Abstract: To address the issue of feature conflicts caused by differences between features when the existing rumor detection methods deal with multiple features, a hierarchical gated interactive fusion network-based rumor detection method is proposed. First, the first-order gate unit is conducted to obtain the enhanced semantic and sentiment features of original posts and comments. Then, the second-order gate unit is used to perform cross-semantic feature fusion on the enhanced features to solve the problem of introducing noise due to differences between different features during feature fusion. On the public Weibo dataset and the self-built Weibo22 dataset, the detection accuracy of the proposed method is 96.71% and 97.36% , respectively. Compared with the baseline methods with the best detection performance, the detection accuracy of the proposed method is improved by 0.84% and 1.31% , respectively, and the training time is reduced by 53% and 46% , respectively.

Key words: rumor detection , gated network , feature fusion