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Comparing information diffusion mechanisms by matching on cascade size

Author

Listed:
  • Jonas L. Juul

    (a Center for Applied Mathematics, Cornell University, Ithaca, NY 14853;)

  • Johan Ugander

    (b Management Science and Engineering, Stanford University, Stanford, CA 94305)

Abstract

Do different types of information spread differently online? In recent years, studies have sought answers to such questions by comparing statistical properties of network paths taken by different kinds of content diffusing online. Here, we demonstrate the importance of controlling for correlations between properties being compared. In particular, we show that previously reported structural differences between diffusion paths of false and true news on Twitter disappear when comparing only cascades of the same size; differences between diffusion paths of images, videos, news, and petitions persist. Paired with a theoretical analysis of diffusion processes, our results suggest that, in order to limit the spread of false news, it may be enough to focus on reducing the mean “infectiousness” of the information.

Suggested Citation

  • Jonas L. Juul & Johan Ugander, 2021. "Comparing information diffusion mechanisms by matching on cascade size," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 118(46), pages 2100786118-, November.
    • Handle: RePEc:nas:journl:v:118:y:2021:p:e2100786118
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    Citations

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    Cited by:

    1. Buechel, Berno & Klößner, Stefan & Meng, Fanyuan & Nassar, Anis, 2023. "Misinformation due to asymmetric information sharing," Journal of Economic Dynamics and Control, Elsevier, vol. 150(C).
    2. Joseph B. Bak-Coleman & Ian Kennedy & Morgan Wack & Andrew Beers & Joseph S. Schafer & Emma S. Spiro & Kate Starbird & Jevin D. West, 2022. "Combining interventions to reduce the spread of viral misinformation," Nature Human Behaviour, Nature, vol. 6(10), pages 1372-1380, October.
    3. 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.
    4. Li, Ai-Wen & Xu, Xiao-Ke & Fan, Ying, 2022. "Immunization strategies for false information spreading on signed social networks," Chaos, Solitons & Fractals, Elsevier, vol. 162(C).

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