CMA-R: Causal Mediation Analysis for Explaining Rumour Detection (2024.findings-eacl)
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| Challenge: | Existing studies on explainable fake news or rumour detection by and large use attention weights as explanation, but the use of attention weighted explanations is problematic. |
| Approach: | They propose a causal mediation analysis approach to explain the decision-making process of neural models for rumour detection on Twitter by identifying salient tweets that explain model predictions and highlighting causally impactful words in the tweets. |
| Outcome: | The proposed approach shows strong agreement with human judgements for critical tweets determining the truthfulness of stories. |
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