Detecting Fake News on Social Media by CSIBERT

Social media has become a significant news source as the modern world develops. Compared with traditional news media such as newspapers and television, people can consume and share news much faster on social media platforms such as Twitter, Facebook, and Weibo. These platforms are not regulated, which leads to massive amounts of fake news produced online and causes severe negative impacts on politics, economics, and social well-being. Thus, detecting fake news on social media is extremely important but technically challenging. This paper proposes a hybrid fake news detection model called CSIBERT, extracting text features of news events utilizing a Bidirectional Encoder Representations from Transformers (BERT) pre-trained model and introducing other social context features via the Capture, Score, and Integrate (CSI) framework. Our proposed model outperforms existing models with an accuracy of 97.1%. In addition, the CSIBERT model receives decent performance even with a small number of labeled samples on the Weibo fake news detection tasks, demonstrating its ability to solve the label shortage problem in fake news detection challenges.