Fake news propagates differently from real news even at early stages of spreading

• Published in: EPJ data science Vol. 9; no. 1; pp. 7 - 14
• Main Authors: Zhao, Zilong, Zhao, Jichang, Sano, Yukie, Levy, Orr, Takayasu, Hideki, Takayasu, Misako, Li, Daqing, Wu, Junjie, Havlin, Shlomo
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 03.04.2020; Springer Nature B.V; SpringerOpen
• Subjects: Complexity; Computer Appl. in Social and Behavioral Sciences; Computer Science; Data-driven Science; Digital media; Early detection; Fake news; Identification methods; Machine learning; Modeling and Theory Building; News; Population studies; Propagation; Regular Article; Social network; Social networks; Social organization; Social network; Fake news; Early detection
• ISSN: 2193-1127; 2193-1127
• DOI: 10.1140/epjds/s13688-020-00224-z

Social media can be a double-edged sword for society, either as a convenient channel exchanging ideas or as an unexpected conduit circulating fake news through a large population. While existing studies of fake news focus on theoretical modeling of propagation or identification methods based on machine learning, it is important to understand the realistic propagation mechanisms between theoretical models and black-box methods. Here we track large databases of fake news and real news in both, Weibo in China and Twitter in Japan from different cultures, which include their traces of re-postings. We find in both online social networks that fake news spreads distinctively from real news even at early stages of propagation, e.g. five hours after the first re-postings. Our finding demonstrates collective structural signals that help to understand the different propagation evolution of fake news and real news. Different from earlier studies, identifying the topological properties of the information propagation at early stages may offer novel features for early detection of fake news in social media.