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Network segregation in a model of misinformation and fact-checking

Author

Listed:
  • Marcella Tambuscio

    (University of Turin)

  • Diego F. M. Oliveira

    (Indiana University
    US Army Research Laboratory
    Rensselaer Polytechnic Institute)

  • Giovanni Luca Ciampaglia

    (Indiana University)

  • Giancarlo Ruffo

    (University of Turin)

Abstract

Misinformation under the form of rumor, hoaxes, and conspiracy theories spreads on social media at alarming rates. One hypothesis is that, since social media are shaped by homophily, belief in misinformation may be more likely to thrive on those social circles that are segregated from the rest of the network. One possible antidote to misinformation is fact checking which, however, does not always stop rumors from spreading further, owing to selective exposure and our limited attention. What are the conditions under which factual verification are effective at containing the spreading of misinformation? Here we take into account the combination of selective exposure due to network segregation, forgetting (i.e., finite memory), and fact-checking. We consider a compartmental model of two interacting epidemic processes over a network that is segregated between gullible and skeptic users. Extensive simulation and mean-field analysis show that a more segregated network facilitates the spread of a hoax only at low forgetting rates, but has no effect when agents forget at faster rates. This finding may inform the development of mitigation techniques and raise awareness on the risks of uncontrolled misinformation online.

Suggested Citation

  • Marcella Tambuscio & Diego F. M. Oliveira & Giovanni Luca Ciampaglia & Giancarlo Ruffo, 2018. "Network segregation in a model of misinformation and fact-checking," Journal of Computational Social Science, Springer, vol. 1(2), pages 261-275, September.
  • Handle: RePEc:spr:jcsosc:v:1:y:2018:i:2:d:10.1007_s42001-018-0018-9
    DOI: 10.1007/s42001-018-0018-9
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    References listed on IDEAS

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    Cited by:

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    2. Oliveira, Diego F.M. & Chan, Kevin S., 2019. "The effects of trust and influence on the spreading of low and high quality information," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 657-663.
    3. Quintino Francesco Lotito & Davide Zanella & Paolo Casari, 2021. "Realistic Aspects of Simulation Models for Fake News Epidemics over Social Networks," Future Internet, MDPI, vol. 13(3), pages 1-20, March.
    4. Juan Miguel Rodriguez-Lopez & Meike Schickhoff & Shubhankar Sengupta & Jürgen Scheffran, 2021. "Technological and social networks of a pastoralist artificial society: agent-based modeling of mobility patterns," Journal of Computational Social Science, Springer, vol. 4(2), pages 681-707, November.
    5. Salvatore Vilella & Mirko Lai & Daniela Paolotti & Giancarlo Ruffo, 2020. "Immigration as a Divisive Topic: Clusters and Content Diffusion in the Italian Twitter Debate," Future Internet, MDPI, vol. 12(10), pages 1-22, October.

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