BCMF: A bidirectional cross-modal fusion model for fake news detection |
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| Institution: | 1. School of Information and Safety Engineering, Zhongnan University of Economics and Law, Wuhan 430073, China;2. School of Information Management, Wuhan University, Wuhan 430072, China;1. West China Biomedical Big Data Center, West China Hospital, Sichuan University, No.37 Guoxue Alley, Chengdu 610041, China;2. Department of Radiology, West China Hospital, Sichuan University, No.37 Guoxue Alley, Chengdu 610041, China;3. West China Periodicals, West China Hospital, Sichuan University, No.37 Guoxue Alley, Chengdu 610041, China;4. Department of Bile Duct Surgery, West China Hospital, Sichuan University, No.37 Guoxue Alley, Chengdu 610041, China;1. School of Information Science and Engineering, Yanshan University, Qinhuangdao, Hebei, China;2. School of Computer Science and Technology, Beijing Institute of Technology, Beijing, China;1. School of Information Management, Wuhan University, 299 Bayi Road, Wuhan, Hubei, China;2. Center for Studies of Information Resources, Wuhan University, 299 Bayi Road, Wuhan, Hubei, China |
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| Abstract: | In recent years, fake news detection has been a significant task attracting much attention. However, most current approaches utilize the features from a single modality, such as text or image, while the comprehensive fusion between features of different modalities has been ignored. To deal with the above problem, we propose a novel model named Bidirectional Cross-Modal Fusion (BCMF), which comprehensively integrates the textual and visual representations in a bidirectional manner. Specifically, the proposed model is decomposed into four submodules, i.e., the input embedding, the image2text fusion, the text2image fusion, and the prediction module. We conduct intensive experiments on four real-world datasets, i.e., Weibo, Twitter, Politi, and Gossip. The results show 2.2, 2.5, 4.9, and 3.1 percentage points of improvements in classification accuracy compared to the state-of-the-art methods on Weibo, Twitter, Politi, and Gossip, respectively. The experimental results suggest that the proposed model could better capture integrated information of different modalities and has high generalizability among different datasets. Further experiments suggest that the bidirectional fusions, the number of multi-attention heads, and the aggregating function could impact the performance of the cross-modal fake news detection. The research sheds light on the role of bidirectional cross-modal fusion in leveraging multi-modal information to improve the effect of fake news detection. |
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