用可解释的因果模型分析社交媒体谣言传播路径
CausalMamba: Interpretable State Space Modeling for Temporal Rumor Causality
- 结合Mamba与图网络，学习推文序列与回复结构的联合表示
- 在Twitter15数据集上达到强基线性能，并支持反事实干预分析
- 能识别关键传播节点，提供可解释的谣言影响机制
社交媒体上的谣言检测因传播动态复杂且现有模型可解释性差而面临挑战。尽管近期神经架构能捕捉内容与结构特征，却难以揭示虚假信息传播的潜在因果机制。本文提出CausalMamba，融合Mamba序列建模、图卷积网络（GCNs）与可微分因果发现（NOTEARS），学习时间推文序列与回复结构的联合表示，同时揭示隐含因果图以识别传播链中的关键节点。在Twitter15数据集上的实验表明，该模型分类性能媲美强基线，并首次实现反事实干预分析。定性结果显示，移除高排名因果节点会显著改变图连通性，为谣言传播机制提供可解释洞察。本框架统一了谣言分类与影响力分析，推动更可解释、可行动的虚假信息检测系统发展。
原文摘要 · Abstract (English)
Rumor detection on social media remains a challenging task due to the complex propagation dynamics and the limited interpretability of existing models. While recent neural architectures capture content and structural features, they often fail to reveal the underlying causal mechanisms of misinformation spread. We propose CausalMamba, a novel framework that integrates Mamba-based sequence modeling, graph convolutional networks (GCNs), and differentiable causal discovery via NOTEARS. CausalMamba learns joint representations of temporal tweet sequences and reply structures, while uncovering latent causal graphs to identify influential nodes within each propagation chain. Experiments on the Twitter15 dataset show that our model achieves competitive classification performance compared to strong baselines, and uniquely enables counterfactual intervention analysis. Qualitative results demonstrate that removing top-ranked causal nodes significantly alters graph connectivity, offering interpretable insights into rumor dynamics. Our framework provides a unified approach for rumor classification and influence analysis, paving the way for more explainable and actionable misinformation detection systems.
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