Multi-Modal Adversarial Adaptive Network for Misinformation Detection on Social Media

Lei Zhang, Peng Zhang, Xi Zhu, Luchen Liu, Hongbo Xu

Abstract: Most existing methods for misinformation detection are mainly based on multi-modal contents, while extract multi-modal features with simple encoders and coarse interactions. What's more, they ignore the complex local structure underlying the event distributions when faced with emerging events. To tackle these issues, we propose a Multi-modal Adversarial Adaptive Network (MAAN) for misinformation detection, extracting fine-grained event-invariant cross-modal features. Specifically, MAAN consists of two components: 1) a multi-modal feature extractor models dense inter-modal interactions, removes noise information, and captures discriminative multi-modal information; 2) an adaptive misinformation detector adaptively evaluates the global and local event distribution discrepancies, and dynamically weights them when disentangling event-specific features from multi-modal representations. Extensive experiments demonstrate that MAAN can outperforms the state-of-the-art approaches on two public datasets.