Rumor Detection on Potential Hot Topics with Bi-directional Graph Attention Network

Most of the existing methods for detecting rumors on social media networks typically focus on individual posts as the target of detection,which leads to a cold start problem due to insufficient data,adversely affecting the detection performance.Moreover,these methods do not filter out the vast amount of irrelevant information in social media networks,resulting in longer detection latency and poorer performance.Additionally,current methods tend to emphasize static features during the spread of rumors when analyzing the characteristics of rumor propagation,making it difficult to fully leverage the dynamic relationships between nodes to model the complex propagation process.To address these issues,a rumor detection method based on potential hot topics and graph attention neural networks is proposed.The method employs a neural topic model and a potential hot topic discover model for topic-level rumor detection to overcome the cold start problem.Furthermore,a detection model named TPC-Bi-GAT is designed to analyze the dynamic features of rumor topic propagation for authenticity detection.Experiments on 3 public datasets show that the proposed method achieves a significant improvement of 3%~5% in accuracy compared with the existing methods,which verifies the effectiveness of the proposed method.