Online communities are flourishing as social meeting web-spaces for users and peer community members. Different online communities require different levels of competence for participants to join, and scattered evidence suggests that the female gender and minorities can be overly under-represented. Additional anecdotal evidence suggests that women withdraw from unfriendly online communities. Due to the limited amount of empirical evidence on the matter, this paper provides a quantitative study of the phenomenon, in order to assess the representation and social impact of gender in online communities. This study positions itself within recent and focused international initiatives, launched by the European Commission in order to encourage women in the field of sciences and technology. Focusing on technical support networks around web design tools (e.g., Drupal and WordPress) and on questions & answers sites (e.g., StackOverflow), this paper unearths a spectrum of online communities, in which women participate to various degrees.

1. UC Davis – DECAL reading club
Gender, Representation
and Online Participation
A Quantitative Study
Bogdan Vasilescu,
Andrea Capiluppi,
Alexander Serebrenik

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6. 1) What
is your
gender?
2) What
do you do
on a Q&A
site?
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7. What is your gender?
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8. What is your gender?
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9. What is your gender?
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10. What is your gender?
Name +
Location =
Gender
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11. w35l3y ⇒ wesley
Lonzo ⇒ Alonzo
Name +
Location =
Gender
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12. Heuristics:
title + first h1
<title>Ben Kamens</title>
…
<h1>We&#8217;re willing
to be embarrassed about
what we
<em>haven&#8217;t</em>
done&#8230;</h1>
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Ben Kamens We’re willing to
be embarrassed about what we
haven’t done…
Stanford Named
Entity Tagger
<PERSON>Ben
Kamens</PERSON> We’re
willing to be embarrassed
about what we haven’t done…
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13. Quality of gender resolution: Survey
SelfAs inferred Total
identification M F ?
M
F
60
2
3 43
5 4
106
11
+ avatars,
other social
media sites
(manually)
SelfAs inferred Total
identification M F ?
M
90 3 13 106
F
2
9 0
11
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14. 2296
291
1557
3043
282
286
2879
328
135
sample
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15. 2296
291
1557
3043
282
286
2879
328
135
sample
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7-10% women as opposed to
1-5% for Open Source and
up to 28% for proprietary
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16. 2296
291
1557
3043
282
286
2879
328
135
sample
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7-10% on different mailing lists
more on “use technology”
less on “design technology”
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17. 2296
291
1557
3043
282
286
2879
328
135
sample
It is easy to remain anonymous on SO and
participants use this opportunity (37.5%)
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18. sample
No significant
differences in
#questions, #answers,
length of engagement
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Affects eng’t
for “design
tech.” lists

19. sample
Engage
Ask more
for longer
questions
No diff in #answers
Women can
contribute to SO
but choose not to!
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20. Why?
• [Gneezy, Niederle, Rustichini 2003]: women are less
effective in mixed-gender competitive environments
• [Niederle, Vesterlund 2007]: women shy away from
competition and men embrace it
⇒ To retain women we need different gamification
techniques
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21. Threats to validity
• Gender inference:
• Automated: Imprecise tooling
• Manual: Errare humanum est
• Gender swapping
• Images of other people as avatars
• Celebrities, children, porn stars…
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22. Future work…
• Roles: coders, translators, UI designers
• Similar to diff mailing lists in Drupal/WordPress
• Activity (commits) rather than discussion
• Output: code, bugs, …
• Confounding variables
• Culture/country: Malaysia – 50% of CS students
• Sexual orientation: IT might be attractive to lesbians for
the same reasons it repels heterosexual women [Landström
2007]
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23. Name +
Location =
Gender
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