Demographic disparity in Wikipedia coverage: a global perspective

Abstract Despite decades-long efforts to increase diversity, underrepresented social groups remain small minorities in many fields. Here, we ask whether disparities in global recognition exist for traditionally underrepresented demographic groups. We investigate whether a notable person’s demographic attributes are associated with their global recognition, considering both the global availability of public information about the person’s life and the consistency of such information. To track bibliographical information about notable people, we study Wikipedia, one of the most accessible knowledge bases on the Web. Using more than 1 million biographical articles from Wikipedia over ten years across the 12 largest language editions of Wikipedia, we study global gender and citizenship disparities in Wikipedia coverage. We measure global coverage in several ways, including the number of languages in which a person appears, the length of a person’s articles, and the global consensus about the person, which measures content similarity in the person’s articles across languages. We find that while females are broadly well-represented in terms of coverage in multiple languages starting from 2015, the quantity of the content of their articles and global consensus disparities persist consistently over time from 2010 to 2020. Additionally, some traditionally underrepresented nationalities are still covered less than their majority counterparts. Also, we observe an improvement on average in coverage while finding a persistent gender disparity in a specific domain, the global appearance of Olympic medal winners.

Authors

Institutions

Publication Details

Journal
EPJ Data Science
Published
2025-02-21
DOI
https://doi.org/10.1140/epjds/s13688-025-00530-4
Primary Topic
Wikis in Education and Collaboration
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Demographic disparity in Wikipedia coverage: a global perspective

Paramveer S. Dhillon, Daniel M. Romero, Tianyi Li, Xianglong Li et al.
EPJ Data Science
Wikis in Education and Collaboration
article

Demographic disparity in Wikipedia coverage: a global perspective

Paramveer S. Dhillon, Daniel M. Romero, Tianyi Li, Xianglong Li, Yulin Yu
article en

Abstract

Abstract Despite decades-long efforts to increase diversity, underrepresented social groups remain small minorities in many fields. Here, we ask whether disparities in global recognition exist for traditionally underrepresented demographic groups. We investigate whether a notable person’s demographic attributes are associated with their global recognition, considering both the global availability of public information about the person’s life and the consistency of such information. To track bibliographical information about notable people, we study Wikipedia, one of the most accessible knowledge bases on the Web. Using more than 1 million biographical articles from Wikipedia over ten years across the 12 largest language editions of Wikipedia, we study global gender and citizenship disparities in Wikipedia coverage. We measure global coverage in several ways, including the number of languages in which a person appears, the length of a person’s articles, and the global consensus about the person, which measures content similarity in the person’s articles across languages. We find that while females are broadly well-represented in terms of coverage in multiple languages starting from 2015, the quantity of the content of their articles and global consensus disparities persist consistently over time from 2010 to 2020. Additionally, some traditionally underrepresented nationalities are still covered less than their majority counterparts. Also, we observe an improvement on average in coverage while finding a persistent gender disparity in a specific domain, the global appearance of Olympic medal winners.

EPJ Data ScienceVol. 14(1)
University of Michigan (US)
University of Michigan
Openalex Percentile: Top 97%
Wikis in Education and Collaboration
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.