计算机科学与探索2025，Vol.19Issue(11)：3033-3045,13.DOI:10.3778/j.issn.1673-9418.2411048
基于超图双重注意力机制的多模态谣言检测模型
Multi-modal Rumor Detection Model Based on Dual Attention Mechanism on Hypergraphs
摘要
Abstract
To address the existing graph neural network models'limitations in effectively modeling complex multi-node relationships during rumor propagation and insufficient exploration of multi-modal feature information within nodes for multi-modal fake news detection,this paper proposes a hypergraph-based rumor detection model incorporating multi-modal node embeddings.Firstly,the method fuses multi-modal features,including text,image,and sentiment to obtain more infor-mative initial node embeddings.Then,it feeds the fused node representations into the constructed multi-relational hyper-graph to capture group-level interaction patterns beyond pairwise relations.Finally,it introduces a gated dual-attention mechanism that adaptively assigns weights to key nodes and hyperedges and highlights high-importance factors.The resulting high-level node representations are used as classifier inputs,ultimately improving the identification of news rela-tionships and propagation patterns.Experimental evaluations demonstrate that the proposed method achieves significant performance improvements on three publicly available datasets,obtaining accuracies of 94.46%,97.36%and 93.86%,respectively.Furthermore,the method shows promising effectiveness in early rumor detection tasks,highlighting its advantages in multi-modal information fusion and complex relationship modeling.关键词
图神经网络/谣言检测/多模态融合/注意力机制Key words
graph neural networks/rumor detection/multi-modal fusion/attention mechanism分类
计算机与自动化引用本文复制引用
王安然,袁得嵛,潘语泉,贾源..基于超图双重注意力机制的多模态谣言检测模型[J].计算机科学与探索,2025,19(11):3033-3045,13.基金项目
公安部技术研究计划重点项目(2024JSZ01). This work was supported by the Key Project of Technology Research Program of Ministry of Public Security of China(2024JSZ01). （2024JSZ01）
