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Mutual Influence of Users Credibility and News Spreading in Online Social Networks

Author

Listed:
  • Vincenza Carchiolo

    (Dipartimento di Matematica e Informatica (DMI), Università di Catania, 95131 Catania, Italy)

  • Alessandro Longheu

    (Dipartimento di Ingegneria Elettrica Elettronica e Informatica (DIEEI), Università di Catania, 95131 Catania, Italy)

  • Michele Malgeri

    (Dipartimento di Ingegneria Elettrica Elettronica e Informatica (DIEEI), Università di Catania, 95131 Catania, Italy)

  • Giuseppe Mangioni

    (Dipartimento di Ingegneria Elettrica Elettronica e Informatica (DIEEI), Università di Catania, 95131 Catania, Italy)

  • Marialaura Previti

    (Dipartimento di Ingegneria Elettrica Elettronica e Informatica (DIEEI), Università di Catania, 95131 Catania, Italy)

Abstract

A real-time news spreading is now available for everyone, especially thanks to Online Social Networks (OSNs) that easily endorse gate watching, so the collective intelligence and knowledge of dedicated communities are exploited to filter the news flow and to highlight and debate relevant topics. The main drawback is that the responsibility for judging the content and accuracy of information moves from editors and journalists to online information users, with the side effect of the potential growth of fake news. In such a scenario, trustworthiness about information providers cannot be overlooked anymore, rather it more and more helps in discerning real news from fakes. In this paper we evaluate how trustworthiness among OSN users influences the news spreading process. To this purpose, we consider the news spreading as a Susceptible-Infected-Recovered (SIR) process in OSN, adding the contribution credibility of users as a layer on top of OSN. Simulations with both fake and true news spreading on such a multiplex network show that the credibility improves the diffusion of real news while limiting the propagation of fakes. The proposed approach can also be extended to real social networks.

Suggested Citation

  • Vincenza Carchiolo & Alessandro Longheu & Michele Malgeri & Giuseppe Mangioni & Marialaura Previti, 2021. "Mutual Influence of Users Credibility and News Spreading in Online Social Networks," Future Internet, MDPI, vol. 13(5), pages 1-15, April.
    • Handle: RePEc:gam:jftint:v:13:y:2021:i:5:p:107-:d:543298
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    References listed on IDEAS

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    1. Jari Jussila & Anu Helena Suominen & Atte Partanen & Tapani Honkanen, 2021. "Text Analysis Methods for Misinformation–Related Research on Finnish Language Twitter," Future Internet, MDPI, vol. 13(6), pages 1-16, June.

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