To address the issues of existing misinformation detection approaches, which primarily focus on single-modal data analysis and ignore the correlation between information during detection, this paper proposed a multimodal misinformation detection model combined with the social network graph, the MMD-SNG model. This model used the pre-trained Transformer model and the image caption model to extract the semantics of each modality from multiple perspectives. It incorporated the features of propagated information into the text and image data by fusing the social network graph of the information dissemination process. Finally, this model used a multimodal co-attention mechanism to allocate the weights of each modality for misinformation detection. This paper conducted comparative experiments on two real datasets including Twitter and Weibo, the proposed MMD-SNG model achieved a consistent detection accuracy of approximately 88%, which was higher than existing misinformation detection approaches such as EANN and PTCA. The experimental results demonstrate that the proposed model can fuse multimodal information effectively to improve the accuracy of misinformation detection.

[2024-01-31] Accepted Paper /doc/2023.11.0565/v1