Gender and country biases in Wikipedia citations to scholarly publications

Xiang Zheng, Jiajing Chen, Erjia Yan, Chaoqun Ni

Journal of the Association for Information Science and Technology · 2022 · 19 citations · 46 references

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TL;DR

Ensuring Wikipedia cites scholarly publications based on quality and relevancy without biases is critical to credible and fair knowledge dissemination. The study investigates gender‑ and country‑based biases in Wikipedia citation practices and recommends guidelines to ensure equitable citation of scholarly works. The authors used linked data from the Web of Science and a Wikipedia citation dataset to examine gender‑ and country‑based citation biases. Publications by women and by authors from non‑Anglosphere countries are cited less by Wikipedia than expected, with the disparity varying across fields and being most pronounced in math‑intensive STEM, indicating a gender‑country intersectional bias.

Abstract

Abstract Ensuring Wikipedia cites scholarly publications based on quality and relevancy without biases is critical to credible and fair knowledge dissemination. We investigate gender‐ and country‐based biases in Wikipedia citation practices using linked data from the Web of Science and a Wikipedia citation dataset. Using coarsened exact matching, we show that publications by women are cited less by Wikipedia than expected, and publications by women are less likely to be cited than those by men. Scholarly publications by authors affiliated with non‐Anglosphere countries are also disadvantaged in getting cited by Wikipedia, compared with those by authors affiliated with Anglosphere countries. The level of gender‐ or country‐based inequalities varies by research field, and the gender‐country intersectional bias is prominent in math‐intensive STEM fields. To ensure the credibility and equality of knowledge presentation, Wikipedia should consider strategies and guidelines to cite scholarly publications independent of the gender and country of authors.

References

46