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A normative approach to radicalization in social networks

Author

Listed:
  • Vincent Bouttier

    (PSL University
    Lille University)

  • Salomé Leclercq

    (Lille University)

  • Renaud Jardri

    (PSL University
    Lille University)

  • Sophie Denève

    (PSL University)

Abstract

In recent decades, the massification of online social connections has made information globally accessible in a matter of seconds. Unfortunately, this has been accompanied by a dramatic surge in extreme opinions, without a clear solution in sight. Using a model performing probabilistic inference in large-scale loopy graphs through exchange of messages between nodes, we show how circularity in the social graph directly leads to radicalization and the polarization of opinions. We demonstrate that these detrimental effects could be avoided if the correlations between incoming messages could be decreased. This approach is based on an extension of Belief Propagation (BP) named Circular Belief Propagation (CBP) that can be trained to drastically improve inference within a cyclic graph. CBP was benchmarked using data from Facebook© and Twitter©. This approach could inspire new methods for preventing the viral spreading and amplification of misinformation online, improving the capacity of social networks to share knowledge globally without resorting to censorship.

Suggested Citation

  • Vincent Bouttier & Salomé Leclercq & Renaud Jardri & Sophie Denève, 2024. "A normative approach to radicalization in social networks," Journal of Computational Social Science, Springer, vol. 7(1), pages 1071-1093, April.
    • Handle: RePEc:spr:jcsosc:v:7:y:2024:i:1:d:10.1007_s42001-024-00267-6
    DOI: 10.1007/s42001-024-00267-6
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    References listed on IDEAS

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    1. Kazutoshi Sasahara & Wen Chen & Hao Peng & Giovanni Luca Ciampaglia & Alessandro Flammini & Filippo Menczer, 2021. "Social influence and unfollowing accelerate the emergence of echo chambers," Journal of Computational Social Science, Springer, vol. 4(1), pages 381-402, May.
    2. Jeff Tollefson, 2021. "Tracking QAnon: how Trump turned conspiracy-theory research upside down," Nature, Nature, vol. 590(7845), pages 192-193, February.
    3. A. Barrat & M. Weigt, 2000. "On the properties of small-world network models," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 13(3), pages 547-560, February.
    4. Praveen Suthaharan & Erin J. Reed & Pantelis Leptourgos & Joshua G. Kenney & Stefan Uddenberg & Christoph D. Mathys & Leib Litman & Jonathan Robinson & Aaron J. Moss & Jane R. Taylor & Stephanie M. Gr, 2021. "Paranoia and belief updating during the COVID-19 crisis," Nature Human Behaviour, Nature, vol. 5(9), pages 1190-1202, September.
    5. Alessandro Vespignani, 2018. "Twenty years of network science," Nature, Nature, vol. 558(7711), pages 528-529, June.
    6. Pantelis Leptourgos & Vincent Bouttier & Renaud Jardri & Sophie Denève, 2020. "A functional theory of bistable perception based on dynamical circular inference," PLOS Computational Biology, Public Library of Science, vol. 16(12), pages 1-23, December.
    7. Guillaume Deffuant & David Neau & Frederic Amblard & Gérard Weisbuch, 2000. "Mixing beliefs among interacting agents," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 3(01n04), pages 87-98.
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