Summary of Cma-r:causal Mediation Analysis For Explaining Rumour Detection, by Lin Tian et al.
CMA-R:Causal Mediation Analysis for Explaining Rumour Detection
by Lin Tian, Xiuzhen Zhang, Jey Han Lau
First submitted to arxiv on: 13 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper explores the decision-making process of neural models for detecting rumors on Twitter using causal mediation analysis. The approach, called CMA-R, reveals the causal impacts of tweets and words in the model output by intervening at the input and network levels. The results show that CMA-R identifies salient tweets that explain model predictions with strong agreement with human judgments regarding critical tweets determining the truthfulness of stories. Additionally, CMA-R highlights causally impactful words in the salient tweets, providing an extra layer of interpretability and transparency into these complex rumor detection systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper uses a special way to understand how artificial intelligence models decide what’s true or false on Twitter. It helps figure out why AI makes certain decisions by looking at what people are saying and what words are important. The approach, called CMA-R, works well with human judges who agree that it’s correct when identifying which tweets say something is true or not. |
