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.
|Journal||EPJ Data Science|
|State||Published - 1 Dec 2020|
Bibliographical noteFunding Information:
SH thanks the Israel Science Foundation, ONR, the Israel Ministry of Science and Technology (MOST) with the Italy Ministry of Foreign Affairs, BSF-NSF, MOST with the Japan Science and Technology Agency, the BIU Center for Research in Applied Cryptography and Cyber Security, and DTRA (Grant no. HDTRA-1-10-1-0014) for financial support. JZ was supported by NSFC (No. 71871006) and the National Key Research and Development Program of China (No. 2016QY01W0205). YS was supported by by JSPS KAKENHI Grand Number 17K12783. HT and MT are supported by JST Strategic International Collaborative Research Program (SICORP) on the topic of “ICT for a Resilient Society” by Japan and Israel. JW was partially supported by the National Key R&D Program of China (2019YFB2101804), the National Special Program on Innovation Methodologies (SQ2019IM4910001), and the National Natural Science Foundation of China (71531001, 71725002, U1636210). We also thank Jiali Gao for providing new dataset of real news.
© 2020, The Author(s).
- Early detection
- Fake news
- Social network