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A novel rumor diffusion model considering the effect of truth in online social media

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  • Ling Sun

    (School of Communication and Information Engineering, Beijing Jiaotong University, Beijing, 100044, P. R. China;
    Key Laboratory of Communication and Information Systems, Beijing Municipal Commission of Education, Beijing Jiaotong University, Beijing, 100044, P. R. China)

  • Yun Liu

    (School of Communication and Information Engineering, Beijing Jiaotong University, Beijing, 100044, P. R. China;
    Key Laboratory of Communication and Information Systems, Beijing Municipal Commission of Education, Beijing Jiaotong University, Beijing, 100044, P. R. China)

  • Qing-An Zeng

    (Department of Computer Systems Technology, North Carolina Agricultural Technical State, University Greenboro, NC, 27411, USA)

  • Fei Xiong

    (School of Communication and Information Engineering, Beijing Jiaotong University, Beijing, 100044, P. R. China;
    Key Laboratory of Communication and Information Systems, Beijing Municipal Commission of Education, Beijing Jiaotong University, Beijing, 100044, P. R. China)

Abstract

In this paper, we propose a model to investigate how truth affects rumor diffusion in online social media. Our model reveals a relation between rumor and truth — namely, when a rumor is diffusing, the truth about the rumor also diffuses with it. Two patterns of the agents used to identify rumor, self-identification and passive learning are taken into account. Combining theoretical proof and simulation analysis, we find that the threshold value of rumor diffusion is negatively correlated to the connectivity between nodes in the network and the probability β of agents knowing truth. Increasing β can reduce the maximum density of the rumor spreaders and slow down the generation speed of new rumor spreaders. On the other hand, we conclude that the best rumor diffusion strategy must balance the probability of forwarding rumor and the probability of agents losing interest in the rumor. High spread rate λ of rumor would lead to a surge in truth dissemination which will greatly limit the diffusion of rumor. Furthermore, in the case of unknown λ, increasing β can effectively reduce the maximum proportion of agents who do not know the truth, but cannot narrow the rumor diffusion range in a certain interval of β.

Suggested Citation

  • Ling Sun & Yun Liu & Qing-An Zeng & Fei Xiong, 2015. "A novel rumor diffusion model considering the effect of truth in online social media," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 26(07), pages 1-20.
    • Handle: RePEc:wsi:ijmpcx:v:26:y:2015:i:07:n:s0129183115500801
    DOI: 10.1142/S0129183115500801
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    Cited by:

    1. Liu, Chuang & Zhou, Nan & Zhan, Xiu-Xiu & Sun, Gui-Quan & Zhang, Zi-Ke, 2020. "Markov-based solution for information diffusion on adaptive social networks," Applied Mathematics and Computation, Elsevier, vol. 380(C).
    2. 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.

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