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

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.

Document Details

Document Type
Pub Defense Publication
Publication Date
Apr 03, 2020
Source ID
10.1140/epjds/s13688-020-00224-z

Entities

People

  • Daqing Li
  • Hideki Takayasu
  • Jichang Zhao
  • Junjie Wu
  • Misako Takayasu
  • Orr Levy
  • Shlomo Havlin
  • Yukie Sano
  • Zilong Zhao

Organizations

  • Defense Threat Reduction Agency
  • National Natural Science Foundation of China

Tags

Readers

  • Educational Psychology
  • Neural Network Machine Learning.
  • Political Science/ International Relations/ European Studies

Technology Areas

  • AI & ML
  • AI & ML - Information Retrieval
  • AI & ML - Neural Networks