• 数据库与数据挖掘 •

### 融合用户历史传播信息的微博谣言检测

1. (上海理工大学光电信息与计算机工程学院,上海200093)
• 出版日期:2022-06-23 发布日期:2022-06-23
• 作者简介:卢悦(1997—),女,山东临沂人,硕士研究生,研究方向:自然语言处理,谣言检测,E-mail: lyy_lkx@163.com; 曹春萍(1968—),女,上海人,副教授,硕士生导师,硕士,CCF会员,研究方向:数据挖掘,个性化服务,E-mail: ccpgcd@163.com。
• 基金资助:
国家自然科学基金资助项目（71901144）

### Microblog Rumor Detection Integrating User’s History and Dissemination Information

1. (School of Optical-Electrical & Computer Engineering, University of Shanghai for Science & Technology, Shanghai 200093, China)
• Online:2022-06-23 Published:2022-06-23

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.