Jia Ding
ABSTRACT
Automatic fake news detection is a challenging problem which needs a number of verifiable facts support back. Wang et al. [16] introduced LIAR, a validated dataset, and presented a six classes classification task with several popular machine learning methods to detect fake news in linguistic level. However, empirical results have shown that the CNN and RNN based model can not perform very well especially when integrating all features with claim. In this paper, we are the first to present a method to build up a BERT-based [4] mental model to capture the mental feature in fake news detection. In details, we present a method to construct a patterned text in linguistic level to integrate the claim and features appropriately. Then we fine-tune the BERT model with all features integrated text. Empirical results show that our method provides significant improvement over the state-of-art model based on the LIAR dataset we have known by 16.71% in accuracy.
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Index Terms
- BERT-Based Mental Model, a Better Fake News Detector
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