Title: Interpolating Quality Dynamics in Wikipedia and Demonstrating the Keilana Effect

Author: Aaron Halfaker:Wikimedia Foundation

Abstract: For open, volunteer generated content like Wikipedia, quality is a prominent concern. To measure Wikipedia’s quality, researchers have historically relied on expert evaluation or assessments of article quality by Wikipedians themselves. While both of these methods have proven effective for answering many questions about Wikipedia’s quality and processes, they are both problematic: expert evaluation is expensive and Wikipedian quality assessments are sporadic and unpredictable. Studies that explore Wikipedia’s quality level or the processes that result in quality improvements have only examined small snapshots of Wikipedia and often rely on complex propensity models to deal with the unpredictable nature of Wikipedians’ own assessments. In this paper, I describe a method for measuring article quality in Wikipedia historically and at a finer granularity than was previously possible. I use this method to demonstrate an important coverage dynamic in Wikipedia (specifically, articles about women scientists) and offer this method, dataset, and open API to the research community studying Wikipedia quality dynamics.

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