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A Novel Model For Rumor Spreading On Social Networks With Considering The Influence Of Dissenting Opinions

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  • AMIRHOSEIN BODAGHI

    (Faculty of New Sciences and Technologies, University of Tehran, Kargar Street, Tehran, Iran)

  • SAMA GOLIAEI

    (Faculty of New Sciences and Technologies, University of Tehran, Kargar Street, Tehran, Iran)

Abstract

Rumor spreading is a good sample of spreading in which human beings are the main players in the spreading process. Therefore, in order to have a more realistic model of rumor spreading on online social networks, the influence of psycho-sociological factors particularly those which affect users’ reactions toward rumor/anti-rumor should be considered. To this aim, we present a new model that considers the influence of dissenting opinions on those users who have already believed in rumor/anti-rumor but have not spread the rumor/anti-rumor yet. We hypothesize that influence is a motive for the believers to spread their beliefs in rumor/anti-rumor. We derive the stochastic equations of the new model and evaluate it by using two real datasets of rumor spreading on Twitter. The evaluation results support the new hypothesis and show that the novel model which is relied on the new hypothesis is able to better represent rumor spreading.

Suggested Citation

  • Amirhosein Bodaghi & Sama Goliaei, 2018. "A Novel Model For Rumor Spreading On Social Networks With Considering The Influence Of Dissenting Opinions," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 21(06n07), pages 1-24, September.
    • Handle: RePEc:wsi:acsxxx:v:21:y:2018:i:06n07:n:s021952591850011x
    DOI: 10.1142/S021952591850011X
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    References listed on IDEAS

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    Cited by:

    1. Bodaghi, Amirhosein & Goliaei, Sama & Salehi, Mostafa, 2019. "The number of followings as an influential factor in rumor spreading," Applied Mathematics and Computation, Elsevier, vol. 357(C), pages 167-184.
    2. Jain, Lokesh, 2022. "An entropy-based method to control COVID-19 rumors in online social networks using opinion leaders," Technology in Society, Elsevier, vol. 70(C).
    3. Cheng, Yingying & Huo, Liang’an & Zhao, Laijun, 2020. "Rumor spreading in complex networks under stochastic node activity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 559(C).
    4. Amirhosein Bodaghi & Jonathan J. H. Zhu, 2024. "A big data analysis of the adoption of quoting encouragement policy on Twitter during the 2020 U.S. presidential election," Journal of Computational Social Science, Springer, vol. 7(2), pages 1861-1893, October.
    5. Jan Lorenz & Martin Neumann, 2018. "Opinion Dynamics And Collective Decisions," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 21(06n07), pages 1-9, September.

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