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Characterizing and Modeling the Dynamics of Activity and Popularity

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  • Peng Zhang
  • Menghui Li
  • Liang Gao
  • Ying Fan
  • Zengru Di

Abstract

Social media, regarded as two-layer networks consisting of users and items, turn out to be the most important channels for access to massive information in the era of Web 2.0. The dynamics of human activity and item popularity is a crucial issue in social media networks. In this paper, by analyzing the growth of user activity and item popularity in four empirical social media networks, i.e., Amazon, Flickr, Delicious and Wikipedia, it is found that cross links between users and items are more likely to be created by active users and to be acquired by popular items, where user activity and item popularity are measured by the number of cross links associated with users and items. This indicates that users generally trace popular items, overall. However, it is found that the inactive users more severely trace popular items than the active users. Inspired by empirical analysis, we propose an evolving model for such networks, in which the evolution is driven only by two-step random walk. Numerical experiments verified that the model can qualitatively reproduce the distributions of user activity and item popularity observed in empirical networks. These results might shed light on the understandings of micro dynamics of activity and popularity in social media networks.

Suggested Citation

  • Peng Zhang & Menghui Li & Liang Gao & Ying Fan & Zengru Di, 2014. "Characterizing and Modeling the Dynamics of Activity and Popularity," PLOS ONE, Public Library of Science, vol. 9(2), pages 1-9, February.
    • Handle: RePEc:plo:pone00:0089192
    DOI: 10.1371/journal.pone.0089192
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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. Kaplan, Andreas M. & Haenlein, Michael, 2010. "Users of the world, unite! The challenges and opportunities of Social Media," Business Horizons, Elsevier, vol. 53(1), pages 59-68, January.
    3. Young-Ho Eom & Santo Fortunato, 2011. "Characterizing and Modeling Citation Dynamics," PLOS ONE, Public Library of Science, vol. 6(9), pages 1-7, September.
    4. Kietzmann, Jan H. & Hermkens, Kristopher & McCarthy, Ian P. & Silvestre, Bruno S., 2011. "Social media? Get serious! Understanding the functional building blocks of social media," Business Horizons, Elsevier, vol. 54(3), pages 241-251, May.
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    Cited by:

    1. Sun, Zhi & Peng, Qinke & Lv, Jia & Zhong, Tao, 2017. "Analyzing the posting behaviors in news forums with incremental inter-event time," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 479(C), pages 203-212.
    2. Jürgen Lerner & Alessandro Lomi, 2018. "Knowledge categorization affects popularity and quality of Wikipedia articles," PLOS ONE, Public Library of Science, vol. 13(1), pages 1-22, January.
    3. Li, Qian & Song, Chenguang & Wu, Bin & Xiao, Yunpeng & Wang, Bai, 2018. "Social hotspot propagation dynamics model based on heterogeneous mean field and evolutionary games," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 508(C), pages 324-341.
    4. Li, Menghui & Yang, Liying & Zhang, Huina & Shen, Zhesi & Wu, Chensheng & Wu, Jinshan, 2017. "Do mathematicians, economists and biomedical scientists trace large topics more strongly than physicists?," Journal of Informetrics, Elsevier, vol. 11(2), pages 598-607.

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