Unfolding large-scale online collaborative human dynamics
Yilong Zha, Tao Zhou, Changsong Zhou

TL;DR
This paper analyzes large-scale online collaborative human activities, revealing universal patterns in update timing and proposing a model that captures individual, interaction, and growth dynamics, supported by empirical data.
Contribution
It introduces a comprehensive model of collaborative human dynamics that explains universal update patterns and provides analytical formulas validated by empirical data.
Findings
Universal double power-law distribution of update intervals
Model accurately reproduces empirical patterns
Highlights simplicity underlying complex human activities
Abstract
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 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 others with a power-law waiting time, and (iii) population growth due to increasing number of interacting individuals. This unfolding allows us to obtain analytical formula that is…
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