We apply causal mediation analysis to explain the decision-making process of neural models for rumour detection on Twitter. Interventions at the input and network level reveal the causal impacts of tweets and words in the model output. We find that our approach CMA-R -- Causal Mediation Analysis for Rumour detection -- identifies salient tweets that explain model predictions and show strong agreement with human judgements for critical tweets determining the truthfulness of stories. CMA-R can further highlight causally impactful words in the salient tweets, providing another layer of interpretability and transparency into these blackbox rumour detection systems. Code is available at: https://github.com/ltian678/cma-r.

翻译：摘要：我们应用因果中介分析来解释用于Twitter谣言检测的神经模型的决策过程。在输入层和网络层面的干预揭示了推文和词汇在模型输出中的因果影响。我们发现，我们提出的方法CMA-R（用于谣言检测的因果中介分析）能够识别出解释模型预测的关键推文，并且这些推文与人类对决定故事真实性的关键推文的判断高度一致。CMA-R还能进一步突出关键推文中具有因果影响的词汇，为这些黑箱谣言检测系统提供了另一层可解释性和透明性。代码可在以下网址获取：https://github.com/ltian678/cma-r。