Abstract: At present, the rumor detection method for microblog field is mainly based on the microblog text itself, supplemented by information such as user comment characteristics and propagation characteristics. However, the current methods ignore the quality of user comments that may directly affect the performance of rumor detection and introduce useless or even negative features, exerting an impact on the performance of detection. In response to this problem, based on the relevance of user comments and rumor detection, a rumor detection algorithm that considers the effectiveness of comments is proposed. It considers the effectiveness of microblog comments while determining rumors, and rumor detection is implemented based on the the multi-task joint learning method. Firstly, rumor detection is taken as the main task, and user comment correlation detection is taken as the auxiliary task. Secondly, the gating mechanism and the attention mechanism are used to filter and select effective user comment features. Finally, experiments on the self-constructed dataset with 30,000 epidemic microblog rumors show that the screening of user comments can not only improve the performance of rumor detection, but also realize the judgment of the quality of user comments.

Key words: rumor detection, joint learning, user comment, comment validity