KMMN: Knowledge Enhanced Multimodal Multi-grained Network for Fake News Detection

The development of the social media has created an environment for the rapid spread of fake news. The existing automated detection methods may have the following shortcomings: (1) Content-based methods neglect the rich background information related to news; (2) Unable to effectively exploit multimodal information at both fine-grained and coarse-grained levels; (3) Unable to effectively handle ambiguity problem (information from different modalities may contradict each other). To overcome these challenges, we present a Knowledge enhanced Multimodal Multi-grained Network (KMMN) for fake news detection. We obtain background knowledge contained in news based on entities to enhance cross-modal interaction and provide external information. The cross-modal feature fusion process is separated at different granularities (with fine-grained and coarse-grained branches). We design an improved Mixture-of-Experts (iMoE) network for feature fusion and reweight the cross-modal features to alleviate ambiguity problem. Experimental results demonstrate that the proposed framework outperforms state-of-the-art methods on three public datasets.