Published: April 27, 2018
Massive amounts of fake news and conspiratorial content have spread over social media before and after the 2016 US Presidential Elections despite intense fact-checking efforts. How do the spread of misinformation and fact-checking compete? What are the structural and dynamic characteristics of the core of the misinformation diffusion network, and who are its main purveyors? How to reduce the overall amount of misinformation? To explore these questions we built Hoaxy, an open platform that enables large-scale, systematic studies of how misinformation and fact-checking spread and compete on Twitter. Hoaxy captures public tweets that include links to articles from low-credibility and fact-checking sources. We perform k-core decomposition on a diffusion network obtained from two million retweets produced by several hundred thousand accounts over the six months before the election. As we move from the periphery to the core of the network, fact-checking nearly disappears, while social bots proliferate. The number of users in the main core reaches equilibrium around the time of the election, with limited churn and increasingly dense connections. We conclude by quantifying how effectively the network can be disrupted by penalizing the most central nodes. These findings provide a first look at the anatomy of a massive online misinformation diffusion network.
Citation: Shao C, Hui P-M, Wang L, Jiang X, Flammini A, Menczer F, et al. (2018) Anatomy of an online misinformation network. PLoS ONE 13(4): e0196087. https://doi.org/10.1371/journal.pone.0196087
Editor: Alain Barrat, Centre de physique theorique, FRANCE
Received: January 20, 2018; Accepted: April 5, 2018; Published: April 27, 2018
Copyright: © 2018 Shao et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All the analyses presented in this paper can be replicated by collecting data through the Hoaxy API (https://market.mashape.com/truthy/hoaxy) or downloading the network dataset at doi.org/10.5281/zenodo.1154840.
Funding: C.S. was supported by the China Scholarship Council. X.J. was supported in part by the National Natural Science Foundation of China (No. 61272010). G.L.C. was supported by Indiana University Network Science Institute. The development of the Botometer platform was supported in part by DARPA (grant W911NF-12-1-0037) and Democracy Fund. A.F. and F.M. were supported in part by the James S. McDonnell Foundation (grant 220020274) and the National Science Foundation (award CCF-1101743). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.