The rapid growth of social media has caused tremendous effects on information propagation, raising extreme challenges in detecting rumors. Existing rumor detection methods typically exploit the reposting propagation of a rumor candidate for detection by regarding all reposts to a rumor candidate as a temporal sequence and learning semantics representations of the repost sequence. However, extracting informative support from the topological structure of propagation and the influence of reposting authors for debunking rumors is crucial, which generally has not been well addressed by existing methods. In this paper, we organize a claim post in circulation as an adhoc event tree, extract event elements, and convert it to bipartite adhoc event trees in terms of both posts and authors, i.e., author tree and post tree. Accordingly, we propose a novel rumor detection model with hierarchical representation on the bipartite adhoc event trees called BAET. Specifically, we introduce word embedding and feature encoder for the author and post tree, respectively, and design a root-aware attention module to perform node representation. Then we adopt the tree-like RNN model to capture the structural correlations and propose a tree-aware attention module to learn tree representation for the author tree and post tree, respectively. Extensive experimental results on two public Twitter datasets demonstrate the effectiveness of BAET in exploring and exploiting the rumor propagation structure and the superior detection performance of BAET over state-of-the-art baseline methods.

翻译：社交媒体的迅猛发展对信息传播产生了深远影响，也为谣言检测带来了严峻挑战。现有谣言检测方法通常将可疑谣言的所有转推视为时间序列，通过学习转推序列的语义表示来检测谣言。然而，从传播拓扑结构及转推作者的影响力中提取有用信息以澄清谣言至关重要，但现有方法对此通常关注不足。本文将传播中的主张帖子组织为即席事件树，提取事件元素，并将其转换为基于帖子和作者的二分即席事件树（即作者树和帖子树）。据此，我们提出一种新颖的基于二分即席事件树分层表示的谣言检测模型BAET。具体而言，我们分别为作者树和帖子树引入词嵌入与特征编码器，并设计根感知注意力模块进行节点表示；随后采用树状RNN模型捕获结构关联性，并提出树感知注意力模块分别学习作者树与帖子树的层次表示。在两个公开Twitter数据集上的大量实验表明，BAET在探索和利用谣言传播结构方面具有有效性，且其检测性能显著优于当前最先进的基线方法。