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SSIC model: A multi-layer model for intervention of online rumors spreading

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  • Tian, Ru-Ya
  • Zhang, Xue-Fu
  • Liu, Yi-Jun

Abstract

SIR model is a classical model to simulate rumor spreading, while the supernetwork is an effective tool for modeling complex systems. Based on the Opinion SuperNetwork involving Social Sub-network, Environmental Sub-network, Psychological Sub-network, and Viewpoint Sub-network, drawing from the modeling idea of SIR model, this paper designs super SIC model (SSIC model) and its evolution rules, and also analyzes intervention effects on public opinion of four elements of supernetwork, which are opinion agent, opinion environment, agent’s psychology and viewpoint. Studies show that, the SSIC model based on supernetwork has effective intervention effects on rumor spreading. It is worth noting that (i) identifying rumor spreaders in Social Sub-network and isolating them can achieve desired intervention results, (ii) improving environmental information transparency so that the public knows as much information as possible to reduce the rumors is a feasible way to intervene, (iii) persuading wavering neutrals has better intervention effects than clarifying rumors already spread everywhere, so rumors should be intervened in properly in time by psychology counseling.

Suggested Citation

  • Tian, Ru-Ya & Zhang, Xue-Fu & Liu, Yi-Jun, 2015. "SSIC model: A multi-layer model for intervention of online rumors spreading," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 427(C), pages 181-191.
    • Handle: RePEc:eee:phsmap:v:427:y:2015:i:c:p:181-191
    DOI: 10.1016/j.physa.2015.02.008
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    References listed on IDEAS

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    4. Yao, Yao & Xiao, Xi & Zhang, Chengping & Dou, Changsheng & Xia, Shutao, 2019. "Stability analysis of an SDILR model based on rumor recurrence on social media," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 535(C).
    5. Lu, Peng & Deng, Liping & Liao, Hongbing, 2019. "Conditional effects of individual judgment heterogeneity in information dissemination," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 335-344.
    6. Liming Zhao & Haihong Zhang & Wenqing Wu, 2019. "Cooperative knowledge creation in an uncertain network environment based on a dynamic knowledge supernetwork," Scientometrics, Springer;Akadémiai Kiadó, vol. 119(2), pages 657-685, May.
    7. Tian, Ru-Ya & Wu, Lei & Liang, Xiao-He & Zhang, Xue-Fu, 2018. "Opinion data mining based on DNA method and ORA software," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 1471-1480.
    8. Lu, Peng & Yao, Qi & Lu, Pengfei, 2019. "Two-stage predictions of evolutionary dynamics during the rumor dissemination," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 517(C), pages 349-369.
    9. Fredy S. Monge-Rodríguez & He Jiang & Liwei Zhang & Andy Alvarado-Yepez & Anahí Cardona-Rivero & Enma Huaman-Chulluncuy & Analy Torres-Mejía, 2021. "Psychological Factors Affecting Risk Perception of COVID-19: Evidence from Peru and China," IJERPH, MDPI, vol. 18(12), pages 1-16, June.
    10. Lu, Peng, 2019. "Heterogeneity, judgment, and social trust of agents in rumor spreading," Applied Mathematics and Computation, Elsevier, vol. 350(C), pages 447-461.

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