mlsanityData to 7 Oct 2026

Women Through the Glass Ceiling: Gender Asymmetries in Wikipedia

arXiv 1601.04890 ↗First posted 19 Jan 2016cs.SIcited by 17

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Abstract (arXiv)

Contributing to the writing of history has never been as easy as it is today thanks to Wikipedia, a community-created encyclopedia that aims to document the world's knowledge from a neutral point of view. Though everyone can participate it is well known that the editor community has a narrow diversity, with a majority of white male editors. While this participatory gender gap has been studied extensively in the literature, this work sets out to assess potential gender inequalities in Wikipedia articles along different dimensions: notability, topical focus, linguistic bias, structural properties, and meta-data presentation. We find that (i) women in Wikipedia are more notable than men, which we interpret as the outcome of a subtle glass ceiling effect; (ii) family-, gender-, and relationship-related topics are more present in biographies about women; (iii) linguistic bias manifests in Wikipedia since abstract terms tend to be used to describe positive aspects in the biographies of men and negative aspects in the biographies of women; and (iv) there are structural differences in terms of meta-data and hyperlinks, which have consequences for information-seeking activities. While some differences are expected, due to historical and social contexts, other differences are attributable to Wikipedia editors. The implications of such differences are discussed having Wikipedia contribution policies in mind. We hope that the present work will contribute to increased awareness about, first, gender issues in the content of Wikipedia, and second, the different levels on which gender biases can manifest on the Web.

Key facts. Cited by 17 papers in the map (data through 7 Oct 2026). First posted on arXiv 19 Jan 2016.

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Citing papers per year: 2021: 4; 2022: 3; 2023: 3; 2024: 3; 2025: 3; 2026: 1.

17 papers.

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  1. 2024Bias in Large Language Models: Origin, Evaluation, and Mitigationcited by 77
  2. 2021The Web Is Your Oyster - Knowledge-Intensive NLP against a Very Large Web Corpuscited by 60
  3. 2021Stereotype and Skew: Quantifying Gender Bias in Pre-trained and Fine-tuned Language Modelscited by 57
  4. 2022The Birth of Bias: A case study on the evolution of gender bias in an English language modelcited by 16
  5. 2022How Gender Debiasing Affects Internal Model Representations, and Why It Matterscited by 12
  6. 2023The Web Can Be Your Oyster for Improving Large Language Modelscited by 7
  7. 2021Face Verification with Challenging Imposters and Diversified Demographicscited by 5
  8. 2021Quantifying Gender Biases Towards Politicians on Redditcited by 5
  9. 2022Undesirable Biases in NLP: Addressing Challenges of Measurementcited by 4
  10. 2024What is the Role of Large Language Models in the Evolution of Astronomy Research?cited by 4
  11. 2025WikiGap: Promoting Epistemic Equity by Surfacing Knowledge Gaps Between English Wikipedia and other Language Editionscited by 3
  12. 2024White Men Lead, Black Women Help? Benchmarking and Mitigating Language Agency Social Biases in LLMscited by 1
5 more
  1. 2025Characterizing Knowledge Manipulation in a Russian Wikipedia Forkcited by 1
  2. 2023Effects of Locality and Rule Language on Explanations for Knowledge Graph Embeddingscited by 0
  3. 2023How-to Guides for Specific Audiences: A Corpus and Initial Findingscited by 0
  4. 2025Benchmarking Gender and Political Bias in Large Language Modelscited by 0
  5. 2026Cross-lingual Biography Enrichment via Claim Extraction and Alignmentcited by 0