Unfolding large-scale online collaborative human dynamics

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 fully supported by the universal patterns in empirical data. Our modeling approaches reveal "simplicity" beyond complex interacting human activities.