TEH-GCN: Topic-Event Based Hierarchical Graph Convolutional Networks for Rumor Detection

In the age of social media, the spread of information on the Internet transcends the geographical restrictions, and any netizen on the Internet can express views, which is the main reason that rumors on the Internet can spread rapidly. To regulate and stop the malicious spread of rumors, rumor detection automatically has become a hot field. Few previous studies consider both the temporal text information and the structural information in rumor detection, ignoring the correlation between the events. To address the above issues, this paper proposes a novel hierarchical structure based on event and topic, i.e., TEH-GCN (Topic-Event Hierarchical Graph Convolutional Networks), for rumor detection. TEH-GCN first fully learns the feature representation of rumor events at the event-level by combining the temporal and the structural information characteristics, then it uses graph network to interact the features of the rumor events in topic-level. The experimental results on the three datasets indicate that the proposed TEH-GCN can achieve better performance compared with various benchmarks.