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Realistic Aspects of Simulation Models for Fake News Epidemics over Social Networks

Author

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  • Quintino Francesco Lotito

    (Department of Information Engineering and Computer Science (DISI), University of Trento, Via Sommarive 9, 38123 Povo, TN, Italy
    These authors contributed equally to this work.)

  • Davide Zanella

    (Department of Information Engineering and Computer Science (DISI), University of Trento, Via Sommarive 9, 38123 Povo, TN, Italy
    These authors contributed equally to this work.)

  • Paolo Casari

    (Department of Information Engineering and Computer Science (DISI), University of Trento, Via Sommarive 9, 38123 Povo, TN, Italy)

Abstract

The pervasiveness of online social networks has reshaped the way people access information. Online social networks make it common for users to inform themselves online and share news among their peers, but also favor the spreading of both reliable and fake news alike. Because fake news may have a profound impact on the society at large, realistically simulating their spreading process helps evaluate the most effective countermeasures to adopt. It is customary to model the spreading of fake news via the same epidemic models used for common diseases; however, these models often miss concepts and dynamics that are peculiar to fake news spreading. In this paper, we fill this gap by enriching typical epidemic models for fake news spreading with network topologies and dynamics that are typical of realistic social networks. Specifically, we introduce agents with the role of influencers and bots in the model and consider the effects of dynamical network access patterns, time-varying engagement, and different degrees of trust in the sources of circulating information. These factors concur with making the simulations more realistic. Among other results, we show that influencers that share fake news help the spreading process reach nodes that would otherwise remain unaffected. Moreover, we emphasize that bots dramatically speed up the spreading process and that time-varying engagement and network access change the effectiveness of fake news spreading.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jftint:v:13:y:2021:i:3:p:76-:d:518994
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    References listed on IDEAS

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    5. Jonathan Albright, 2017. "Welcome to the Era of Fake News," Media and Communication, Cogitatio Press, vol. 5(2), pages 87-89.
    6. Spricer, Kristoffer & Britton, Tom, 2019. "An SIR epidemic on a weighted network," Network Science, Cambridge University Press, vol. 7(4), pages 556-580, December.
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    1. Raffaele D’Ambrosio & Giuseppe Giordano & Serena Mottola & Beatrice Paternoster, 2021. "Stiffness Analysis to Predict the Spread Out of Fake Information," Future Internet, MDPI, vol. 13(9), pages 1-10, August.

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