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Revealing the relationship of topics popularity and bursty human activity patterns in social temporal networks

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  • Wu, Lianren
  • Qi, Jiayin
  • Shi, Nan
  • Li, Jinjie
  • Yan, Qiang

Abstract

In social networks, how human activity patterns affect the popularity of topics has always been the focus of research. In this paper, a quantitative temporal analysis of the dynamics of topics popularity in Sina Weibo system was provided. Firstly, the popularity time series of 1167 topics were clustered into four clusters by K-Spectral Centroid (K-SC) clustering algorithm. Secondly, for each cluster, we calculated the exponents of topic popularity decay distribution α and the exponents of inter-activity time distribution β, respectively. Two interesting results were found: one is that the peak fraction F of topics popularity positively correlated with the topics popularity decay exponent α; the other is that bursty activity patterns in social network significantly affect topics popularity dynamics: there is a positive correlation between exponent α and exponent β. Finally, we proposed an extended SI (susceptible–infected) epidemic model with incorporate bursty human activity and verified the results by simulation.

Suggested Citation

  • Wu, Lianren & Qi, Jiayin & Shi, Nan & Li, Jinjie & Yan, Qiang, 2022. "Revealing the relationship of topics popularity and bursty human activity patterns in social temporal networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 588(C).
    • Handle: RePEc:eee:phsmap:v:588:y:2022:i:c:s0378437121008414
    DOI: 10.1016/j.physa.2021.126568
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    References listed on IDEAS

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    1. Qiang Yan & Lianren Wu, 2012. "Impact of Bursty Human Activity Patterns on the Popularity of Online Content," Discrete Dynamics in Nature and Society, Hindawi, vol. 2012, pages 1-11, September.
    2. Zhu, Liang & Wang, Youguo, 2018. "Rumor diffusion model with spatio-temporal diffusion and uncertainty of behavior decision in complex social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 502(C), pages 29-39.
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    4. Mohammad Akbarpour & Matthew O. Jackson, 2018. "Diffusion in networks and the virtue of burstiness," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 115(30), pages 6996-7004, July.
    5. Yan, Qiang & Wu, Lianren & Zheng, Lan, 2013. "Social network based microblog user behavior analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(7), pages 1712-1723.
    6. Yan, Qiang & Yi, Lanli & Wu, Lianren, 2012. "Human dynamic model co-driven by interest and social identity in the MicroBlog community," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(4), pages 1540-1545.
    7. Rabbani, Fereshteh & Khraisha, Tamer & Abbasi, Fatemeh & Jafari, Gholam Reza, 2021. "Memory effects on link formation in temporal networks: A fractional calculus approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 564(C).
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    1. Cui, Hao & Kertész, János, 2023. "“Born in Rome” or “Sleeping Beauty”: Emergence of hashtag popularity on the Chinese microblog Sina Weibo," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 619(C).

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