Intellectual interchanges in the history of the massive online open-editing encyclopedia, Wikipedia

TL;DR

This study analyzes the entire history of Wikipedia to understand how collective editing dynamics develop over real time, revealing universal scaling laws and proposing a mechanistic model that explains editing patterns and editor participation behaviors.

Contribution

It introduces a comprehensive real-time analysis of Wikipedia's editing history and develops a mechanistic model capturing key editing dynamics and editor participation trends.

Findings

Universal scaling laws in editing activity over real time

Distinct growth patterns for different article types

Infrequently referred articles grow faster and high motivation reduces participation

Abstract

Wikipedia is a free Internet encyclopedia with an enormous amount of content. This encyclopedia is written by volunteers with various backgrounds in a collective fashion; anyone can access and edit most of the articles. This open-editing nature may give us prejudice that Wikipedia is an unstable and unreliable source; yet many studies suggest that Wikipedia is even more accurate and self-consistent than traditional encyclopedias. Scholars have attempted to understand such extraordinary credibility, but usually used the number of edits as the unit of time, without consideration of real time. In this work, we probe the formation of such collective intelligence through a systematic analysis using the entire history of 34,534,110 English Wikipedia articles, between 2001 and 2014. From this massive data set, we observe the universality of both timewise and lengthwise editing scales, which suggests that it is essential to consider the real-time dynamics. By considering real time, we find the existence of distinct growth patterns that are unobserved by utilizing the number of edits as the unit of time. To account for these results, we present a mechanistic model that adopts the article editing dynamics based on both editor-editor and editor-article interactions. The model successfully generates the key properties of real Wikipedia articles such as distinct types of articles for the editing patterns characterized by the interrelationship between the numbers of edits and editors, and the article size. In addition, the model indicates that infrequently referred articles tend to grow faster than frequently referred ones, and articles attracting a high motivation to edit counterintuitively reduce the number of participants. We suggest that this decay of participants eventually brings inequality among the editors, which will become more severe with time.