Leverage Causal Graphs and Rumor-Refuting Texts for Interpretable Rumor Analysis

Previous rumors detection study mostly ignores causal features in rumor texts and the interpretability of rumor classification results. Based on the phenomenon that rumors can cause changes or even loss of the original causal relationship in the truth, we can leverage causal features to help classify rumors. Meanwhile, rumor-refuting text is of great significance in curbing the spread of rumors and can interpret the results of rumor detection. To address these challenges, we propose a rumor analysis model integrating rumor detection and rumor refuting. It builds the causal graph of rumor texts and uses it to improve the performance of rumor detection. In particular, we embed the rumor-refuting text generation module in the model to realize the integration of rumor detection and rum -cation results' analytical ability. We conducted experiments on two benchmark datasets and performed better than the state-of-the-art methods.