First Women, Second Sex: Gender Bias in Wikipedia
Not on the map. Why?
Abstract (arXiv)
Contributing to history has never been as easy as it is today. Anyone with access to the Web is able to play a part on Wikipedia, an open and free encyclopedia. Wikipedia, available in many languages, is one of the most visited websites in the world and arguably one of the primary sources of knowledge on the Web. However, not everyone is contributing to Wikipedia from a diversity point of view; several groups are severely underrepresented. One of those groups is women, who make up approximately 16% of the current contributor community, meaning that most of the content is written by men. In addition, although there are specific guidelines of verifiability, notability, and neutral point of view that must be adhered by Wikipedia content, these guidelines are supervised and enforced by men. In this paper, we propose that gender bias is not about participation and representation only, but also about characterization of women. We approach the analysis of gender bias by defining a methodology for comparing the characterizations of men and women in biographies in three aspects: meta-data, language, and network structure. Our results show that, indeed, there are differences in characterization and structure. Some of these differences are reflected from the off-line world documented by Wikipedia, but other differences can be attributed to gender bias in Wikipedia content. We contextualize these differences in feminist theory and discuss their implications for Wikipedia policy.
Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). First posted on arXiv 9 Feb 2015.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2021: 4; 2022: 3; 2023: 1; 2024: 2; 2025: 1.
11 papers.
- 2021The Web Is Your Oyster - Knowledge-Intensive NLP against a Very Large Web Corpuscited by 60
- 2022Whose Language Counts as High Quality? Measuring Language Ideologies in Text Data Selectioncited by 52
- 2022Datasheet for the Pilecited by 41
- 2021SynthBio: A Case Study in Human-AI Collaborative Curation of Text Datasetscited by 18
- 2022Generating Full Length Wikipedia Biographies: The Impact of Gender Bias on the Retrieval-Based Generation of Women Biographiescited by 7
- 2021Quantifying Gender Biases Towards Politicians on Redditcited by 5
- 2023How Prevalent is Gender Bias in ChatGPT? -- Exploring German and English ChatGPT Responsescited by 5
- 2024GECOBench: A Gender-Controlled Text Dataset and Benchmark for Quantifying Biases in Explanationscited by 5
- 2021Assessing gender bias in medical and scientific masked language models with StereoSetcited by 2
- 2025How Similar Are Grokipedia and Wikipedia? A Multi-Dimensional Textual and Structural Comparisoncited by 2
- 2024Social Science Is Necessary for Operationalizing Socially Responsible Foundation Modelscited by 1
- 2025How Similar Are Grokipedia and Wikipedia? A Multi-Dimensional Textual and Structural Comparison30 Oct 2025
- 2024Social Science Is Necessary for Operationalizing Socially Responsible Foundation Models20 Dec 2024
- 2024GECOBench: A Gender-Controlled Text Dataset and Benchmark for Quantifying Biases in Explanations17 Jun 2024
- 2023How Prevalent is Gender Bias in ChatGPT? -- Exploring German and English ChatGPT Responses21 Sep 2023
- 2022Generating Full Length Wikipedia Biographies: The Impact of Gender Bias on the Retrieval-Based Generation of Women Biographies12 Apr 2022
- 2022Whose Language Counts as High Quality? Measuring Language Ideologies in Text Data Selection25 Jan 2022
- 2022Datasheet for the Pile13 Jan 2022
- 2021Quantifying Gender Biases Towards Politicians on Reddit22 Dec 2021
- 2021The Web Is Your Oyster - Knowledge-Intensive NLP against a Very Large Web Corpus18 Dec 2021
- 2021Assessing gender bias in medical and scientific masked language models with StereoSet15 Nov 2021
- 2021SynthBio: A Case Study in Human-AI Collaborative Curation of Text Datasets11 Nov 2021