An Unsupervised Fake News Detection Framework Based on Structural Contrastive Learning - Yajie Guo; Shujuan Ji; Xianwen Fang; Dickson K. W. Chiu; Ning Cao; Hofung Leung (Cybersecurity)
Abstract Recently, fake news detection on social media (SM) has attracted a lot of attention. With the emergence of fake news at a breakneck pace, the massive spread of fake news has had a serious impact in our society. The authenticity of the news is questionable and there exists a necessity for an automated tool for the detection. However, most fake news detection methods are mainly supervised, requiring huge amounts of annotated data, which is time-consuming, expensive, and almost impossible with vast new SM volume. To deal with this problem, in this paper, we propose a novel unsupervised fake news detection framework based on structural contrastive learning by combining the propagation structure of news and contrastive learning to achieve unsupervised training. To validate the influence of parameters and our method’s performance, we design experiment sets on public Twitter and Weibo datasets, which validate our approach outperforms current baseline ones and has proper robustness.