Traditional methods for detecting rumors on social media primarily focus on analyzing textual content, often struggling to capture the complexity of online interactions. Recent research has shifted towards leveraging graph neural networks to model the hierarchical conversation structure that emerges during rumor propagation. However, these methods tend to overlook the temporal aspect of rumor propagation and may disregard potential noise within the propagation structure. In this paper, we propose a novel approach that incorporates temporal information by constructing a weighted propagation tree, where the weight of each edge represents the time interval between connected posts. Drawing upon the theory of structural entropy, we transform this tree into a coding tree. This transformation aims to preserve the essential structure of rumor propagation while reducing noise. Finally, we introduce a recursive neural network to learn from the coding tree for rumor veracity prediction. Experimental results on two common datasets demonstrate the superiority of our approach.

翻译：传统的社交媒体谣言检测方法主要侧重于分析文本内容，往往难以捕捉在线交互的复杂性。近期研究已转向利用图神经网络来建模谣言传播过程中出现的层次化对话结构。然而，这些方法容易忽视谣言传播的时间维度，并可能忽略传播结构中的潜在噪声。本文提出一种创新方法，通过构建加权传播树来整合时序信息，其中每条边的权重代表相连帖子间的时间间隔。基于结构熵理论，我们将该树转化为编码树。这一转换旨在保留谣言传播的核心结构同时降低噪声干扰。最后，我们引入递归神经网络从编码树中学习以实现谣言真实性预测。在两个常用数据集上的实验结果验证了本方法的优越性。