Fake news detection method based on multi-feature fusion of entity and structured text

Internet technology has facilitated the development of social media, which has become the main way for people to get news. The low cost of information generation has allowed unconfirmed fake news to be noticed and reposted, which can easily cause social and economic harm. Much of the existing research on fake news detection has focused solely on the accuracy of detection, but has neglected the time efficiency of detection. We propose a fake news detection method called MFEAST, which simplifies article content and speeds up model training by extracting key information from articles. Compared with existing real fake news detection methods, our method substantially reduces the training time and achieves high detection accuracy.