bidirectional feature fusion mechanism and Adaptive Multi-perspective Decision Integration Network.