通过时间加权传播树优化谣言检测，提升预测准确性
Rumor Detection on Social Media with Temporal Propagation Structure Optimization
- 构建带时间权重的传播树，捕捉谣言扩散的时间动态
- 利用结构熵将传播树转为编码树，有效降噪并保留核心结构
- 基于递归神经网络学习编码树，适用于社交平台谣言识别
社交媒体谣言检测传统方法多聚焦文本内容分析，难以捕捉在线交互的复杂性。近期研究转向图神经网络建模谣言传播中的层级对话结构，但常忽略传播的时间特性，且可能受传播结构中噪声干扰。本文提出一种新方法：通过构建带权传播树，边权重表示相邻帖子间的时间间隔；基于结构熵理论，将该树转化为编码树，旨在保留谣言传播的核心结构同时降低噪声；最后引入递归神经网络从编码树中学习以进行谣言真伪预测。在两个常用数据集上的实验结果表明，该方法具有显著优势。
原文摘要 · Abstract (English)
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.
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