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Impacts of suppressing guide on information spreading

Author

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  • Xu, Jinghong
  • Zhang, Lin
  • Ma, Baojun
  • Wu, Ye

Abstract

It is quite common that guides are introduced to suppress the information spreading in modern society for different purposes. In this paper, an agent-based model is established to quantitatively analyze the impacts of suppressing guides on information spreading. We find that the spreading threshold depends on the attractiveness of the information and the topology of the social network with no suppressing guides at all. Usually, one would expect that the existence of suppressing guides in the spreading procedure may result in less diffusion of information within the overall network. However, we find that sometimes the opposite is true: the manipulating nodes of suppressing guides may lead to more extensive information spreading when there are audiences with the reversal mind. These results can provide valuable theoretical references to public opinion guidance on various information, e.g., rumor or news spreading.

Suggested Citation

  • Xu, Jinghong & Zhang, Lin & Ma, Baojun & Wu, Ye, 2016. "Impacts of suppressing guide on information spreading," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 444(C), pages 922-927.
  • Handle: RePEc:eee:phsmap:v:444:y:2016:i:c:p:922-927
    DOI: 10.1016/j.physa.2015.10.059
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    References listed on IDEAS

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    1. Wei, Dong & Zhou, Tao & Cimini, Giulio & Wu, Pei & Liu, Weiping & Zhang, Yi-Cheng, 2011. "Effective mechanism for social recommendation of news," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(11), pages 2117-2126.
    2. G. Cimini & M. Medo & T. Zhou & D. Wei & Y.-C. Zhang, 2011. "Heterogeneity, quality, and reputation in an adaptive recommendation model," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 80(2), pages 201-208, March.
    3. Centola, Damon & Eguíluz, Víctor M. & Macy, Michael W., 2007. "Cascade dynamics of complex propagation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 374(1), pages 449-456.
    4. Modis, Theodore, 2007. "Strengths and weaknesses of S-curves," OSF Preprints r5zk7, Center for Open Science.
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