This document analyzes gender inequalities in Wikipedia by measuring how notable men and women are presented and how professions are described. It finds that while coverage of notable men and women is generally good compared to external sources, women local heroes are covered less than expected. It also finds linguistic bias and structural differences in how men and women are depicted. Regarding professions, it finds gender-neutral descriptions are rare, professions dominated by women often refer mainly to men, and gender differences exist in descriptions of notable men and women in those fields. It concludes automatic tools and guidelines are needed to support editors in addressing some of these inequalities.

1. Measuring Gender Inequalities
in Wikipedia
Claudia Wagner
Computational Social Science @
GESIS – Leibniz Institute for the Social Sciences, Germany
Web-Science @
University of Koblenz-Landau, Germany

2. Who edits Wikipedia?
2

3. (i) How are notable men and women
presented in Wikipedia?
(ii) How are professions described on
Wikipedia?
3

4. Notable Men/Women
4k individuals (3% women)
11k individuals (13% women)
110k individuals (11% women)

5. Are both genders covered equally?

6. • Hypothesis:
– If Wikipedia functions as a glass ceiling then
the women who are covered will be more
notable. Large gender gap for local heroes,
less gap for superstars.
• But how to assess notability of people?
6
Who makes it into Wikipedia?

7. 7
Angela Merkel Fritz Kuhn
Global Notability (Internal Proxy)

8. Google Trends
8
Angela Merkel

9. 9
Fritz Kuhn

10. • Negative Binomial Regression Models
– Outcome Variable:
• Number of language editions (internal notability)
– Dependent Variables:
• Gender, profession and birth decade
10
coef IRR P>|z| [95.0% Conf. Int.]
female 0.1186 1.13 0.000 0.111 0.126
birth decade -0.0096 0.99 -0.0096 -0.010 -0.009
…. … … … …

11. Local Heroes
• 45% of men and 40% of women are local
heroes.
– Born after 1900:
• 5 men for 1 women  16,7% (expected)
• 6 men for 1 women  14,3% (observed)
– Born before 1900:
• 12 men for 1 women  7,7% (expected)
• 13 men for 1 women  7,1 % (observed)
11

12. Interest via Google Search
• On average, women who are depicted in
Wikipedia are of interest in more regions
(IRR=1.555) and during more months (IRR=1.322)
than men
12

13. How are they depicted?
13

14. 14
After 1900Before 1900

15. Linguistic Bias
• Linguistic Intergroup Bias theory:
– We generalize positive aspects of people in our
ingroup
– We generalize negative aspects of people in our
outgroup
15
Maass A, Salvi D, Arcuri L, Semin GR (1989) Language use in intergroup contexts: the linguistic
intergroup bias. J Pers Soc Psychol 57(6):981-993

16. Structural Differences
16

17. 17
Hyperlink Network

18. Men are more central

19. Men are better connected
The k-core is the largest subnetwork comprising only nodes of degree at least k.

20. 20

21. 21

22. Summary
• Coverage of notable men and women on Wikipedia is good (if
we compare with external lists)
• Women are on average more notable according to internal
and external criteria
• Less female local heroes than expected
• Topical difference and linguistic bias
• Structural differences
22

23. Professions in Wikipedia
• List of ~4200 German profession names
– Male, female and neutral name for the same
profession
– e.g. Feuerwehrmann, Feuerwehrfrau,
Feuerwehrpersonal, Feuerwehrfachkraft,
Feuerwehrmann/frau
• Mapping of profession names to Wikipedia
23

24. Coverage
24
0% 50% 100%
Masculine
Feminine
Neutral
Page
No Page
Redirect

25. 25https://de.wikipedia.org/wiki/Journalist

26. Images
26

27. Relation to Offline Statistics
27

28. Text
28
Male Bias
Female Bias

29. Relation to Offline Statistics
29

30. Conclusions
• Gender-neutral profession descriptions rarely exist on German
Wikipedia
• Also professions which are dominated by women nowadays refer
mainly to men
• Gender differences in the description of notable men and women
• Some inequalities simply reflect historic differences, others do not
– How to decide what is appropriate?
• Guidelines and automatic tools necessary to support editors
30

31. Joint work with
31
Markus Strohmaier
Fabian FlöckOlga Zagovora David Garcia Mohsen Jadidi
Eduardo Graells Garrido Fil Menczer

32. Questions?