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    基于事件驱动的超图卷积网络的谣言检测方法

    Event-Driven Hypergraph Convolutional Network Based Rumor Detection Method

    • 摘要: 依靠社交平台谣言传播链检测谣言是社交网络分析研究中的一个重要课题. 但以往的研究大多将这个任务过度简化为依靠评论的传播链检测谣言,忽略了对现实世界新闻帖的复杂用户和事件交互的关注,难以捕捉到这些信息提供的潜在检测线索. 针对该挑战,提出了一个事件驱动的超图卷积网络(event-driven hypergraph convolutional network,EHGCN),首次尝试将新闻、用户和事件建模在一个统一的超图卷积网络之中,以提升谣言检测性能. 具体而言,基于用户的中心网络构建同质用户圈,以增强用户感知的谣言检测. 此外,EHGCN联合利用事件内的主事件和子事件关联以及事件间的不一致性关联来进行谣言检测. 在3个真实世界的数据集上进行的实验结果验证了EHGCN相较于现有方法在谣言检测方面的优势. 研究也证实EHGCN可以通过获取丰富的用户社交圈和事件信息,在谣言传播早期及时发现谣言.

       

      Abstract: Rumor detection with propagation chain remains an important topic in the research of social network analysis. Previous studies mostly oversimplify this task as detecting rumor in propagation chains of comments. However, real-world news posts usually share complex user and event interactions, which can provide potential detection clues. Therefore, we propose an event-driven HyperGraph convolutional network (EHGCN), which makes the first attempt to model news, user and event correlations into a unified hypergraph convolutional network for rumor detection. Specifically, it exploits egocentric network of users to construct homogeneous users circles for enhancing user-aware rumor verification. Moreover, EHGCN jointly leverages the main event and sub-event patterns at the intra-event level for rumor detection. The extensive experimental results show that EHGCN could improve the performance of rumor detection. Studies also confirm that EHGCN can detect rumors at an early stage by acquiring rich user circle and event information.

       

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