Rumor Detection with Bidirectional Graph Attention Networks

In order to extract the relevant features of rumors effectively, this paper proposes a novel rumor detection model with bidirectional graph attention network on the basis of constructing a directed graph, named P-BiGAT. Firstly, this model builds the propagation tree and diffusion tree through the tweet comment and reposting relationship. Secondly, the improved graph attention network (GAT) is used to extract the propagation feature and the diffusion feature through two different directions, and the multihead attention mechanism is used to extract the semantic information of the source tweet. Finally, the propagation feature, diffusion feature, and semantic information representation of the source tweet are connected together through a fully connected layer, and the mapping function is used to determine the authenticity of the information. In addition, this paper also proposes a new node update method and applies it to the model in order to select neighbor node information effectively. Specifically, it can select the neighbor information node with larger weight to update the node according to the weight of the neighbor node. The results of the experiment show that the model is better than the baseline method of comparison in accuracy, precision, recall, and F1 measure on the public datasets.