Improving Multimodal Rumor Detection via Dynamic Graph Modeling

Improving Multimodal Rumor Detection via Dynamic Graph Modeling

연구 분야: Infrastructure

학회: International Conference on Pattern Recognition

Due to the proliferation of rumors in social networks, automatic rumor detection has evoked increasing attention in recent years. Despite great progress achieved by exploiting multimodal features, existing works suffer from false discrimination issues due to insufficient multimodal modeling, mainly from two aspects: 1) neglect of the dynamicity of social networks. 2) misaligned multimodal features. To alleviate the issues, we propose DGM, Dynamic Graph Modeling for rumor detection. Firstly, dynamic graph attention is devised to exploit message propagation’s structural and temporal features. Secondly, we propose a modality-shared adapter to learn better multimodal representation. Thirdly, well-aligned visual-textual features are introduced to achieve better multimodality alignment and fusion, together with cross-modal attention and alignment supervision. We conduct extensive experiments on two public datasets, demonstrating the effectiveness and superiority of DGM.