Abstract: In recent years, traditional graph neural networks have been widely applied to rumor detection tasks, with their core advantage lying in the effective capture of the structural characteristics of rumor propagation. However, most existing models only focus on the explicit interactions between tweets during the propagation process, neglecting the deep interaction between users and tweets, as well as the semantic relationships of tweet content. This limitation restricts the further improvement of rumor detection performance. To address these issues, this paper proposes a dual-hypergraph neural network model, UPST-HGNN, which integrates user, propagation, semantic, and temporal features. Specifically, the model first incorporates user features and high-order propagation features to construct a "user- propagation" hypergraph. At the same time, tweet semantic features are introduced and semantic similarity is calculated to construct a "semantic-temporal" hypergraph. Hypergraph convolutional networks combined with graph attention networks are utilized to extract hypergraph features, and feature representations are dynamically fused based on attention mechanisms. Finally, the fused feature vector is input into a classifier to complete rumor detection. Experimental results show that the UPST-HGNN model achieved accuracy of 86.27% and 94.10% on the publicly available PHEME and WEIBO datasets, respectively, which were 1.67% and 2.8% higher than the accuracy of the selected optimal baseline model. These results confirm that the model can more comprehensively capture rumor-related information, deeply understand the diversity and complexity of the propagation process, which effectively enhances detection performance and provides new insights for rumor detection research.

摘要： 近年来，传统图神经网络已广泛应用于谣言检测任务，其核心优势在于能够有效捕捉谣言的传播结构特征。然而，现有模型大多仅聚焦于传播过程中推文间的显式交互关系，既未能充分挖掘用户与推文的深层交互逻辑，也未有效建立推文内容间的语义关联，这一缺陷直接制约了谣言检测性能的进一步提升。针对上述问题，本文提出一种融合用户、传播、语义及时间特征的双超图神经网络模型——UPST-HGNN。具体而言，模型首先引入用户特征与高阶传播特征，构建“用户—传播”超图，以刻画跨主体的复杂交互关系；同时，引入推文语义特征并计算语义相似性，构建“语义—时序”超图，实现语义关联与时间演化特征的联合建模。在此基础上，采用超图卷积网络结合图注意力网络提取超图特征，基于注意力机制动态融合多维度特征表示，最终将整合后的特征输入分类器，完成谣言检测判断。实验结果表明，在公开的PHEME与WEIBO数据集上，UPST-HGNN模型的准确率分别达到86.27%和94.10%。相较于最优基线模型，其准确率进一步提升1.67%与2.8%。这一结果证实，该模型能够更全面地捕捉谣言相关信息，深刻理解传播过程的多样性与复杂性，从而有效提升检测性能，并为谣言检测研究提供了新的思路。