User Stance Aware Network for Rumor Detection Using Semantic Relation Inference and Temporal Graph Convolution

The massive propagation of rumor has impaired the credibility of online social networks while effective rumor detection remains a difficulty. Recent studies leverage stance inference to explore the semantic evidence in comments to improve detection performance. However, existing models only consider stance-relevant semantic features and ignore stance distribution and evolution, thus leaving room for improvement. Moreover, we argue that stance inference without considering the context in threads may lead to incorrect semantic features being accumulated and carried through to rumor detection. In this paper, we propose a user stance aware attention network (USAT), which learns the temporal features in semantic content, individual stance and collective stance for rumor detection. Specifically, a high-order graph convolutional operator is designed to aggregate the preceding posts of each post, ensuring a complete semantic context for stance inference. Two temporal graph convolutional networks work in parallel to model the evolution of stance distribution and semantic content respectively and share stance-based attention for de-nosing content aggregation. Extensive experiments demonstrate that our model outperforms the state-of-the-art baselines.