Early Rumor Detection Based on Bert-GNNs Heterogeneous Graph Attention Network

The widespread spread of network rumors has caused great harm to the society, so the task of early rumor detection has become an important research focus. The majority of existing methods for rumor detection focus on mining effective features from text contents, user profiles, and patterns of propagation, but these methods do not take full advantage of both global semantic relationship of text and local context semantic relationship. In order to overcome the above limitations and make full use of the text global-local context semantic relationship, text semantic content feature and the structural feature of tweet propagation in the rumor data, this paper puts forward a kind of early rumors detection algorithm based on Bert-GNNs heterogeneous graph attention network (BGHGAN). This method constructs a tweet-word-user heterogeneous graph according to historical rumor sets and user characteristics, using the method of combining Bert and GCN (Graph Convolutional Network) for feature learning to mine the relationship between the text semantic features and the text of rumors. And by decomposing the heterogeneous graph into tweet-word subgraph and tweet-user subgraph, the method uses GAT (Graph Attention network) to perform feature learning respectively, so as to make full use of the global-local context semantic relationship of the text and the global structure relationship of the propagation graph to strengthen the feature expression. Finally, the learning integration of different modules is carried out through the subgraph-level attention mechanism for final rumor detection. The proposed algorithm is experimented on real Twitter15 and Twitter16 data, and verifies that the detection accuracy of the algorithm is 91.4% and 91.9%, respectively, which is 1% and 1.4% higher than the existing best model, and also has the ability to detect rumors in the early stage. And this paper discusses the importance of different features to rumor detection and the importance of the quality of heterogeneous graph construction. © 2024 Chinese Institute of Electronics. All rights reserved.