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Anatomy of an online misinformation network

Author

Listed:
  • Chengcheng Shao
  • Pik-Mai Hui
  • Lei Wang
  • Xinwen Jiang
  • Alessandro Flammini
  • Filippo Menczer
  • Giovanni Luca Ciampaglia

Abstract

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.

Suggested Citation

  • Chengcheng Shao & Pik-Mai Hui & Lei Wang & Xinwen Jiang & Alessandro Flammini & Filippo Menczer & Giovanni Luca Ciampaglia, 2018. "Anatomy of an online misinformation network," PLOS ONE, Public Library of Science, vol. 13(4), pages 1-23, April.
  • Handle: RePEc:plo:pone00:0196087
    DOI: 10.1371/journal.pone.0196087
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    Cited by:

    1. Joshua Uyheng & Lynnette Hui Xian Ng & Kathleen M. Carley, 2021. "Active, aggressive, but to little avail: characterizing bot activity during the 2020 Singaporean elections," Computational and Mathematical Organization Theory, Springer, vol. 27(3), pages 324-342, September.
    2. Kathie M. d'I. Treen & Hywel T. P. Williams & Saffron J. O'Neill, 2020. "Online misinformation about climate change," Wiley Interdisciplinary Reviews: Climate Change, John Wiley & Sons, vol. 11(5), September.
    3. Bartosz Wilczek, 2020. "Misinformation and herd behavior in media markets: A cross-national investigation of how tabloids’ attention to misinformation drives broadsheets’ attention to misinformation in political and business," PLOS ONE, Public Library of Science, vol. 15(11), pages 1-22, November.
    4. Cioroianu, Iulia & Corbet, Shaen & Larkin, Charles, 2021. "The differential impact of corporate blockchain-development as conditioned by sentiment and financial desperation," Journal of Corporate Finance, Elsevier, vol. 66(C).
    5. Yaming Zhang & Wenjie Song & Jiang Shao & Majed Abbas & Jiaqi Zhang & Yaya H. Koura & Yanyuan Su, 2023. "Social Bots’ Role in the COVID-19 Pandemic Discussion on Twitter," IJERPH, MDPI, vol. 20(4), pages 1-21, February.
    6. James Flamino & Alessandro Galeazzi & Stuart Feldman & Michael W. Macy & Brendan Cross & Zhenkun Zhou & Matteo Serafino & Alexandre Bovet & Hernán A. Makse & Boleslaw K. Szymanski, 2023. "Political polarization of news media and influencers on Twitter in the 2016 and 2020 US presidential elections," Nature Human Behaviour, Nature, vol. 7(6), pages 904-916, June.

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