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Empirical analysis of online human dynamics

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  • Zhao, Zhi-Dan
  • Zhou, Tao

Abstract

Patterns of human activities have attracted increasing academic interests, since the quantitative understanding of human behavior is helpful to uncover the origins of many socioeconomic phenomena. This paper focuses on behaviors of Internet users. Six large-scale systems are studied in our experiments, including the movie-watching in Netflix and MovieLens, the transaction in Ebay, the bookmark-collecting in Delicious, and the posting in FreindFeed and Twitter. Empirical analysis reveals some common statistical features of online human behavior: (1) The total number of user’s actions, the user’s activity, and the interevent time all follow heavy-tailed distributions. (2) There exists a strongly positive correlation between user’s activity and the total number of user’s actions, and a significantly negative correlation between the user’s activity and the width of the interevent time distribution. We further study the rescaling method and show that this method could to some extent eliminate the different statistics among users caused by the different activities, yet the effectiveness depends on the data sets.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:phsmap:v:391:y:2012:i:11:p:3308-3315
    DOI: 10.1016/j.physa.2012.01.008
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    Citations

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    Cited by:

    1. 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).
    2. 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.
    3. Alexander Jones Gross & Dhiraj Murthy & Lav R. Varshney, 2017. "Pace of Life in Cities and the Emergence of Town Tweeters," SAGE Open, , vol. 7(4), pages 21582440177, December.
    4. Wang, Wenjun & Yuan, Ning & Pan, Lin & Jiao, Pengfei & Dai, Weidi & Xue, Guixiang & Liu, Dong, 2015. "Temporal patterns of emergency calls of a metropolitan city in China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 436(C), pages 846-855.
    5. 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).
    6. 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).
    7. 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.
    8. Pan, Junshan & Hu, Hanping & Liu, Ying, 2014. "Human behavior during Flash Crowd in web surfing," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 413(C), pages 212-219.
    9. Peng, Dan & Han, Xiao-Pu & Wei, Zong-Wen & Wang, Bing-Hong, 2015. "Punctuated equilibrium dynamics in human communications," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 436(C), pages 36-44.
    10. Lin, Zhenquan & Meng, Fan, 2018. "Empirical analysis on the runners’ velocity distribution in city marathons," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 533-541.

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