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Automatically verifying rumorous information has become an important andchallenging task in natural language processing and social media analytics.Previous studies reveal that people's stances towards rumorous messages canprovide indicative clues for identifying the veracity of rumors, and thusdetermining the stances of public reactions is a crucial preceding step forrumor veracity prediction. In this paper, we propose a hierarchical multi-tasklearning framework for jointly predicting rumor stance and veracity on Twitter,which consists of two components. The bottom component of our frameworkclassifies the stances of tweets in a conversation discussing a rumor viamodeling the structural property based on a novel graph convolutional network.The top component predicts the rumor veracity by exploiting the temporaldynamics of stance evolution. Experimental results on two benchmark datasetsshow that our method outperforms previous methods in both rumor stanceclassification and veracity prediction.

Modeling Conversation Structure and Temporal Dynamics for Jointly Predicting Rumor Stance and Veracity