Presentation for Master Thesis defense , University of Koblenz-Landau, GESIS – Leibniz Institute for the Social Sciences

1. SLA 2014/15 Zagovora Olga 1Institute for Web Science and Technologies · University of Koblenz-Landau, Germany
Measuring gender inequalities of
German professions on Wikipedia
Olga Zagovora
Supervisors: Prof. Dr. Claudia Wagner
Dr. Fabian Flöck

2. Olga Zagovora Measuring gender inequalities of German professions on Wikipedia 2
Gender stereotypes
#RedrawTheBalance www.inspiringthefuture.org
Watch video from: https://www.youtube.com/watch?v=kJP1zPOfq_0

3. Olga Zagovora Measuring gender inequalities of German professions on Wikipedia 3
Profession article. Example:
Images: http:// de.wikipedia.org

4. Olga Zagovora Measuring gender inequalities of German professions on Wikipedia 4
Profession article. Example:
Images: http:// de.wikipedia.org

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1. Redirection analysis
• Classification of professions according to existing
Wiki articles [based on gender of profession title
Example: “Hebamme” or “Entbindungspfleger”
“Kaufmann”, “Kauffrau”, or “Kaufleute”]
2. Images analysis -> People on images
• Identification of people gender on images
• Distribution comparison of image categories
[based on persons’ gender]
3. Textual analysis -> Mentioned people in the text
• Mining of persons names from articles
• Distribution comparison of persons‘ gender
Method. Main dimensions

6. Olga Zagovora Measuring gender inequalities of German professions on Wikipedia 6
List of professions [based on profession list from
“Bundesagentur für Arbeit”] n=4457:
• "Lehrer": "Lehrerin",
• "Krankenpfleger": "Krankenschwester“,
• "Entbindungspfleger": "Hebamme",
• "PR-Fachkraft", "Fotomodell",
"Aufsichtsperson"
Wikipedia
• Articles about professions
• Images from profession articles
• Mentioned people in profession articles
Datasets

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Pages exist
1. Redirection analysis. Terminology

8. Olga Zagovora Measuring gender inequalities of German professions on Wikipedia 8
Pages exist
1. Redirection analysis. Terminology

9. Olga Zagovora Measuring gender inequalities of German professions on Wikipedia 9
Pages exist
1. Redirection analysis. Terminology
Neutral case
(no bias)

10. Olga Zagovora Measuring gender inequalities of German professions on Wikipedia 10
Pages exist
No page
1. Redirection analysis. Terminology
Neutral case
(no bias)

11. Olga Zagovora Measuring gender inequalities of German professions on Wikipedia 11
Pages exist
No page
1. Redirection analysis. Terminology
Neutral case
(no bias)

12. Olga Zagovora Measuring gender inequalities of German professions on Wikipedia 12
Pages exist
No page
1. Redirection analysis. Terminology
Neutral case
(no bias)

13. Olga Zagovora Measuring gender inequalities of German professions on Wikipedia 13
Pages exist
No page
1. Redirection analysis. Terminology
Neutral case
(no bias)
Male bias

14. Olga Zagovora Measuring gender inequalities of German professions on Wikipedia 14
Pages exist
No page
1. Redirection analysis. Terminology
Neutral case
(no bias)
Male bias
Female bias

15. Olga Zagovora Measuring gender inequalities of German professions on Wikipedia 15
Pages exist
No page
Redirects
1. Redirection analysis. Terminology
Neutral case
(no bias)
Male bias
Female bias
Male bias
Female bias

16. Olga Zagovora Measuring gender inequalities of German professions on Wikipedia 16
• most articles have male title
• most redirects are from female to male title
• 885 articles
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Male title
[n=4274]
Female
title
[n=4274]
Neutral
title
[n=183]
No page
Redirects
Wiki pages
Redirection analysis. Results
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Male title
[n=503]
Female title
[n=310]
Neutral title
[n=7]
Redirects to opposit gender Other redirects

17. Olga Zagovora Measuring gender inequalities of German professions on Wikipedia 17
• most articles have male title
• most redirects are from female to male title
• 885 articles
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Male title
[n=4274]
Female
title
[n=4274]
Neutral
title
[n=183]
No page
Redirects
Wiki pages
Redirection analysis. Results
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Male title
[n=503]
Female title
[n=310]
Neutral title
[n=7]
Redirects to opposit gender Other redirects
Redirection bias
groups:
Male: 812 professions
Female: 6 professions
Neutral: 55 professions

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Data: Google hits for profession names
Is it only Wikipedia specific phenomena?

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Data: Google hits for profession names
German speaking web is a male biased -> more sources for male than female profession names
Is it only Wikipedia specific phenomena?

20. Olga Zagovora Measuring gender inequalities of German professions on Wikipedia 20
Data: Google hits for profession names
𝑁𝑜𝑟𝑚𝑎𝑙𝑖𝑧𝑒𝑑_𝑑𝑖𝑓𝑓𝑒𝑟𝑒𝑛𝑐𝑒𝑖 =
𝐻𝑖𝑡𝑠 𝑚𝑎𝑙𝑒 𝑖
− 𝐻𝑖𝑡𝑠𝑓𝑒𝑚𝑎𝑙𝑒 𝑖
𝐻𝑖𝑡𝑠 𝑚𝑎𝑙𝑒 𝑖
+ 𝐻𝑖𝑡𝑠𝑓𝑒𝑚𝑎𝑙𝑒 𝑖
Dependent variable:
Model1: binary state of having male bias
Model2: binary state of having female bias
Does Wikipedia reflects the general bias on
the Web?
coef
Model1
Normalized Google difference 2.44***
(intercept) 2.41***
Model2
Normalized Google difference -5.93**
(intercept) -5.55**
Logistic regression models:

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Could we explain Wikipedia phenomena with
labor market statistics?
Bias groups z
male & neutral 0.83
male & female -3.32**
neutral & female -3.35**
RankSum tests1Data: German labor market statistics
¹ with two stage p-value correction of Benjamini-Hochberg
Logistic regression model:
Dependent variable:
binary state of having female bias
coef
percentage of women 0.36**
(intercept) -35.5**

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Data: Images from profession articles
CrowdFlower task
2. Images analysis

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Images analysis. Results
• 906 images from 885 Wikipedia articles
• 3 judges per photo -> response of majority [reliability of agreement κ = 0.75]
• Group images according to responses:
• „male“, „only male“, „mixed, but predominantly male” -> men in image
• „female“, „only female“, „mixed, but predominantly female” -> women in image
• „mixed, equal amount of men and women“
• „gender is not recognizable“
• „no person“

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Do Wikipedia images reflect labor market
statistics?

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Relation to labor market statistics
German labor market
statisticsImage results
Feature 1 Feature 2 Correlation The more images depicting /
The higher the percentage of …
number of images depicted
women
number of women in the
labor market
women are in the article, the more women
are working in the profession
number of images depicted
men
number of men in the labor
market
0.088 -
percentage of images
depicted men
percentage of men in the
labor market;
percentage of women in the
labor market
images depicting men is in the article, the
higher the percentage of men is in the labor
market;
images depicting men is in the article, the
lower the percentage of women is in the
labor market
percentage of images
depicted women
percentage of women in the
labor market
images depicting women is in the article,
the higher the percentage of women is in
the labor market
0.34***
-0.3***
0.15*
0.3***

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Data: Mentioned people from profession articles
• gender identification according to the first name (accuracy=0.97)
• 5085 (4272 men and 813 women) persons from 885 articles
• 411 articles with at least one person
Distribution of ratios of male names in an article
3. Textual analysis
mean 0.83
median 0.98
25% 0.8
75% 1.0
avg.number of
persons per article
10.4 m
1.9 f
Male bias
Female bias

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Do articles with male title have higher ratio of mentioned men than articles with
• neutral title?
• female title?
Do articles with neutral title have higher ratio of mentioned men than articles with
female title?
Is there an effect of gender of article title on
ratio of mentioned men?
male & female z=2.46*
median“male title” 1.0
median“female title” 0.65
Rank sum tests1
H0: Two sets of ratios of mentioned men are
drawn from the same distribution
Halt: Values in one set are more likely to be
larger than the values in the other sample
¹ with two stage p-value correction of Benjamini-Hochberg

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Relation to labor market statistics
German labor market
statistics
Feature 1 Feature 2 Correlation The higher the percentage of /
The more …
percentage of mentioned
men
percentage of women in
the labor market
mentioned men is in the article, the lower
the percentage of women is in the
profession
number of mentioned men number of people in the
labor market
men are mentioned in the article, the
fewer people are employed in the
profession
number of mentioned men number of men in the labor
market
men are mentioned in the article, the
fewer men are employed in the
profession
number of mentioned men number of women in the
labor market
men are mentioned in the article, the
fewer women are employed in the
profession
-0.27
-0.2
-0.15
-0.23
Mentioned people
in an article
***
**
***
***
No correlation between:
• number of mentioned women & number of women in labor market
• number of mentioned women & number of men in labor market

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• dbpedia -> birthDate
• divide people on those which were born before&after 1960
before 1960 after 1960
• Negative correlation remains between ratio of mentioned men &
percentage of women in labor market
Is there an effect of history on number of
mentioned men
cor
# mentioned men amount of people in
labor market
# mentioned men amount of men in labor
market
# mentioned men amount of women in
labor market
- 0.19**
-0.15*
- 0.20**
cor
# mentioned men amount of people in
labor market
-0.12*
# mentioned men amount of men in labor
market
-0.12
# mentioned men amount of women in
labor market
-0.11

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• Male bias over all dimensions:
• redirections
• images
• mentioned people
• High female bias for some professions
• Examples: “Model”(mentioned people),
“Hebamme”(images)
Summary

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Why does the male bias exist on Wikipedia?
• male editors
• implicit stereotypes of each individual
• male bias over other media (including Search engines aka Google)
What can be done to reduce it?
• attraction of more female editors
• development of Wikipedia equality rules
• warning editors before acceptance of revision
• profession equality lessons for kids
Future directions:
• cross-language analysis of gender inequalities for different Wiki editions
• timestamp analysis of revisions
• software tool for Wikipedia editors
Discussion & Outlook

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Questions?
zagovora@uni-koblenz.de

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Heinrich Heine
Fotografen beim Fußball © Ralf Roletschek /CC-BY-SA-3.0/ http://creativecommons.org/licenses/by-sa/3.0
Fotojournalisten bei der Fußball-Europameisterschaft 2008 © Arne Müseler / arne-mueseler.de / CC-BY-SA-3.0 / https://creativecommons.org/licenses/by-sa/3.0/de/deed.de
Lothar Loewe bei einem Vortrag im Juli 2009 © Bücherhexe /CC-BY-SA-3.0 / http://creativecommons.org/licenses/by-sa/3.0
1. Jugendolympiade 2012 Innsbruck © Ralf Roletschek / CC-BY-SA-3.0 at / http://creativecommons.org/licenses/by-sa/3.0/at/deed.en
Reporter Heinz abel (PHOENIX) im Gespräch mit Peter Fahrenholz © André Zahn/ CC-BY-SA-2.0 de / http://creativecommons.org/licenses/by-sa/2.0/de/deed.en
Bob Woodward, assistant managing editor © Jim Wallace (Smithsonian Institution) / CC-BY-2.0 / http://creativecommons.org/licenses/by/2.0
Journalisten bei der Fußball-Europameisterschaft 2008 © Arne Müseler / arne-mueseler.de / CC-BY-SA-3.0 / https://creativecommons.org/licenses/by-sa/3.0/de/deed.de
Oriana Fallaci in Tehran 1979
Oprah Winfrey at the Hotel Bel Air in Los Angeles © Alan Light / CC-BY-2.0 / http://creativecommons.org/licenses/by/2.0
Images

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License
Measuring Gender Inequalities of German Professions on Wikipedia by Olga Zagovora is licensed under a
Creative Commons Attribution-NonCommercial 4.0 International License.
Based on a work at https://arxiv.org/abs/1702.00829.