Conference paper
Estimating Gender Completeness in Wikipedia
ACIS 2024 Proceedings, pp.1-13
Australasian Conference on Information Systems (ACIS), 34th (Canberra, Australia, 04-Dec-2024–06-Dec-2024)
Australasian Association for Information Systems (AAIS)
2024
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.
Details
- Title
- Estimating Gender Completeness in Wikipedia
- Authors
- Hrishi Patel - The University of QueenslandTianwa Chen - The University of QueenslandIvano Bongiovanni - The University of QueenslandGianluca Demartini - The University of Queensland
- Publication details
- ACIS 2024 Proceedings, pp.1-13
- Conference details
- Australasian Conference on Information Systems (ACIS), 34th (Canberra, Australia, 04-Dec-2024–06-Dec-2024)
- Publisher
- Australasian Association for Information Systems (AAIS)
- Date published
- 2024
- Copyright note
- © 2024 Hrishi Patel, Tianwa Chen, Ivano Bongiovanni, Gianluca Demartini. This is an open-access article licensed under a Creative Commons Attribution-Non-Commercial 4.0 International License, which permits non commercial use, distribution, and reproduction in any medium, provided the original author and ACIS are credited.
- Grant note
- This research is supported by the Wikimedia Foundation’s Research Fund under Grant No. G-RS-2303 12081.
- Organisation Unit
- School of Science, Technology and Engineering
- Language
- English
- Record Identifier
- 991246799502621
- Output Type
- Conference paper
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