Anatomy of an online misinformation network
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Abstract (arXiv)
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 filters public tweets that include links to unverified claims or fact-checking articles. 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.
Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). First posted on arXiv 18 Jan 2018.
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Citing papers per year: 2021: 3; 2022: 1; 2023: 2; 2024: 1; 2025: 1.
8 papers.
- 2023Simplistic Collection and Labeling Practices Limit the Utility of Benchmark Datasets for Twitter Bot Detectioncited by 13
- 2021Model Generalization on COVID-19 Fake News Detectioncited by 11
- 2022Explainable Misinformation Detection Across Multiple Social Media Platformscited by 5
- 2025An Explainable Framework for Misinformation Identification via Critical Question Answeringcited by 4
- 2023From Online Behaviours to Images: A Novel Approach to Social Bot Detectioncited by 2
- 2024Bangla AI: A Framework for Machine Translation Utilizing Large Language Models for Ethnic Mediacited by 1
- 2021A Hierarchical Network-Oriented Analysis of User Participation in Misinformation Spread on WhatsAppcited by 0
- 2021Personalized multi-faceted trust modeling to determine trust links in social media and its potential for misinformation managementcited by 0
- 2025An Explainable Framework for Misinformation Identification via Critical Question Answering18 Mar 2025
- 2024Bangla AI: A Framework for Machine Translation Utilizing Large Language Models for Ethnic Media21 Feb 2024
- 2023From Online Behaviours to Images: A Novel Approach to Social Bot Detection15 Apr 2023
- 2023Simplistic Collection and Labeling Practices Limit the Utility of Benchmark Datasets for Twitter Bot Detection17 Jan 2023
- 2022Explainable Misinformation Detection Across Multiple Social Media Platforms20 Mar 2022
- 2021Personalized multi-faceted trust modeling to determine trust links in social media and its potential for misinformation management11 Nov 2021
- 2021A Hierarchical Network-Oriented Analysis of User Participation in Misinformation Spread on WhatsApp22 Sep 2021
- 2021Model Generalization on COVID-19 Fake News Detection11 Jan 2021