Early Detection of Fake News by Utilizing the Credibility of News, Publishers, and Users based on Weakly Supervised Learning

Proceedings of the 28th International Conference on Computational Linguistics, pages 5444–5454 Barcelona, Spain (Online), December 8-13, 2020 5444 Early Detection of Fake News by Utilizing the Credibility of News, Publishers, and Users Based on Weakly Supervised Learning Chunyuan Yuan1,2, Qianwen Ma1,2, Wei Zhou1,*, Jizhong Han2 and Songlin Hu1,2,* 1 Institute of Information Engineering, Chinese Academy of Sciences 1 School of Cyber Security, University of Chinese Academy of Sciences {yuanchunyuan,maqianwen,zhouwei,hanjizhong,husonglin}@iie.ac.cn Abstract The dissemination of fake news significantly affects personal reputation and public trust. Re- cently, fake news detection has attracted tremendous attention, and previous studies mainly fo- cused on finding clues from news content or diffusion path. However, the required features of previous models are often unavailable or insufficient in early detection scenarios, resulting in poor performance. Thus, early fake news detection remains a tough challenge. Intuitively, the news from trusted and authoritative sources or shared by many users with a good reputation is more reliable than other news. Using the credibility of publishers and users as prior weakly su- pervised information, we can quickly locate fake news in massive news and detect them in the early stages of dissemination. In this paper, we propose a novel Structure-aware Multi-head Attention Network (SMAN), which combines the news content, publishing, and reposting relations of publishers and users, to jointly optimize the fake news detection and credibility prediction tasks. In this way, we can explicitly exploit the credibility of publishers and users for early fake news detection. We conducted ex- periments on three real-world datasets, and the results show that SMAN can detect fake news in 4 hours with an accuracy of over 91%, which is much faster than the state-of-the-art models. 1 Introduction The widespread dissemination of fake news has lead to a significant influence on personal fame, public trust, and security. For example, spreading misinformation, such as “Asians are more vulnerable to novel coronavirus” 1 about COVID-19 has very serious repercussions, making people ignore the harmfulness of the virus and directly affecting public health. Research has shown that misinformation spreads faster, farther, deeper, and more widely than true information (Vosoughi et al., 2018). Therefore, fake news detection on social media has attracted tremendous attention recently in both research and industrial fields. Early research on fake news detection mainly focused on the design of effective features from various sources, including textual content, user profiling data, and news diffusion patterns. Linguistic features, such as writing styles and sensational headlines (Kwon et al., 2013), lexical and syntactic analysis (Pot- thast et al., 2017), have been explored to separate fake news from true news. Apart from linguistic features, some studies also proposed a series of user-based features (Castillo et al., 2011; Shu et al., 2018), and temporal features (Kwon et al., 2013) about the news diffusion. However, these feature-based methods are very time-consuming, biased, and require a lot of labor to design. Besides, these features are easily manipulated by users. To solve the above problems, many recent studies (Ma et al., 2016; Yu et al., 2017; …

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