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Quantifying the Gap: A Case Study of Wikidata Gender Disparities

Authors:

Charles Chuankai Zhang,

Loren TerveenAuthors Info & Claims

OpenSym '21: Proceedings of the 17th International Symposium on Open Collaboration

Article No.: 6, Pages 1 - 12

https://doi.org/10.1145/3479986.3479992

Published: 15 October 2021 Publication History

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Abstract

Much prior research has found gender bias in peer production systems like Wikipedia and OpenStreetMap. This bias affects both women’s participation in these platforms and content about women on these platforms. We investigated the gender content gap in Wikidata, where less than 22% of items that represent people are about women. We asked: what is the source of this bias? Specifically, does it originate from the actions of Wikidata editors or from external factors; that is, does it simply reflect existing real world gender bias? We conducted a quantitative case study that found: (i) the most popular categories of people included in Wikidata represent male-dominant professions, such as American football; (ii) within a selected set of professions where we could obtain gender distribution data, Wikidata is no more biased than the real world: men and women are included at similar percentages, and the quality of items representing men and women also is similar. We provide possible explanations for our findings and implications for addressing the Wikidata content gap.

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Cited By

Kraft ASoulier E(2024)Knowledge-Enhanced Language Models Are Not Bias-Proof: Situated Knowledge and Epistemic Injustice in AIProceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency10.1145/3630106.3658981(1433-1445)Online publication date: 3-Jun-2024
https://dl.acm.org/doi/10.1145/3630106.3658981
Nesterov AHollink Lvan Ossenbruggen JChua TNgo CKa-Wei Lee RKumar RLauw H(2024)How Contentious Terms About People and Cultures are Used in Linked Open DataProceedings of the ACM Web Conference 202410.1145/3589334.3648140(4523-4533)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3648140
Centelles MFerran-Ferrer N(2024)Assessing knowledge organization systems from a gender perspective: Wikipedia taxonomy and Wikidata ontologiesJournal of Documentation10.1108/JD-11-2023-023080:7(124-147)Online publication date: 5-Apr-2024
https://doi.org/10.1108/JD-11-2023-0230
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cover image ACM Other conferences

OpenSym '21: Proceedings of the 17th International Symposium on Open Collaboration

September 2021

136 pages

ISBN:9781450385008

DOI:10.1145/3479986

Editors:
Gregorio Robles
Universidad Rey Juan Carlos, Spain
,
Javier Arroyo
Universidad Complutense de Madrid, Spain
,
Ann Barcomb
University of Calgary, Canada
,
Kuljit Kaur Chahal
Guru Nanak Dev University, India
,
Sulayman Sowe
Universität Bayreuth, Germany
,
Xiaofeng Wang
Free University of Bolzen-Bolzano, Italy

Copyright © 2021 ACM.

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New York, NY, United States

Publication History

Published: 15 October 2021

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OpenSym 2021

OpenSym 2021: 17th International Symposium on Open Collaboration

September 15 - 17, 2021

Online, Spain

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Cited By

Kraft ASoulier E(2024)Knowledge-Enhanced Language Models Are Not Bias-Proof: Situated Knowledge and Epistemic Injustice in AIProceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency10.1145/3630106.3658981(1433-1445)Online publication date: 3-Jun-2024
https://dl.acm.org/doi/10.1145/3630106.3658981
Nesterov AHollink Lvan Ossenbruggen JChua TNgo CKa-Wei Lee RKumar RLauw H(2024)How Contentious Terms About People and Cultures are Used in Linked Open DataProceedings of the ACM Web Conference 202410.1145/3589334.3648140(4523-4533)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3648140
Centelles MFerran-Ferrer N(2024)Assessing knowledge organization systems from a gender perspective: Wikipedia taxonomy and Wikidata ontologiesJournal of Documentation10.1108/JD-11-2023-023080:7(124-147)Online publication date: 5-Apr-2024
https://doi.org/10.1108/JD-11-2023-0230
Melis BFioravanti MPaolini CMetilli D(2024)How have you modelled my gender? Reconstructing the history of gender representation in WikidataInternet Histories10.1080/24701475.2024.2431798(1-17)Online publication date: 17-Dec-2024
https://doi.org/10.1080/24701475.2024.2431798
Ferran-Ferrer NBoté-Vericad JMinguillón J(2023)Wikipedia gender gap: a scoping reviewEl Profesional de la información10.3145/epi.2023.nov.17Online publication date: 16-Dec-2023
https://doi.org/10.3145/epi.2023.nov.17
Ferran-Ferrer NBoté-Vericad JMinguillón J(2023)Wikipedia gender gap: a scoping reviewEl Profesional de la información10.3145/10.3145/epi.2023.nov.17Online publication date: 16-Dec-2023
https://doi.org/10.3145/10.3145/epi.2023.nov.17
Conroy M(2023)Quantifying the Gap: The Gender Gap in French Writers’ WikidataJournal of Cultural Analytics10.22148/001c.740688:2Online publication date: 11-May-2023
https://doi.org/10.22148/001c.74068
Abián DMeroño-Peñuela ASimperl E(2022)An Analysis of Content Gaps Versus User Needs in the Wikidata Knowledge GraphThe Semantic Web – ISWC 202210.1007/978-3-031-19433-7_21(354-374)Online publication date: 23-Oct-2022
https://dl.acm.org/doi/10.1007/978-3-031-19433-7_21

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