Computer Engineering and Applications ›› 2021, Vol. 57 ›› Issue (24): 192-197.DOI: 10.3778/j.issn.1002-8331.2007-0152

• Pattern Recognition and Artificial Intelligence • Previous Articles     Next Articles

Microblog Rumor Detection Method Based on Multi-task Learning

SHEN Ruilin, PAN Weimin, PENG Cheng, YIN Pengbo   

  1. School of Computer Science and Technology, Xinjiang Normal University, Urumqi 830054, China
  • Online:2021-12-15 Published:2021-12-13



  1. 新疆师范大学 计算机科学技术学院,乌鲁木齐 830054


The widespread dissemination of Weibo rumors have caused an increasingly severe negative impact on today’s society. The method based on deep neural network has the problem of lack of a large amount of labeled data. The research has found that rumors are often accompanied by negative emotions, while non-rumors are accompanied by positive emotions. Taking into account the opposite emotional tendencies between rumors and non-rumors, a method is proposed to highly correlate rumors detection and sentiment analysis. The multi-task learning method that combines the tasks of BERT and BiGRU, in order to mine as many associations between different tasks as possible, and comprehensively analyze the characteristics of the rumor detection task, a multi-task learning framework(BERT-BiGRU-MTL, BBiGM). The weight sharing method is used to jointly train the two tasks, and at the same time, the common features between the tasks and the specific features for the rumor detection task are extracted, and the sentiment analysis task is used to assist the rumor detection. The research results show that this method is better than the single-task learning method in terms of accuracy, precision and F1 value evaluation index.

Key words: multi-task learning, rumor detection, emotion analysis, Weibo



关键词: 多任务学习, 谣言检测, 情感分析, 微博