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Empirical analysis on the human dynamics of blogging behavior on GitHub

Author

Listed:
  • Yan, Deng-Cheng
  • Wei, Zong-Wen
  • Han, Xiao-Pu
  • Wang, Bing-Hong

Abstract

GitHub is a social collaborative coding platform on which software developers not only collaborate on codes but also share knowledge through blogs using GitHub Pages. In this article, we analyze the blogging behavior of software developers on GitHub Pages. The results show that both the commit number and the inter-event time of two consecutive blogging actions follow heavy-tailed distribution. We further observe a significant variety of activity among individual developers, and a strongly positive correlation between the activity and the power-law exponent of the inter-event time distribution. We also find a difference between the user behaviors of GitHub Pages and other online systems which is driven by the diversity of users and length of contents. In addition, our result shows an obvious difference between the majority of developers and elite developers in their burstiness property.

Suggested Citation

  • Yan, Deng-Cheng & Wei, Zong-Wen & Han, Xiao-Pu & Wang, Bing-Hong, 2017. "Empirical analysis on the human dynamics of blogging behavior on GitHub," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 465(C), pages 775-781.
    • Handle: RePEc:eee:phsmap:v:465:y:2017:i:c:p:775-781
    DOI: 10.1016/j.physa.2016.08.054
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    References listed on IDEAS

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    1. Ghoshal, Gourab & Holme, Petter, 2006. "Attractiveness and activity in Internet communities," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 364(C), pages 603-609.
    2. 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.
    3. Jeff Alstott & Ed Bullmore & Dietmar Plenz, 2014. "powerlaw: A Python Package for Analysis of Heavy-Tailed Distributions," PLOS ONE, Public Library of Science, vol. 9(1), pages 1-11, January.
    4. Zhao, Zhi-Dan & Zhou, Tao, 2012. "Empirical analysis of online human dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(11), pages 3308-3315.
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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. Rashidisabet, Homa & Ajilore, Olusola & Leow, Alex & Demos, Alexander P., 2022. "Revisiting power-law estimation with applications to real-world human typing dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 599(C).
    3. Pan, Jun-Shan & Li, Yuan-Qi & Hu, Han-Ping & Hu, Yong, 2021. "Modeling collective behavior of posting microblogs by stochastic differential equation with jump," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 578(C).
    4. Li, Kai & Lv, Tianyang & Shen, Huawei & Qiao, Lisheng & Chen, Enhong & Cheng, Xueqi & Sun, Zhi, 2020. "An empirical analysis on the behavioral differentia of the “Elite-Civilian” users in Sina microblog," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 539(C).
    5. Guo, Shengyu & Zhang, Pan & Ding, Lieyun, 2019. "Time-statistical laws of workers’ unsafe behavior in the construction industry: A case study," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 515(C), pages 419-429.
    6. Zhang, Sheng-Tai & Yuan, Hao-Yu & Duan, Ling-Li, 2020. "Analysis of human behavior statistics law based on WeChat Moment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 540(C).

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