Correcting the Bias: Mitigating Multimodal Inconsistency Contrastive Learning for Multimodal Fake News Detection

Multimodal fake news detection has become a topical research of fake news detection. Existing models have made great efforts in capturing and fusing multimodal semantics of news for classification. However, they overlooked mitigating inconsistency between different modalities, which may result in learning biased statistical information. Therefore, we propose a mitigating multimodal inconsistency contrastive learning framework (MMICF), which mitigates inconsistency in multi-modal relations for fake news detection. Inspired by various forms of artificial fake news, we summarize two patterns of multimodal inconsistency: local and global inconsistency. To mitigate local inconsistency in multimodal relations, we use a causal-relation reasoning module by causally removing the direct effects of the textual and visual entities. Considering the influence of global inconsistency in multimodal semantics, our contrastive learning framework mitigates the semantic deviation of contrastive text-image objectives, which are constrained into a unified semantic space by a modal unified module. Thus, our MMICF can jointly mitigate local and global inconsistency for further maximally exploiting multimodal consistent semantics for fake news detection. The extensive experimental results show that the MMICF can improve the performance of multimodal fake news detection and provide a novel paradigm for mitigating multimodal inconsistency contrastive learning.