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SHIR competitive information diffusion model for online social media

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  • Liu, Yun
  • Diao, Su-Meng
  • Zhu, Yi-Xiang
  • Liu, Qing

Abstract

In online social media, opinion divergences and differentiations generally exist as a result of individuals’ extensive participation and personalization. In this paper, a Susceptible–Hesitated–Infected–Removed (SHIR) model is proposed to study the dynamics of competitive dual information diffusion. The proposed model extends the classical SIR model by adding hesitators as a neutralized state of dual information competition. It is both hesitators and stable spreaders that facilitate information dissemination. Researching on the impacts of diffusion parameters, it is found that the final density of stiflers increases monotonically as infection rate increases and removal rate decreases. And the advantage information with larger stable transition rate takes control of whole influence of dual information. The density of disadvantage information spreaders slightly grows with the increase of its stable transition rate, while whole spreaders of dual information and the relaxation time remain almost unchanged. Moreover, simulations imply that the final result of competition is closely related to the ratio of stable transition rates of dual information. If the stable transition rates of dual information are nearly the same, a slightly reduction of the smaller one brings out a significant disadvantage in its propagation coverage. Additionally, the relationship of the ratio of final stiflers versus the ratio of stable transition rates presents power characteristic.

Suggested Citation

  • Liu, Yun & Diao, Su-Meng & Zhu, Yi-Xiang & Liu, Qing, 2016. "SHIR competitive information diffusion model for online social media," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 461(C), pages 543-553.
  • Handle: RePEc:eee:phsmap:v:461:y:2016:i:c:p:543-553
    DOI: 10.1016/j.physa.2016.06.080
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    3. Zhang, Yaming & Su, Yanyuan & Weigang, Li & Liu, Haiou, 2018. "Rumor and authoritative information propagation model considering super spreading in complex social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 506(C), pages 395-411.
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    5. Jiaqi Liu & Jiayin Qi, 2022. "Online Public Rumor Engagement Model and Intervention Strategy in Major Public Health Emergencies: From the Perspective of Social Psychological Stress," IJERPH, MDPI, vol. 19(4), pages 1-22, February.
    6. Rui, Xiaobin & Meng, Fanrong & Wang, Zhixiao & Yuan, Guan & Du, Changjiang, 2018. "SPIR: The potential spreaders involved SIR model for information diffusion in social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 506(C), pages 254-269.
    7. Liu, Fangzhou & Zhang, Zengjie & Buss, Martin, 2019. "Robust optimal control of deterministic information epidemics with noisy transition rates," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 517(C), pages 577-587.
    8. Wang, Zhixiao & Rui, Xiaobin & Yuan, Guan & Cui, Jingjing & Hadzibeganovic, Tarik, 2021. "Endemic information-contagion outbreaks in complex networks with potential spreaders based recurrent-state transmission dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 573(C).
    9. Yi, Yinxue & Zhang, Zufan & Gan, Chenquan, 2018. "The effect of social tie on information diffusion in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 509(C), pages 783-794.
    10. Fu, Guiyuan & Chen, Feier & Liu, Jianguo & Han, Jingti, 2019. "Analysis of competitive information diffusion in a group-based population over social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 409-419.

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