EPJ Data Sci. (2020) 9: 7
https://doi.org/10.1140/epjds/s13688-020-00224-z

Regular article

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

¹ School of Reliability and Systems Engineering, Beihang University, Beijing, China
² National Key Laboratory of Science and Technology on Reliability and Environmental Engineering, Beijing, China
³ School of Economics and Management, Beihang University, Beijing, China
⁴ Faculty of Engineering, Information and Systems, University of Tsukuba, Tsukuba, Japan
⁵ Department of Physics, Bar-Ilan University, Ramat Gan, Israel
⁶ Sony Computer Science Laboratories, Tokyo, Japan
⁷ Institute of Innovative Research, Tokyo Institute of Technology, Yokohama, Japan
⁸ Beijing Advanced Innovation Center for Big Data and Brain Computing, Beihang University, Beijing, China

^* e-mail: daqingl@buaa.edu.cn
^** e-mail: wujj@buaa.edu.cn

Received: 14 June 2019
Accepted: 25 February 2020
Published online: 3 April 2020

Abstract

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.