Unfolding large-scale online collaborative human dynamics

Yilong Zha*, Tao Zhou, Changsong ZHOU

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

32 Citations (Scopus)


Large-scale interacting human activities underlie all social and economic phenomena, but quantitative understanding of regular patterns and mechanism is very challenging and still rare. Self-organized online collaborative activities with a precise record of event timing provide unprecedented opportunity. Our empirical analysis of the history of millions of updates in Wikipedia shows a universal double-power-law distribution of time intervals between consecutive updates of an article. We then propose a generic model to unfold collaborative human activities into three modules: (i) individual behavior characterized by Poissonian initiation of an action, (ii) human interaction captured by a cascading response to previous actions with a power-law waiting time, and (iii) population growth due to the increasing number of interacting individuals. This unfolding allows us to obtain an analytical formula that is fully supported by the universal patterns in empirical data. Our modeling approaches reveal "simplicity" beyond complex interacting human activities.

Original languageEnglish
Pages (from-to)14627-14632
Number of pages6
JournalProceedings of the National Academy of Sciences of the United States of America
Issue number51
Publication statusPublished - 20 Dec 2016

Scopus Subject Areas

  • General

User-Defined Keywords

  • Double power law
  • Human dynamics
  • Multibranching
  • Online collaboration


Dive into the research topics of 'Unfolding large-scale online collaborative human dynamics'. Together they form a unique fingerprint.

Cite this