Fake News Detection by Using Common Latent Semantics Matching Method

As news has become an important way to obtain in-formation, the spread of fake news has caused serious social problems, such as misleading readers and damaging the authority of the government. Therefore, fake news detection has become an important field in social network research. One challenge of fake news detection is how to explore the common latent semantics, which are universally implied in fake news. However, the existing methods are not enough for mining this kind of semantic information. Therefore, we proposed a fake news detection framework named Common Latent Semantics Matching Model (CLSMM), which improves the performance of fake news detection by utilizing common latent semantics in fake news. First, we use BERT model to extract common latent semantics of fake news and use summary generation model to extract distinct latent semantics among each piece of news. Second, we rank the semantic credibility score according to the matching degree of the two kinds of latent semantics mentioned above. Finally, these semantic credibility scores are injected into a fake news classifier to improve the detection performance. Experiments are based on two large scale real-world social media datasets, namely Liar and BuzzFeed. The experimental results show that our model can outperform the accuracy of the state-of-the-art methods by 2.7% and 17.26% on Liar and BuzzFeed, respectively.