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Empirical and modeling studies of WeChat information dissemination

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  • Xu, Jinghong
  • Du, Zhitao
  • Guo, Jianchao
  • Fu, Xiangling
  • Zhang, Yuqiang
  • Wu, Ye

Abstract

WeChat is now one of the most popular social media and WeChat public account is a special “We Media” service. By gathering the pageviews of 620 articles from 140 WeChat public accounts, this study finds that there is a power law relation between the pageviews of an article and time. It means that the pageviews and the effect of an article are predictable within a short period of time. In this paper, a model of information dissemination within WeChat was established to define the new probability of a WeChat user’s willingness to share the same article. It shows that the attractiveness of information and the activeness of WeChat user have significant effects on the scope of information dissemination. These results can provide valuable theoretical references to the information dissemination mechanism of WeChat and to the governance of WeChat.

Suggested Citation

  • Xu, Jinghong & Du, Zhitao & Guo, Jianchao & Fu, Xiangling & Zhang, Yuqiang & Wu, Ye, 2018. "Empirical and modeling studies of WeChat information dissemination," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 1113-1120.
    • Handle: RePEc:eee:phsmap:v:512:y:2018:i:c:p:1113-1120
    DOI: 10.1016/j.physa.2018.08.056
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    References listed on IDEAS

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    1. Liu, Liang & Qu, Bo & Chen, Bin & Hanjalic, Alan & Wang, Huijuan, 2018. "Modelling of information diffusion on social networks with applications to WeChat," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 496(C), pages 318-329.
    2. Yang, Dingda & Liao, Xiangwen & Shen, Huawei & Cheng, Xueqi & Chen, Guolong, 2018. "Modeling the reemergence of information diffusion in social network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 1493-1500.
    3. Qu, Bo & Wang, Huiijuan, 2017. "SIS epidemic spreading with correlated heterogeneous infection rates," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 472(C), pages 13-24.
    4. Huo, Liang'an & Wang, Li & Song, Naixiang & Ma, Chenyang & He, Bing, 2017. "Rumor spreading model considering the activity of spreaders in the homogeneous network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 468(C), pages 855-865.
    5. Si-hua Chen & Wei He, 2014. "Study on Knowledge Propagation in Complex Networks Based on Preferences, Taking Wechat as Example," Abstract and Applied Analysis, Hindawi, vol. 2014, pages 1-11, July.
    6. Wu, Qingchu & Zhang, Fei, 2016. "Threshold conditions for SIS epidemic models on edge-weighted networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 453(C), pages 77-83.
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    Cited by:

    1. Cui, Yapeng & Ni, Shunjiang & Shen, Shifei & Wang, Zhiru, 2020. "Modeling the dynamics of information dissemination under disaster," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 537(C).

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