invited talk at wiki workshop at ICWSM and NLP+CSS workshop at WebScience

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?