计算机应用研究2024，Vol.41Issue(7)：1992-1998,7.DOI:10.19734/j.issn.1001-3695.2023.11.0565
结合社交网络图的多模态虚假信息检测模型
Multimodal misinformation detection model with social network graph
摘要
Abstract
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 de-tection model combined with the social network graph(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 misinforma-tion 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 misinforma-tion 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.关键词
网络舆情/虚假信息检测/多模态融合/跨模态注意力/社交网络图Key words
online public opinion/misinformation detection/multimodal fusion/multimodal co-attention/social network graph分类
信息技术与安全科学引用本文复制引用
叶舟波,罗舜,于娟..结合社交网络图的多模态虚假信息检测模型[J].计算机应用研究,2024,41(7):1992-1998,7.基金项目
国家自然科学基金资助项目(71771054,72171090) （71771054,72171090）
福建省自然科学基金资助项目(2023J01393) （2023J01393）
