Rumor detection method integrating rich semantics and global propagation

Abstract:

Existing rumor detection methods mainly rely on text semantic features and network propagation features, but the source tweets dominated by short texts can easily lead to insufficient semantic features, and the propagation tree used to extract propagation features can generate a large amount of data. To solve these problems, we proposed a rumor detection method, namely multi-view graph neural network, which integrated rich semantics and global propagation. This model used source texts to get structural semantic relationships, utilized external knowledge to extract potential semantic relationships, and got the global propagation relationship among users by source tweets and their response users. Finally, it automatically learned the feature weights of different views through the attention fusion mechanism, achieving adaptive information fusion and improving the accuracy of rumor detection. Besides, it adopted Word2Vec to supplement the content semantics of source tweets. Experimental results show that using source texts, external knowledge, and response users to construct graphs, respectively, can effectively capture rich semantic information and concise global propagation relationships. The model outperforms a series of baseline models on the public datasets Twitter15 and Twitter16, with the accuracy rates of 90.2% and 90.8%, respectively. The analysis results from the ablation experiment show that the proposed method can comprehensively capture rich semantic features of the source tweets and effectively obtain the global propagation relationship in a concise manner, so as to improve the accuracy of rumor detection.