Measuring Gender Inequality in Wikipedia

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

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Measuring Gender Inequality in Wikipedia

  1. 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. 2. Who edits Wikipedia? 2
  3. 3. (i) How are notable men and women presented in Wikipedia? (ii) How are professions described on Wikipedia? 3
  4. 4. Notable Men/Women 4k individuals (3% women) 11k individuals (13% women) 110k individuals (11% women)
  5. 5. Are both genders covered equally?
  6. 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. 7 Angela Merkel Fritz Kuhn Global Notability (Internal Proxy)
  8. 8. Google Trends 8 Angela Merkel
  9. 9. 9 Fritz Kuhn
  10. 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. 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. 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. 13. How are they depicted? 13
  14. 14. 14 After 1900Before 1900
  15. 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. 16. Structural Differences 16
  17. 17. 17 Hyperlink Network
  18. 18. Men are more central
  19. 19. Men are better connected The k-core is the largest subnetwork comprising only nodes of degree at least k.
  20. 20. 20
  21. 21. 21
  22. 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. 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. 24. Coverage 24 0% 50% 100% Masculine Feminine Neutral Page No Page Redirect
  25. 25. 25https://de.wikipedia.org/wiki/Journalist
  26. 26. Images 26
  27. 27. Relation to Offline Statistics 27
  28. 28. Text 28 Male Bias Female Bias
  29. 29. Relation to Offline Statistics 29
  30. 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. 31. Joint work with 31 Markus Strohmaier Fabian FlöckOlga Zagovora David Garcia Mohsen Jadidi Eduardo Graells Garrido Fil Menczer
  32. 32. Questions?

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