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From agent-based models to the macroscopic description of fake-news spread: the role of competence in data-driven applications

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  • J. Franceschi

    (University of Pavia)

  • L. Pareschi

    (University of Ferrara)

  • M. Zanella

    (University of Pavia)

Abstract

Fake news spreading, with the aim of manipulating individuals’ perceptions of facts, is now recognized as a major problem in many democratic societies. Yet, to date, little has been understood about how fake news spreads on social networks, what the influence of the education level of individuals is, when fake news is effective in influencing public opinion, and what interventions might be successful in mitigating their effect. In this paper, starting from the recently introduced kinetic multi-agent model with competence by the first two authors, we propose to derive reduced-order models through the notion of social closure in the mean-field approximation that has its roots in the classical hydrodynamic closure of kinetic theory. This approach allows to obtain simplified models in which the competence and learning of the agents maintain their role in the dynamics and, at the same time, the structure of such models is more suitable to be interfaced with data-driven applications. Examples of different Twitter-based test cases are described and discussed.

Suggested Citation

  • J. Franceschi & L. Pareschi & M. Zanella, 2022. "From agent-based models to the macroscopic description of fake-news spread: the role of competence in data-driven applications," Partial Differential Equations and Applications, Springer, vol. 3(6), pages 1-26, December.
    • Handle: RePEc:spr:pardea:v:3:y:2022:i:6:d:10.1007_s42985-022-00194-z
    DOI: 10.1007/s42985-022-00194-z
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    References listed on IDEAS

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    1. Taichi Murayama & Shoko Wakamiya & Eiji Aramaki & Ryota Kobayashi, 2021. "Modeling the spread of fake news on Twitter," PLOS ONE, Public Library of Science, vol. 16(4), pages 1-16, April.
    2. Pareschi, Lorenzo & Vellucci, Pierluigi & Zanella, Mattia, 2017. "Kinetic models of collective decision-making in the presence of equality bias," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 467(C), pages 201-217.
    3. Pareschi, Lorenzo & Toscani, Giuseppe, 2013. "Interacting Multiagent Systems: Kinetic equations and Monte Carlo methods," OUP Catalogue, Oxford University Press, number 9780199655465.
    4. Lorenzo Pareschi & Giuseppe Toscani, 2014. "Wealth distribution and collective knowledge. A Boltzmann approach," Papers 1401.4550, arXiv.org.
    5. Gualandi, Stefano & Toscani, Giuseppe, 2018. "Pareto tails in socio-economic phenomena: A kinetic description," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 12, pages 1-17.
    6. Joshua Uyheng & Kathleen M. Carley, 2020. "Bots and online hate during the COVID-19 pandemic: case studies in the United States and the Philippines," Journal of Computational Social Science, Springer, vol. 3(2), pages 445-468, November.
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