Abstract: With the development of Internet technology, online rumors have gradually spread on social media platforms based on Weibo. Research on the automatic detection of Weibo rumors is of great significance to maintaining social stability. The current mainstream rumor detection methods based on deep learning generally have the problem of not fully considering the semantic information of Weibo texts. At the same time, the rumor detection methods that rely too much on dissemination of information make the detection time lag and cannot meet the actual needs of rumor detection. In response to the above problems, this paper proposes a microblog rumor detection model that integrates user historical interaction information. It does not use the dissemination information of microblogs to be detected, constructs and trains the AbaNet (ALBERT-BiGRU-Attention) deep learning network model, and fully considers the text features and semantic information of Weibo and user history dissemination information text for rumor detection. The experimental results show that the model in this paper has the characteristics of high accuracy and strong stability, and can greatly shorten the time of rumor detection while obtaining high detection accuracy.

Key words: Weibo rumor, rumor detection, deep neural network, pre-training