The growth in social media has exacerbated the threat of fake news to individuals and communities. This draws increasing attention to developing efficient and timely rumor detection methods. The prevailing approaches resort to graph neural networks (GNNs) to exploit the post-propagation patterns of the rumor-spreading process. However, these methods lack inherent interpretation of rumor detection due to the black-box nature of GNNs. Moreover, these methods suffer from less robust results as they employ all the propagation patterns for rumor detection. In this paper, we address the above issues with the proposed Diverse Counterfactual Evidence framework for Rumor Detection (DCE-RD). Our intuition is to exploit the diverse counterfactual evidence of an event graph to serve as multi-view interpretations, which are further aggregated for robust rumor detection results. Specifically, our method first designs a subgraph generation strategy to efficiently generate different subgraphs of the event graph. We constrain the removal of these subgraphs to cause the change in rumor detection results. Thus, these subgraphs naturally serve as counterfactual evidence for rumor detection. To achieve multi-view interpretation, we design a diversity loss inspired by Determinantal Point Processes (DPP) to encourage diversity among the counterfactual evidence. A GNN-based rumor detection model further aggregates the diverse counterfactual evidence discovered by the proposed DCE-RD to achieve interpretable and robust rumor detection results. Extensive experiments on two real-world datasets show the superior performance of our method. Our code is available at https://github.com/Vicinity111/DCE-RD.

翻译：社交媒体上的信息增长加剧了虚假新闻对个人和社区的威胁，这促使人们日益关注开发高效及时的谣言检测方法。当前主流方法借助图神经网络（GNN）利用谣言传播过程中的帖子传播模式。然而，由于GNN的"黑箱"特性，这些方法缺乏对谣言检测的内在解释性。此外，这些方法将所有传播模式用于谣言检测，导致结果鲁棒性不足。本文提出面向谣言检测的多样化反事实证据框架（DCE-RD）来解决上述问题。我们的核心思路是利用事件图的多视角反事实证据作为多维度解释，通过聚合这些证据获得鲁棒的谣言检测结果。具体而言，该方法首先设计子图生成策略，高效生成事件图的不同子图。我们约束这些子图的移除会导致谣言检测结果发生变化，因此这些子图天然成为谣言检测的反事实证据。为实现多视角解释，受行列式点过程（DPP）启发，我们设计了多样性损失函数来促进反事实证据的多样化。基于GNN的谣言检测模型进一步聚合DCE-RD发现的多样化反事实证据，从而实现可解释且鲁棒的谣言检测。在两个真实数据集上的大量实验表明，我们方法具有优越性能。代码已开源在https://github.com/Vicinity111/DCE-RD。