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Exploring Wikipedia Gender Diversity Over Time $\unicode{x2013}$ The Wikipedia Gender Dashboard (WGD)

Yahya Yunus, Tianwa Chen, Gianluca Demartini

TL;DR

The paper tackles gender disparities in Wikipedia by constructing the Wikipedia Gender Dashboard (WGD) to visualize gender distribution and mean age across the class 'Person' and its subclasses over time. It combines data from DBpedia and Wikidata via SPARQL queries, followed by a rigorous preprocessing pipeline and a public Power BI-based dashboard embedded on a website. Key findings include that female articles represent about 17% of English Wikipedia, rising from 6.98% in 2001 to 23.24% in 2022, alongside a general decline in mean age across genders. The work provides a practical tool for editors to identify and address gaps in coverage, while acknowledging limitations in age inference and data generalizability, and suggesting future improvements like enhanced alive/deceased classification. Overall, WGD offers a data-driven foundation to support efforts toward gender equality in Wikipedia, enabling targeted content development across subclasses and time.

Abstract

The Wikipedia editors' community has been actively pursuing the intent of achieving gender equality. To that end, it is important to explore the historical evolution of underlying gender disparities in Wikipedia articles. This paper presents the Wikipedia Gender Dashboard (WGD), a tool designed to enable the interaction with gender distribution data, including the average age in every subclass of individuals (i.e. Astronauts, Politicians, etc.) over the years. Wikipedia APIs, DBpedia, and Wikidata endpoints were used to query the data to ensure persistent data collection. The WGD was then created with Microsoft Power BI before being embedded on a public website. The analysis of the data available in the WGD found that female articles only represent around 17% of English Wikipedia, but it has been growing steadily over the last 20 years. Meanwhile, the average age across genders decreased over time. WGD also shows that most subclasses of `Person' are male-dominated. Wikipedia editors can make use of WGD to locate areas with marginalized genders in Wikipedia, and increase their efforts to produce more content providing coverage for those genders to achieve better gender equality in Wikipedia.

Exploring Wikipedia Gender Diversity Over Time $\unicode{x2013}$ The Wikipedia Gender Dashboard (WGD)

TL;DR

The paper tackles gender disparities in Wikipedia by constructing the Wikipedia Gender Dashboard (WGD) to visualize gender distribution and mean age across the class 'Person' and its subclasses over time. It combines data from DBpedia and Wikidata via SPARQL queries, followed by a rigorous preprocessing pipeline and a public Power BI-based dashboard embedded on a website. Key findings include that female articles represent about 17% of English Wikipedia, rising from 6.98% in 2001 to 23.24% in 2022, alongside a general decline in mean age across genders. The work provides a practical tool for editors to identify and address gaps in coverage, while acknowledging limitations in age inference and data generalizability, and suggesting future improvements like enhanced alive/deceased classification. Overall, WGD offers a data-driven foundation to support efforts toward gender equality in Wikipedia, enabling targeted content development across subclasses and time.

Abstract

The Wikipedia editors' community has been actively pursuing the intent of achieving gender equality. To that end, it is important to explore the historical evolution of underlying gender disparities in Wikipedia articles. This paper presents the Wikipedia Gender Dashboard (WGD), a tool designed to enable the interaction with gender distribution data, including the average age in every subclass of individuals (i.e. Astronauts, Politicians, etc.) over the years. Wikipedia APIs, DBpedia, and Wikidata endpoints were used to query the data to ensure persistent data collection. The WGD was then created with Microsoft Power BI before being embedded on a public website. The analysis of the data available in the WGD found that female articles only represent around 17% of English Wikipedia, but it has been growing steadily over the last 20 years. Meanwhile, the average age across genders decreased over time. WGD also shows that most subclasses of `Person' are male-dominated. Wikipedia editors can make use of WGD to locate areas with marginalized genders in Wikipedia, and increase their efforts to produce more content providing coverage for those genders to achieve better gender equality in Wikipedia.
Paper Structure (14 sections, 5 figures)

This paper contains 14 sections, 5 figures.

Table of Contents

  1. Introduction
  2. System Design
  3. Data Collection
  4. Data Preprocessing
  5. Removing Duplicates
  6. Handling Incorrect Values
  7. Handling Missing Values
  8. Dashboard Creation and Exploratory Data Analysis
  9. Website Development
  10. Demonstration
  11. Comparative Analysis of Male and Female
  12. Gender and Average Age of Wikipedia Articles Published over the Years
  13. Gender and Average Age Grouped By Subclasses
  14. Other Genders

Figures (5)

  • Figure 1: Top-down pipeline describing the system design.
  • Figure 2: Main dashboard showing the distributions of female articles over the whole dataset.
  • Figure 3: Distribution in the Beauty Queen subclass.
  • Figure 4: Distribution in the Youtuber subclass.
  • Figure 5: 'Other Genders' dashboard that visualizes the instances having gender other than male or female.