A data-driven model for social media fake news detection

Abstract

The rapid development of social media leads to the spread of a large amount of false news, which not only affects people’s daily life but also harms the credibility of social media platforms. Therefore, detecting Chinese fake news is a challenging and meaningful task. However, existing fake news datasets from Chinese social media platforms have a relatively small amount of data and data collection in this field is relatively old, thus being unable to meet the requirements of further research. In consideration of this background, we release a new Chinese Weibo Fake News dataset, which contains 26320 fake news data collected from Weibo. In addition, we propose a fake news detection model based on data augmentation that can effectively solve the problem of a lack of fake news, and we improve the generalization ability and robustness of the model. We conduct numerous experiments on our Chinese Weibo Fake News dataset and successfully deploy the model on the web page. The experimental performance proves the effectiveness of the proposed end-to-end model for detecting fake news on social media platforms.