Jintao Su
ABSTRACT
With the development of social media, false news has become a serious problem facing today’s society. In view of the fact that most of the existing research on fake news detection technology relies on external knowledge sources, and there are still limitations in processing text details, this paper proposes a fake news detection algorithm (BCCU) that integrates multi-level features. First, the pre-trained model BERT is used to obtain global text features, and the BERT coding layer is used to access the capsule network to extract local text features. Then a collaborative attention mechanism is introduced to process the redundant parts of the two text features and fuse the two. Finally User attribute data is added to assist in false news detection to improve the model’s ability to predict future news. Experimental results show that when this method only relies on news text, the accuracy on Weibo, Twitter15 and Twitter16 datasets are 1.1%, 13.3% and 11.4% higher than the classic model (RvNN) respectively.
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Index Terms
- Fake news detection algorithm based on incorporating multi-level features
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