  Automatically verifying rumorous information has become an important and
challenging task in natural language processing and social media analytics.
Previous studies reveal that people's stances towards rumorous messages can
provide indicative clues for identifying the veracity of rumors, and thus
determining the stances of public reactions is a crucial preceding step for
rumor veracity prediction. In this paper, we propose a hierarchical multi-task
learning framework for jointly predicting rumor stance and veracity on Twitter,
which consists of two components. The bottom component of our framework
classifies the stances of tweets in a conversation discussing a rumor via
modeling the structural property based on a novel graph convolutional network.
The top component predicts the rumor veracity by exploiting the temporal
dynamics of stance evolution. Experimental results on two benchmark datasets
show that our method outperforms previous methods in both rumor stance
classification and veracity prediction.
