Abstract: In recent years,the surge in fake news has adversely affected people’s decision-making process.Many existing fake news detection methods emphasize the exploration and utilization of multimodal information,such as text and image.However,how to generate discriminative features for the detection task and effectively aggregate features of different modalities for fake news detection remains an open question.In this paper,we propose a novel fake news detection model,i.e.,cross-modal interaction and feature fusion network(CMIFFN).To generate discriminant features,a supervised contrastive learning-based feature learning module is designed.By performing intra-modality and inter-modality supervised contrastive learning simultaneously,it ensures that the similarity of heterogeneous features is smaller and the similarity of similar features is greater.In addition,in order to mine more useful multi-modal information,this paper designs a multi-stage cross-modal interaction module to learn cross-modal interaction features with graph structure information.The method introduces consistency evaluation-based attention me-chanism to effectively aggregate modality-specific features and cross-modal interaction features by learning multi-modal consistency weight.Experiments on two benchmark datasets Weibo and Twitter show that CMIFFN is significantly superior to the state-of-the-art multimodal fake news detection methods.

Key words: Fake news detection, Supervised contrastive learning, Multi-stage cross-modal interaction, Graph convolutional network