Multiview Spatio-Temporal Learning With Dual Dynamic Graph Convolutional Networks for Rumor Detection

Detecting rumors on social networks is increasingly important due to their rapid dissemination and negative societal impact. The structural characteristics of propagation play a crucial role in rumor detection. However, most current graph neural network-based methods focus on spatial structural features, overlooking the temporal structural features or exploring spatio-temporal features from a single perspective, failing to comprehensively and finely learn representations of dynamic events. Therefore, this article proposes a multiview spatio-temporal feature learning method based on dual dynamic graph convolutional networks. First, dynamic graphs of information propagation and user interactions are constructed based on retweet and reply relationships. Second, BERT is utilized to extract semantic features of content, serving as initial node representations for the information propagation graph, while social features of users serve as initial node representations for the user interaction graph. Subsequently, dual graph convolutional networks are employed to learn representations of graph structures at different time steps. Finally, a time fusion unit based on cross-attention is devised to facilitate the learning and fusion of the spatio-temporal features from the two dynamic graphs. Experimental results on two real-world social network rumor datasets, PHEME and Weibo, demonstrate that our method outperforms all compared baseline methods and enables early detection of rumors.