CMA-R:Causal Mediation Analysis for Explaining Rumour Detection
Lin Tian Xiuzhen Zhang Jey Han Lau
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.