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Submissions/Wikipedia Gender Inequality Index: Analysing Who We Write About

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This is an accepted submission for Wikimania 2015.

Submission no.
2144
Title of the submission

Wikipedia Gender Inequality Index: Analysing Who We Write About

Type of submission (discussion, hot seat, panel, presentation, tutorial, workshop)

Presentation

Author of the submission
  • Max Klein
  • Piotr Konieczny
E-mail address

isalix@gmail.com

Username
Country of origin
  • U.K.
  • Poland
Affiliation, if any (organisation, company etc.)
  • Indepedent Researcher and
  • Hanyang University
Personal homepage or blog
Abstract (at least 300 words to describe your proposal)
Tracking female ratio over time and by culture.

In addressing gender gaps worldwide inequality indices are valuable tools that allow the problem to be measured. The United Nations and World Economic Forum release famous examples of these indices each year, but an index based on Wikipedia could provide a fresh approach to the measuring gender gaps.

Wikipedias (all of them taken together), even with all their biases, provide a constantly updated view of the world. Combined with the rise of Wikidata, it is now feasible to easily ask and combine questions about the people the encyclopedia covers. Most obviously, we have data about their gender, their dates birth, their citizenship, and the languages of the Wikipedias that describe them. Putting this all together we made "WIGI", the Wikipedia Gender Inequality Index, to measure world-wide bias, which can stand alongside other traditional inequality measures.

Celebritie ratios by language, decade, and gender.

In particular several different views of Wikipedias are presented. At first we might ask for some summary statistics, like the ratio of female articles among all biographies? 16%, on par with other Encyclopedias. From there we move on to looking the gender ratios by date of birth. Through the use of Iglehart-Welzel cultural clusters, we show that gender inequality can be analyzed with regards to world’s cultures. Another way we can view the data is to look at differences in bias between different Wikipedia languages. Out of our analysis came the result that Confucian and South Asian cultures appear to have higher than avearge female representation. Finally, we test a "celebrity hypothesis" that theses cultures are more attentive to celebrities.

In sum, we show how Wikimedia's data-renaissance can be used to provide an a series of inequality indices, and how these measures can be used on a global scale, and what they mean in addressing Wikipedia's editor gender gap.

Track
  • WikiCulture & Community
Length of session (if other than 30 minutes, specify how long)
30 minutes
Will you attend Wikimania if your submission is not accepted?
Yes
Slides or further information (optional)
Blog Post Article pre-print Open Science Notebook
Special requests


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  1. Daniel Mietchen (talk) 00:31, 27 February 2015 (UTC)[reply]
  2. Sannita (talk) 23:12, 27 February 2015 (UTC)[reply]
  3. I have been reading the research. Blue Rasberry (talk) 01:10, 1 March 2015 (UTC)[reply]
  4. Wotancito (talk) 08:02, 1 March 2015 (UTC)[reply]
  5. --Atropine (talk) 23:49, 7 March 2015 (UTC)[reply]
  6. CT Cooper · talk 23:52, 10 March 2015 (UTC)[reply]
  7. Ovedc (talk) 08:23, 12 May 2015 (UTC)[reply]
  8. Melina Masnatta (WMAR) (talk) 09:42, 14 July 2015 (UTC)[reply]
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