ACIS 2024 Proceedings

Abstract

As the world's largest crowdsourced online encyclopedia, Wikipedia exemplifies how digital platforms can facilitate global knowledge exchange. Its extensive repository enhances public access to information and provides data that supports the development of Large Language Models. This paper addresses the persistent challenge of gender imbalance in Wikipedia’s content, a known challenge that the editor community is actively addressing. The aim of this paper is to provide the Wikipedia community with instruments to estimate the magnitude of the problem for different entity types (also known as classes) in Wikipedia. To this end, we apply class completeness estimation methods based on the gender attribute. Our results show not only which gender for different sub-classes of Person is more prevalent in Wikipedia, but also an idea of how complete the coverage is for different genders and sub-classes of Person. The proposed methods and results of this study offer valuable insights to inform and improve the editorial decision-making processes for the Wikipedia editor community.

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