WP:Clubhouse? An Exploration of Wikipedia’s Gender
Imbalance
Shyong (Tony) K. Lam1 , Anuradha Uduwage1 , Zhenhua Dong2 , Shilad Sen3 , David R.
Musicant4 , Loren Terveen1 , John Riedl1
1 GroupLens Research
Dept. of Computer Science and Engineering
University of Minnesota
Minneapolis, Minnesota

2 Dept. of Information Technical Science

Nankai University
Tianjin, China{lam,uduwage,terveen,riedl}@cs.umn.edu

dongzhh@mail.nankai.edu.cn

3 Math, Statistics, and Computer Science Dept.

4 Dept. of Computer Science

Macalester College
St. Paul, Minnesota

Carleton College
Northfield, Minnesota

ssen@macalester.edu

dmusican@carleton.edu

ABSTRACT
Wikipedia has rapidly become an invaluable destination for millions of information-seeking users. However, media reports suggest an important challenge: only a small fraction of Wikipedia’s
legion of volunteer editors are female. In the current work, we
present a scientific exploration of the gender imbalance in the English Wikipedia’s population of editors. We look at the nature of
the imbalance itself, its effects on the quality of the encyclopedia,
and several conflict-related factors that may be contributing to thegender gap. Our findings confirm the presence of a large gender
gap among editors and a corresponding gender-oriented disparity
in the content of Wikipedia’s articles. Further, we find evidence
hinting at a culture that may be resistant to female participation.

Categories and Subject Descriptors
H.3.4 [Information Systems]: Systems and Software—Information networks; H.5.3 [Information Systems]: Group and Organization Interfaces—computer-supported collaborative work

General Terms
Human Factors, Measurement

Keywords
Wikipedia, collaboration, gender gap, content coverage

1. INTRODUCTION
Over the past decade, Wikipedia has become a premier information resource on the web. Remarkably, it was not built by experts,

but by harnessing the collective effort of millions of volunteer editors. However, not all is well with Wikipedia. Researchers haveidentified and studied several factors that represent challenges for
Wikipedia, including increased vandalism [21], increased overhead
in resolving editor conflict and performing other coordination activities [13], and an overall stagnation in growth rate [24].
More recently, in a January 2011 New York Times article, Noam
Cohen described another challenge: a wide gender gap amongst
Wikipedia’s editors [7]. Cohen observes that just 13% of Wikipedia’s
contributors are female, according to a 2009 Wikimedia Foundation
survey. Furthermore, he suggests that this disparity has led to deficiencies in Wikipedia’s coverage of “female” topics, as evidenced
by a series of anecdotal examples (e.g., Wikipedia’s coverage of
topics like friendship bracelets or “Sex and the City” pales in comparison to that of toy soldiers or “The Sopranos”).
The Wikimedia Foundation has established a goal of increasing the female share in editors to 25% by 2015. While ambitious,
such a accomplishment is certainly not out of reach. In Unlocking
the Clubhouse: Women in Computing [17], Margolis and Fisher
describe a series of studies and educational reforms that helped
Carnegie Mellon University address a wide gender gap in their undergraduate Computer Science program. Over the course of five
years, female enrollment rose from 7% in 1995 to 42% in 2000.
In the current work, we present a quantitative exploration of thegender imbalance in English Wikipedia’s volunteer editor population. Cohen’s article presents a compelling argument, but we believe there is need for more rigorous analysis that expands on thereported survey results and anecdotal evidence. We believe ourwork represents a crucial next step in understanding the nature of
the gender gap and deciding what should be done to address it.

1.1
Permission to make digital or hard copies of all or part of this work forpersonal or classroom use is granted without fee provided that copies are
not made or distributed for profit or commercial advantage and that copies
bear this notice and the full citation on the first page. To copy otherwise, torepublish, to post on servers or to redistribute to lists, requires prior specificpermission and/or a fee.
WikiSym’11, October 3–5, 2011, Mountain View, California
Copyright 2011 ACM 978-1-4503-0909-7/11/10 ...$10.00.

Related Work

Research from the volunteering literature and technology adoption literature offer reasons not to expect a large gender gap among
Wikipedia’s editors. Taniguchi finds that females are more likely
to volunteer than males, and that females do more volunteer workthan males [26]. In [31], Wilson cites four underlying reasons forfemales’ increased volunteerism: they exhibit greater empathy andaltruism, they place more value in helping others, they perceive agender-specific norm that they should take care of others, and theyview volunteering as part of their “social life.” Overall, these findings suggest that females may be more likely to volunteer their timeto edit Wikipedia, though they may edit less if they lack a social
connection to the Wikipedia community.
The technology adoption literature suggests that females maylag behind males in adopting new technology. Broadly, Venkatesh
et al. find that females are less likely to adopt new technologies
than males, and that females are more heavily influenced by social
norms related to a technology and the perceived difficulty of the
new technology [29]. However, studies of gender differences in
adoption of the Internet and social media offer more encouraging
findings. Periodic Pew Research Center surveys of the general populace show that Internet usage between 2000 to 2004 was skewed
toward males, but that the gap has since dissolved [19]. Females are
now more likely than males to participate in some social media sites
such as Facebook or MySpace [28]. In addition, females are more
likely to tweet (10% of females, 7% of males), and teenage girls
are more likely to blog (25% of girls, 15% of boys) [23, 15]. Even
online gaming, which is traditionally seen as a male-dominated activity [34], shows signs of a sea change; market research surveys
indicate that females and males are on par with each other in onlinesocial gaming [12, 25].
Together, these observations suggest that Wikipedia, a community of volunteers collaborating to build an online encyclopedia,
ought to have a reasonable gender balance. However, multiple
studies have indicated an apparent disparity. Lim’s surveys of college students find that while all respondents had used Wikipedia,
females visited it less frequently and perceived it to be of lower
quality than males did [16]. A 2011 Pew Research survey finds
a small gender gap in readership (50% of female Internet users,and 56% of male ones) [35]. The Wikimedia Foundation commissioned a survey of Wikipedia users in 2009, and its results showa large gap among readers (75% male, 25% female), and an evenlarger gap among editors (87% male, 13% female) [10]. However,
because users self-selected to participate in the survey, the reportauthors acknowledge that it is “hard to evaluate whether the shares
we found in our survey are representative.” Furthermore, 75% of
the users who took the survey were using a non-English Wikipedia.
Thus, it is uncertain what the gender gap is in English Wikipedia.

potheses and research questions provisionally assume that this hypothesis will be supported.

1.2 Contributions

2.3

Our work seeks to explore more carefully the state of gender imbalance among the English Wikipedia’s volunteer editors. We extend existing research on Wikipedia and gender in three key ways.
First, we conduct a high-level study of gender and editing behavior
in order to measure and characterize the editor gender gap. Second,
we explore how the imbalance affects Wikipedia as a way of showing why it is an important issue to address. Finally, we analyze how
conflict-related behaviors such as reverts and blocks affect editorsof each gender to help understand why an imbalance might exist.

What gender differences exist in conflicts in Wikipedia, andhow do those differences relate to the gender gap?

2. RESEARCH QUESTIONS
We begin our exploration of Wikipedia’s gender gap by posing
three overarching research questions and nine hypotheses.

2.1 RQ1: Gap-Overall

H1a Gap-Exists: Wikipedia has a substantial editor gender gap.
Periodic surveys of the general populace indicate that there was amodest gender gap in Internet use in the early 2000s, but that it has
been shrinking steadily [19]. We hypothesize that a similar trend
has been taking place in Wikipedia.
H1b Gap-Shrinking: Wikipedia’s gender gap is shrinking.

2.2

RQ2: Gap-Matters

How is Wikipedia affected by the gender gap?
In this research question, we wish to explore whether the gender
gap is causing some parts of Wikipedia to receive less attention thanother parts. Our next hypotheses are inspired by Noam Cohen’s
New York Times article [7], which anecdotally demonstrates a large
disparity in the depth of coverage between “female” and “male”
topics in Wikipedia. We systematically study this phenomenon inlarge-scale data-driven ways that do not depend on anecdotal evidence and invocation of gender stereotypes to determine which
topics are “female” or “male.” Formally:
H2a Focus-Differences: Male and female editors focus on different content areas.
H2b F-Coverage-Worse: Coverage of topics with particular interest to females is inferior to topics with particular interest to males.
We further hypothesize that due to gender differences in extraversion, empathy, and altruism [9, 31], females will tend to be more active than males in social- or community-oriented areas of Wikipediathat offer increased interaction with other editors and opportunity
to build interpersonal relationships. If this hypothesis is supported,
addressing the gender gap might lead to a healthier community inwhich there are more resources available for community-oriented
tasks like helping new editors and organizing editor efforts.
H2c F-Social: Females are more likely to be involved in socialand community-oriented areas of Wikipedia.

RQ3: Gender-Conflict

In our final research question, we look at gender differences inconflict among Wikipedia’s editors in order to learn about how conflict might be contributing to the gender gap. Prior research findsthat conflict has been a growing problem for Wikipedia, consuming increasing amounts of editor effort [13]. Studies on gender andpersonality have shown that females tend to have more agreeable
and less aggressive personalities [9, 2], which suggests that they
may tend to avoid conflict if possible. Therefore, a possible explanation for the gender gap is that females may find conflict among
Wikipedia editors to be distasteful and unappealing, and may simply choose to not edit Wikipedia as a result. As a partial test of
this explanation, we hypothesize that Wikipedia’s existing female
editors tend to do their work in less controversial areas.

What is the extent of Wikipedia’s gender gap, and how has itchanged over time?

H3a F-Uncontentious: Females tend to avoid controversial orcontentious articles.

We are interested in measuring Wikipedia’s gender gap and determining whether the imbalance is growing or shrinking. Based on
the survey results presented in [10], we hypothesize that Wikipedia
does have a wide gender gap. Note that the remainder of our hy-

Females who do decide to edit Wikipedia may find it difficult
to make contributions that are accepted by the community. Drawing upon years of research in gender and computer-mediated communication, Herring finds that “gender differences in on-line com-munication tend to disfavor women” and that females who participate in mixed-gender online environments tend to be marginalized [11]. Furthermore, Herring notes that even if participant gender is not made salient, features of a participant’s discourse style
can often reveal gender information. Recent research has shownthat males and females have measurably different editing behaviors on Wikipedia [1]. Thus, we believe there may be a systemic
bias against females that cause their edits to be more likely to bereverted (undone) by another editor, particularly early on in their
Wikipedia tenure. Furthermore, since females may prefer to avoid
conflict, we believe they are more likely than males to lose interestand leave Wikipedia if their early contributions are reverted.
H3b F-Reverted-More: Female editors are more likely to havetheir early edits reverted.
H3c F-Reverted-Leave: Female editors are more likely to stop
editing and leave Wikipedia when being reverted as newcomers.
Another action used in Wikipedia conflict resolution is a block,which prevents the affected user from editing for a period of time.
Blocks are much less common than reverts, and their use is limited to cases of disruptive or anti-social behavior where other formsof conflict resolution have been ineffective. Research in multiple
domains indicates that males are more likely to violate rules [30,
11, 27] and that they are more aggressive, especially when provoked [2]. Thus, we hypothesize that females are less likely to
be blocked than males, and that conflicts that escalate to the point
where a block is required are not a contributor to the gender gap.
H3d F-Blocked-Less: Female editors are less likely to be blocked.

3. DATATo test our hypotheses and answer our research questions, we
perform a variety of quantitative statistical analyses on publiclyavailable English Wikipedia data. The majority of our data is either
from the January 2011 data dump1 or from the Wikipedia website
itself (collected during February and March 2011 via the public
API or screen-scraping). We also drew upon several other sourcesof data during our analyses, including data derived from older fulltext Wikipedia dumps released in January 2010 or January 2008.
Because each of these additional sources of data is specific to oneof our hypotheses, we will defer discussion about these other data
until the relevant section.

3.1 Editor Gender Data
A key piece of information we need for our analyses is editor
gender. In Wikipedia, there are several ways that an editor canpublicly disclose his or her gender:
1. They can specify whether they are male or female in their account’s preference settings. The gender setting is described
as being “used for gender-correct addressing by the software,”and is public information available from Wikipedia’s API.
2. They can place a gender userbox on their User page to openly
announce and display their gender in a de facto standard way.
3. They can mention their gender while describing themselveson their User page or during a discussion with fellow editors
in one of Wikipedia’s discussion areas.
The editor gender data used in our analysis includes users who
disclosed their gender via preference setting (#1) and a subset of
those who used a userbox (#2). Figures 1 and 2 shows screen shotsof these two disclosure methods. For #1, we queried the Wikipedia
1 http://dumps.wikimedia.org/enwiki/20110115/

Figure 1: Gender preference setting in its default state.

Figure 2: Userboxes used by Wikipedia editors to display andannounce their gender.
API to obtain the gender setting for all users with at least one edit,
and for #2, we identified users who have either the User:UBX/maleor User:UBX/female gender userbox displayed on their User page.
There exist several other userboxes that can be used to denote one’s
gender (including ones for transgender and other alternative genders), but since they have been used by too few users to give statistically reliable data, we chose to not include them in our analysis.
We also chose to not collect gender disclosures that occurred only
via method #3 because doing so would require substantially moreadvanced natural language processing techniques.
We collected data for 106,698 users that have disclosed their gender using the user preference setting and 8,630 users who have done
so using a userbox. Of these, 1,478 users specified their gender using both methods. 1,476 of these users specified the same genderin both places. The remaining two users have a different genderspecified using each method, and are excluded from our analyses.
In total, our editor gender data set contains 113,848 users who havecollectively made over 67 million edits.

3.2

Assumptions and Limitations

This gender data is comprised of self-reports from self-selected
users, and thus has limitations. The validity and generalizability of
our results are subject to several assumptions about this data. First,
we assume that users are mostly honest in reporting their gender.
Second, we assume that users who do not report their gender behave similarly to those who do report their gender (which is just
2.8% of editors). Finally, we assume that self-report rates are similar between males and females at similar stages of their Wikipedia
life-cycle. In our data, we find that self-report rates increase dramatically for dedicated editors: we have gender information for
6.5% of editors with at least ten edits, 14.1% of those with at least
100 edits, and 34.7% of those with at least 1,000 edits.
While necessary to enable our analysis, these assumptions are
difficult to confirm. We do note that at a high level, our results arecomparable to those obtained from the Wikimedia Foundation’s recent survey of Wikipedia users [10], which provides limited support for our assumption about truthful self-reports. However, we
are unable to provide evidence for the other assumptions. Unfortunately, this problem is fundamental for any Wikipedia research that
depends on existing publicly-available data. Nearly all the resultsin this paper are subject to these assumptions and limitations.

4.

RESULTS AND ANALYSIS

We now step through each of our research questions and hypotheses, describing our methods for testing each hypothesis, thedata we used, and our results.

Gender Gap vs. Edit Count

Wikipedia Editor Survival Curve
1.0

Males
Females

0.8

15%Survival Rate

% Female Editors

20%

10%

5%

0.6

0.4

0.2

0%
0.0
0

Number of Edits

Figure 3: Wikipedia’s gender gap as a function of editor activity for editors first editing Wikipedia during 2009. The gender
gap is more pronounced when looking at high-activity editors.

4.1 RQ1: Gap-Overall
H1a Gap-Exists: Wikipedia has a substantial editor gender gap.
To characterize the gender gap at a high level, we comparedmale and female editors using three broad metrics: editor count,
edit count, and activity lifespan. We found that females comprised
16.1% of the 38,497 editors who started editing Wikipedia during
2009 and who specified their gender. This is indicative of a substantial gender gap in Wikipedia editors – males outnumber females byover 5 to 1. However, this is not the end of the story. Our other two
metrics suggest that the gender gap is even deeper than indicatedby editor count.
We found that despite females being 16.1% of the new editors in
2009, they only accounted for 9.0% of edits made by this cohort ofeditors. On average, a male editor made almost twice as many edits
as a female editor. Figure 3 depicts the gender gap at various levelsof edit count. We see that the gender gap widens when looking ateditors with many edits, and does not appear to stabilize until thepercentage of female editors drops to around 6% for editors making
more than about 500 edits. This observation points to the possibility that females leave Wikipedia earlier than males in their editing
tenures. Our third metric, activity lifespan, directly examines thispossibility.
Activity lifespan measures how long a user is active in a systembefore leaving. We used a definition of activity lifespan similar to
Yang et al.’s in their work studying user survival in social questionanswering systems [33]. We considered an editor’s “birth” to be
his or her first edit date and a “death” to be a period of edit inactivity exceeding six months (the death is recorded as the beginningof the period). Using this notion of user birth and death, we applied standard survival analysis techniques [8]. Figure 4 shows theestimated male and female survival curves for users joining during 2009. We see a distinct difference between the two curves.
Females stop editing Wikipedia sooner than males, and the ratioof males remaining to females remaining for this cohort increases
steadily as time passes. Therefore, one of the factors contributing toWikipedia’s gender gap is a lower retention rate for female editors
compared to male editors. We will return to exploring this survival
data in greater detail later in section 4.3.
H1b Gap-Shrinking: Wikipedia’s gender gap is shrinking.
To examine how the overall gender gap has evolved over time,
we looked for a trend in the gender breakdown of new editors join-

100

200

300

400

500

600

700

Lifespan (days)

Figure 4: Activity lifespan of new female and male Wikipedia
editors in 2009 expressed as Kaplan-Meier survival curves.
The difference between the curves is statistically significant (logrank test, p < 0.001).
ing Wikipedia each month. However, there are confounds in thedata that made this a tricky task. The two methods for disclosing
one’s gender that we consider in our data (userboxes and preference setting) were introduced at different times, so simply looking
at the trend over all of Wikipedia’s existence is not a fair analysis. Users who started editing Wikipedia before a gender disclosure method was introduced could not specify their gender usingthat method until after its introduction date. The survival analysis presented above shows that males tend to have longer activity
lifespans. Therefore, males who joined Wikipedia before a gender
disclosure method was introduced are more likely than females tostill be active once the method is made available (and thus, be able
to specify a gender using it).
Due this confound, we could only make valid comparisons forusers joining Wikipedia after the introduction of a gender disclosure method, and only for the subset of users who used that disclosure method. The gender userboxes were introduced in December
2005, and the gender preference setting was introduced in January
2009. Figure 5 shows two charts, each depicting the gender gap
over time for one of the gender disclosure methods. The trends inboth charts are flat with nearly zero slope. Therefore, Wikipedia’s
gender gap appears to have remained approximately constant since
December 2005, which is surprising given that other online gender gaps have been shrinking over time. Note that the two charts
indicate different female editor percentages. This may be because
the userbox gender disclosure method requires more Wikipediaspecific knowledge to use. Thus, there may be fewer female userbox users due to the survival differences shown earlier (that is, females might be more likely to stop editing Wikipedia before learning about the userboxes).
Our findings yield evidence to support H1a Gap-Exists, but not
H1b Gap-Shrinking. Wikipedia suffers from a substantial gender
gap, and the gender gap has not been closing over time. Next, weturn to an exploration of why the gender gap matters to Wikipedia.

4.2

RQ2: Gap-Matters

H2a Focus-Differences: Male and female editors focus on different content areas.
To test this hypothesis, we used data and methods described byChen et al. in [6] to determine editors’ interest areas. Based on theJanuary 2008 data dump, this method uses Wikipedia’s category

Area
PeopleArts
Philosophy
Religion
HealthHistory
Science
Geography
All Editors

New Editor Gender - Userbox
30%% Female Editors

25%
20%

R² = 0.0003

15%
10%
5%
0%
2006

2007

2008

2009

2010

2011

Month of First Edit

New Editor Gender - Preference Setting
30%% Female Editors

R² = 0.0388

15%
10%
5%
0%
Jan 2009

Jul 2009

Males
3,673
3,282
344
484
313
1,578
4,299
1,856
23,430% Female
10.7% ***
10.4% ***
8.3%
7.1%
7.1%
6.7%
5.2% ***
3.7% ***
7.6% aTable 1: Gender distribution of editors in eight interest areas
as of January 2008. Not all editors are in an interest area,and some editors are in multiple areas. Statistical comparisonswere performed using a Chi-Square goodness-of-fit test using
the overall gender breakdown of editors in this data as the expected distribution (*** p < 0.001).
a 7.6% of editors being female is very low compared to figures reported earlier. This is a manifestation of the survival-related confounds described in section 4.1. The data issues do not affect thisanalysis because the gender distributions we are comparing are all
drawn from the same data.

25%
20%

Females
441
382
31
37
24
113
235
71
1,915

Jan 2010

Jul 2010

Jan 2011

Month of First Edit

Figure 5: Wikipedia’s gender gap in new editors as a functionof time for users specifying their gender via userbox (top) andusers specifying their gender via preference setting (bottom).
Best-fit lines are plotted as black dotted lines, and have slopesof 0.0043% (top) and -0.029% (bottom) per month.
structure to assign articles to eight primary interest areas: Arts, Geography, Health, History, Science, People, Philosophy and Religion. Each editor is then placed into zero or more of these eightareas based on his or her editing activity and how focused it is in
specific areas’ articles.
Using Chen et al.’s data, we determined how many male and female editors are in each of the eight interest areas and comparedeach area’s gender distribution with the Wikipedia-wide one. Table 1 shows our results, which indicate that males and females arefocused on disparate content areas within Wikipedia. There is agreater concentration of females in the People and Arts areas, whilemales focus more on Geography and Science. These findings are
consistent with gender differences found in the National Science
Foundation’s biennial survey of college graduates: men generally
skew toward science and engineering fields, while women skew toward other fields including arts and humanities [18]. These results,
when combined with the existence of an editor gender gap, are suggestive of a deficiency in some areas.
H2b F-Coverage-Worse: Coverage of topics with particular interest to females is inferior to topics with particular interest to males.
To more directly examine the effect of the gender gap on content
coverage quality, we performed two analyses: a general one basedon editor activity, and a domain-specific one using an external data
source that allowed us to more carefully control for possible confounds. To measure coverage quality, we used article length asa proxy. While length is a very simple metric, existing researchshows that it is an excellent predictor of a Wikipedia article’s assessment level [3, 32], which is a community-assigned rating of anarticle’s overall quality.
Wikipedia-wide comparison. In our first analysis, we used thegender of an article’s editors to determine whether the article topic
is of male or female interest. Specifically, we defined an article’s
fe as the proportion of its known-gender editors that are female.
As we found earlier in section 4.1, edits by female editors turn out
to be more rare than one would expect, so this metric is subject tohigh relative variance and noise. To help reduce the effect of noise,
we limited this analysis to high-activity articles where we knew thegender of at least 30 editors. In addition, we excluded any articles
that are less than 100 bytes long because such “articles” are likely tobe redirects that point to other articles. Applying these constraints
left us with 59,579 articles in the Main namespace, which is where
all of Wikipedia’s encyclopedic content is located.
We considered an article topic to be “male” if it is in the bottom
quintile of fe , “neutral” if it is in the third (center) quintile, and“female” if it is in the top quintile. We found that the average malearticle is 33,301 bytes long, the average female article is 28,434
bytes long, and the average neutral article is 36,511 bytes long. All
differences are statistically significant (t-Test, p < 0.001). So, on
average, male articles are significantly longer than female articles,
which indicates that coverage quality of topics with particular interest to females is indeed lacking. We also see that neutral articles are
longer than both male and female articles. This is perhaps because
gender-neutral topics appeal to both genders, and thus, are likely togarner the most overall attention from Wikipedia’s editors.
Movie article comparison. The second analysis for this hypothesis looked at one specific domain of topics: movies. We chose
to focus on this particular domain because it allowed us to use amethod that let us more carefully control for other factors that may
affect an article’s length such as the popularity or age of the topic.
Furthermore, this method is not subject to the Wikipedia data assumptions described in section 3.2.
For this analysis, we used data from our movie recommender
web site MovieLens2 . MovieLens users can assign ratings to movies
2 http://movielens.umn.edu

• Article Length is our dependent variable and is defined as thelength (in characters) of the Wikipedia article about the movie(log-transformed for normality).
• Movie Gender is our independent variable and is defined as fr .
We additionally include its quadratic term (labelled as “Movie
Gender Sq.”) because our previous analysis suggests that article
length may have a non-linear relationship with topic gender.
• Movie Popularity is a control variable defined as the number ofMovieLens ratings that the movie has (log-transformed for normality). Articles about often-rated movies may draw more attention from editors, and thus, may be longer.
• Movie Quality is a control variable defined as the average MovieLens rating assigned to the movie. Articles about well-liked
movies may be longer than ones about poorly-rated movies, again
due to increased editor attention.
• Movie Age is a control variable defined as the age of the movie
in years. Article length may vary with movie age due to better
availability of information about newer movies.
The results of the regression model are shown in table 2. We seethat even when controlling for variables that we would expect toaffect article length, both fr and its quadratic term are significantly
associated with article length. A plot of the effect size of movie
gender is shown in figure 6. All else being equal, articles about “female” movies are shorter than ones about “male” movies3 . We also
built a similar regression model using WikiProject Film’s article
assessment ratings4 as the dependent variable (coded as an equally
spaced ordinal variable) and obtained qualitatively similar results;
that is, “female” movie articles have lower assessment ratings than“male” movie articles.
3 The Movie Popularity control variable has the expected effect.
The Movie Quality variable has no significant effect. The Movie
Age variable has the opposite of the expected effect: older movies
tend to have longer articles. Perhaps only very noteworthy older
movies were in our data set.
4 http://en.wikipedia.org/wiki/WP:FILMA

Variable
Coef.
SE
Intercept-0.335 *** 0.0190
Movie Popularity
0.676 ***
0.0106
Movie Quality
0.005210.0107
Movie Age
0.0123 *** 0.000604
Movie Gender-0.144 *** 0.0107
Movie Gender Sq. 0.0257 *** 0.00692
Adj. R2 = 0.4704, F(5, 5843) = 1040, p < 0.001
Table 2: Results of multiple linear regression model withmovie article length as the dependent variable. Positive coefficients indicate variables associated with increased article
length, and negative coefficients indicate variables associatedwith decreased article length (*** p < 0.001).
Effect of Movie Gender on Article Length
1.8

Relative Effect on Article Length

in order to receive personalized movie recommendations. To date,MovieLens has collected over 15 million movie ratings from its
150,000 users. A key feature of this data is that it contains selfreported gender information from over 80% of users who started
using MovieLens before May 2003 (MovieLens stopped requesting
demographic information upon registration in May 2003). While
MovieLens also appears to be affected by a gender gap (32% of
its users are female), it is less imbalanced than Wikipedia, whichallowed us to compute a gender metric even for relatively obscure
movies. We defined a movie’s fr as the proportion of its knowngender raters that are female. To help avoid confounds due to gender differences in long-term MovieLens usage, we limited our analysis to movies that existed in the system as of May 2003.
We mapped each MovieLens movie to its corresponding article
by scanning Wikipedia for articles that have a link to the movie’s
IMDb page and then applying basic heuristics to compare the movie
name and article titles. We hand-checked 100 randomly-selected
mappings and found no errors. We excluded any movies with fewer
than ten known-gender raters, as well as movies that we could not
locate a Wikipedia article for (3.7% of movies). The resulting
movie data set contained 5,850 movies.
For this analysis, we built a linear regression model that predicts
article length using fr and several movie properties that may affectarticle length. The regression variables are summarized below. Allvariables except Movie Age are standardized with a z-score transformation. VIF values for these variables are below 1.3, so multicollinearity is not an issue.

1.6

1.4

1.2

1

0.8
−3

−2

−1

0

1

2

3

Movie Gender (Z−Score)

Figure 6: Effect size of movie gender ( fr ) on Wikipedia article
length. Movie gender is shown as standardized z-score, where
negative values are more “male” and positive values are more“female”. Effect is shown as a multiplicative factor. For example, a movie that is at -2 on the x axis is two standard deviations
more “male” than the average movie, and its Wikipedia article
is expected to be 1.4 times as long as the average movie’s article.
The results of these two analyses show the same thing: there
are measurable gender-associated imbalances in Wikipedia’s content coverage quality. There is a silver lining though: in follow-up
analyses of two other domains – Nobel Prize winners, and recipients of the Academy Award for Best Actor/Actress – we found thatthe average length of articles about female subjects is comparable
to that of articles about male subjects. The effects of Wikipedia’s
gender gap do not seem to apply when it comes to coverage of very
important and notable topics (these award winners are arguably at
the pinnacle of scientific, social, and/or cultural achievement).
H2c F-Social: Females are more likely to be involved in socialand community-oriented areas of Wikipedia.
To test this hypothesis, we performed comparisons of male andfemale involvement in three different areas of Wikipedia that represent increased social or community engagement. We describe eachof the three in turn below.
Editing Behavior. First, we looked at social engagement at a
broad level by examining activity within the User and User Talk
namespaces. Wikipedia’s guideline5 on these namespaces states:“There is no fixed use for user pages, except that usually one’s
5 http://en.wikipedia.org/wiki/WP:UPYES

Namespace
Females Males
User & User Talk *** 25.2% 19.1%
Main & Talk*** 69.1%
74.5%
Other namespaces *** 1.88%
2.82%
Table 3: Comparisons of Wikipedia editing behavior across different namespaces. The figures are the percentage of editing behavior in each namespace, averaged across female or male editors. Differences were compared using a t-Test (*** p < 0.001).
Females
Males
Menteeships (Jan. 2010 data)
Adopted users *** 0.54% (51/9511) 0.30% (200/67252)
Administrators (Jan. 2011 data)
Administrators *** 0.33% (50/15362) 0.59% (579/98486)" ≥ 2, 000 edits *18.6% (49/263)
13.4% (575/4283)
Table 4: Comparisons of Wikipedia participation in mentoring
and administration. The figures are the percentage of each gender to have participated. Proportions were compared using a
Chi-square test (*** p < 0.001, ** p < 0.01, * p < 0.05).
user page has something about oneself, and one’s talk page is used
for messaging.” Since pages in these namespaces are usually used
for self-expression and interpersonal communication, we interpret
editing activity within them to be indicative of social engagement.
We looked at users with at least ten edits (4,990 females and 43,850
males), computed the percentage of each user’s edits that are ineach namespace, and compared the male and female means. Table 3 shows our findings for three groups of namespaces: 1) Userand User Talk, 2) Main and Talk, and 3) all other namespaces. We
see that on average, a female makes a significantly higher concentration of her edits in the User and User Talk namespaces, mostly
at the cost of fewer edits in Main and Talk, which contain encyclopedic content and discussions about the content, respectively.
Menteeships. Second, we looked at activity in one of Wikipedia’s
mentoring programs, Adopt-a-User6 . Specifically, we looked at all
editors who obtained a mentor, referred to as an “adopter,” throughthis program. We view participation in this program as a form ofsocial engagement because the mentoring process is inherently interpersonal and social [22]: an adoptee chooses to seek personalized help and advice from a more experienced peer instead of goingit alone. In the Adopt-a-User program, once an adoption relationship has formed, the editor who has been adopted places an Adoptee
template on his or her User page. To identify program participants,
we scanned all User pages in the January 2010 data dump for editors with an Adoptee template on their User page at any point intheir history. Table 4 shows our results, which indicate that females
are significantly more likely than males to participate in this program as mentees.
Administrators. Lastly, we looked at how often editors have become Wikipedia administrators. Being an administrator provides auser with additional capabilities such as protecting pages, hiding
revisions from public view, and blocking others from editing. Besides their usual editing tasks, administrators have the additional
responsibility of being Wikipedia’s janitors and custodians7 . Administrators help clean up after vandals, resolve conflicts between
editors, and maintain order in the community. Since becoming an
administrator is a major form of civic duty in Wikipedia, we consider it an indicator of increased community engagement.
6 http://en.wikipedia.org/wiki/WP:ADOPT
7 Wikipedia’s symbol for an administrator is a janitorial mop!

Returning to table 4, we see that overall, a greater proportionof males than females become administrators. However, one major factor in successfully becoming an administrator is having asubstantial edit count [5], and we know from our earlier resultsthat females are more likely to leave Wikipedia before accumulating many edits. If we restrict our analysis to users who have atleast 2,000 edits (all but five administrators have over 2,000 edits),
the tables are turned. Within this group of dedicated Wikipedians,
females are actually significantly more likely to become administrators than their male counterparts. We also performed the same
comparison using thresholds of 1,000 and 4,000 edits and obtained
the same qualitative result.
The data support all three hypotheses under RQ2: Gap-Matters.
In H2a Focus-Differences and H2b F-Coverage-Worse, we findthat the gender gap appears to have a detrimental effect on content coverage of topics with particular interest to females. Our results for H2c F-Social suggest that addressing the gender gap could
help Wikipedia better address its needs in social- and communityoriented areas.

4.3

RQ3: Gender-Conflict

H3a F-Uncontentious: Females tend to avoid controversial orcontentious articles.
We address this hypothesis by looking at the edit protection status of Main namespace articles that have a high concentration ofeither female editors or male editors. An edit-protected article cannot be edited by certain classes of users, depending on the levelof protection (typically new or anonymous editors). Wikipedia’s
protection policy8 states that articles that are subject to content disputes, vandalism, or other forms of disruption are candidates forprotection. Therefore, protected articles tend to be ones that areabout controversial or contentious topics.
We found that 5.20% of the “female” articles described in section 4.2 are protected, while just 2.39% of the “male” articles areprotected, χ2 (1, N = 23989) = 129.1, p < 0.001. Thus, articles thathave a higher concentration of female editorship are actually more
likely to be contentious than those with more males.
H3b F-Reverted-More: Female editors are more likely to havetheir early edits reverted.
Our analysis of reverts uses the January 2010 data dump, whichis the most recent dump that we had processed revert information
for9 We used the method described in Priedhorsky et al. in [21]to detect reverts and to classify whether they are for damage repair (specifically, we use the “D-Loose” classification, which is animperfect heuristic, but identifies many common vandalism repair
patterns). Because we were only interested in reverts of good-faithattempts to improve the encyclopedia, we only considered revertsin the Main namespace that were not for the purposes of repairingdamage or vandalism. To limit the effect of right truncation, we
also only considered reverts that occurred within one week of anedit (this was the case for over 95% of the reverts in our data set).
Using this data, we took each user’s chronological sequence ofMain namespace edits, partitioned the edits into bins of increasing size to represent different stages of editor tenure, and determined what percentage of edits in each bin were reverted for nonvandalism reasons. Table 5 shows the results of this analysis aggre8 http://en.wikipedia.org/wiki/WP:PROTECT
9 This dump was generated with about five months of data missing(http://blog.wikimedia.org/2010/05/29/). Because this issue only
affects a small subset of edits made by 2.7% of users in our analysis,
we do not believe it materially affects the reported results.

Revert Rates During Editor Tenure
Females
Males
Edit #
N
Mean
N
Mean
1
6,305 6.99% *** 53,738 4.96%
2-3
4,989 6.04% *** 45,514 4.44%
4-7
3,798 4.69% **37,272 3.98%
8-15
2,871 3.47%
30,066 3.12%
16-31
2,080 2.56%
23,798 2.66%
32-63
1,490 2.33%
18,270 2.49%
64-127
1,039 1.97%
13,850 2.27%
128-255 749
2.29%
10,355 2.07%
Table 5: Average rate at which editors are reverted for nonvandalism-related reasons, by gender and by stage of editor
tenure (the first row shows the reverted rates for users’ first edits, the second row shows the rates for users’ second and thirdedits, and so on). Averages were compared using a t-Test (***
p < 0.001, ** p < 0.01).
gated by gender. We see that in the first three bins, which consistof users’ first seven edits, the average reverted edit percentage forfemales is significantly higher than that for males. Therefore, females are indeed significantly more likely than males to have theiredits reverted during the early parts of their tenure.
Interestingly, beyond this initial handful of edits, we see little
statistical difference between females and males in how often they
are reverted. This suggests that females and males who manage toreach a modest level of Wikipedia experience are on par with eachother with respect to community-perceived contribution quality.
H3c F-Reverted-Leave: Female editors are more likely to stop
editing and leave Wikipedia when being reverted as newcomers.
Now, we look at how editors react to being reverted. The survival
analysis in section 4.1 indicated that females appear to stop editing Wikipedia sooner than males. We investigate this phenomenon
more deeply here, looking at whether female newcomers are more
likely than their male counterparts to stop editing if reverted. To doso, we developed a Cox regression model [8] to determine which
factors are associated with longer (or shorter) activity lifespan. The
variables in our model are limited to those that describe a user during his or her first 24 hours of editing Wikipedia, and are summarized below. VIF values for these variables are below 1.4.
• Gender is the editor’s gender, dummy-coded with females as 1
and males as 0.
• Edits24H is the number of edits made in the first 24 hours ofediting Wikipedia (log-transformed for normality).
• %RvVandal is the proportion of edits made in the first 24 hours
that were reverted for vandalism-related reasons.
• %RvNonVandal is the proportion of edits made in the first 24
hours that were reverted, but not for vandalism-related reasons.
• %RvNV × Gen is an interaction term between %RvNonVandal
and Gender, and is used to study the interaction effect between
gender and being reverted for non-vandalism reasons.
The results of the regression model are shown in table 6. The
model has limited predictive power, but nonetheless, we see that all
the variables except the interaction term have a significant association with activity lifespan. Making more edits during one’s first
24 hours as a Wikipedia editor is associated with a longer activity lifespan, while having one’s early edits reverted for any reason,
vandalism-related or otherwise, is associated with a shorter lifespan. Note that even after taking these factors into account, being
female still has a strong association with shorter activity lifespan.

Variable
Gender (female)
Edits24H%RvVandal%RvNonVandal%RvNV × Gen

Coef.
0.248
-0.1640.486
0.332
0.0393

Hazard Ratio 95% CI
1.281 ***
1.229-1.335
0.849 ***
0.838-0.861
1.626 ***
1.498-1.766
1.394 ***
1.314-1.478
1.040
0.904-1.197
Adj. R2 = 0.043, p < 0.001

Table 6: Results of Cox proportional hazards regression model
predicting activity lifespan for editors who started editing during 2009. Variables with hazard ratios above 1 are associatedwith shorter activity lifespans, while those with ratios below 1
are associated with longer lifespans (*** p < 0.001).
However, contrary to our expectation, there is no interaction effect between gender and being reverted for non-vandalism reasons.
It appears that males and females are affected similarly when theiredits are not accepted by the Wikipedia community. This point deserves elucidation. Although early reverts appear to drive editors
away from Wikipedia, and although females are more likely to bereverted early in their tenure, if a revert happens to a female, thelikelihood of her departure is not affected more than that of a malein a similar situation. Therefore, the gender gap appears to be due
more to females being reverted disproportionately, rather than tofemales reacting more strongly when they are reverted.
H3d F-Blocked-Less: Female editors are less likely to be blocked.
Our final hypothesis looks at blocks imposed by Wikipedia’s administrators. We counted how many males and females have everbeen blocked and determined which of those blocks were designated as being for an indefinite length of time. Indefinite-length
blocks are typically reserved for cases of “significant disruption or
threats of disruption, or major breaches of policy”10 , so we expected to see fewer females affected by such blocks.
We found that 4.39% of female users (673) and 4.52% of maleusers (4,449) have been blocked at some point in their Wikipedia
tenures, which is not a significant difference, χ2 (1, N = 113848) =0.545, p = 0.460. Looking only at users who were subject to anindefinite-length block, we found a 3.85% rate for females (592),
and 3.32% for males (3,274), χ2 (1, N = 113848) = 11.2, p < 0.001.
So, while males and females appear to be blocked at similar rates,
females are significantly more likely to be blocked indefinitely.
Intrigued by this result, we performed a follow-up analysis looking at another form of anti-social behavior on Wikipedia: vandalizing articles. Using an approach similar to the one depicted intable 5, we looked at how often males and females have their edits reverted specifically for vandalism-related reasons early in theirtenure. The results here are qualitatively consistent with the block
results presented above: females are significantly more likely tobe reverted for vandalizing Wikipedia’s articles (female and malereverted-for-vandalism rate over each user’s first seven edits: 3.26%
and 2.11% respectively, t(60041) = 7.28, p < 0.001).
A limitation of the data available for testing this hypothesis isthat of users who are blocked or who are reverted for vandalism, theproportion who have self-reported their gender is even smaller thanthe baseline. It is possible that gender differences in self-reporting
rate or other phenomena among these troubled users are distorting the results presented above. For example, perhaps some people
who participate in Wikipedia for anti-social purposes will intentionally mis-report their gender to elicit a different reaction fromthe community [4]. (We have no data available to test speculationsof this sort.) Taken at face value, the results are quite surprising
10 http://en.wikipedia.org/wiki/WP:BLOCK

Hypothesis
H1a Gap-Exists
H1b Gap-Shrinking
H2a Focus-Differences
H2b F-Coverage-Worse
H2c F-Social
H3a F-Uncontentious
H3b F-Reverted-More
H3c F-Reverted-Leave
H3d F-Blocked-Less

Supported?
Yes
No
Yes
Yes
Yes
Reversed
Yes
No
Reversed

Description
Wikipedia has relatively few female editors, and they leave Wikipedia sooner than males
The gender gap has not been shrinking over time
Females and males focus on different broad content areas
Coverage of “female” topics is inferior to coverage of “male” topics
Females are more likely to participate in social- or community-oriented areas of Wikipedia
Articles with high female editor concentrations are more contentious
Female newcomers are reverted more than males
Being reverted as newcomers has the same apparent effect on males and females
Females are more likely to be indefinitely blocked

Table 7: A summary of our hypotheses and findings.
given the substantial body of literature indicating that males are
more likely to act out or violate rules (e.g., [30, 11, 27]).
Our findings for RQ3: Gender-Conflict are mixed. We find thatfemale newcomers have a harder time getting good-faith contributions to be accepted by the community (H3b F-Reverted-More).
However, our findings for H3c F-Reverted-Leave indicate that theeffect of having an edit reverted is no worse for females than it isfor males. Unexpectedly, we find that female editors are more concentrated in areas with high controversy (H3a F-Uncontentious),
and are more likely than males to draw corrective actions from fellow editors (H3d F-Blocked-Less). In summary, the available data
indicate that female editors experience more adversity than maleeditors in all the areas that we studied.

5. DISCUSSION
Table 7 shows a summary of our hypotheses, whether we foundsupport for each hypothesis, and a brief statement of our results.
One way of interpreting our results is in terms of Preece and Shneiderman’s Reader-to-Leader Framework for social media participation [20]. Preece and Shneiderman describe a process in whichusers of a social media system move through four levels of participation: reader, contributor, collaborator, and leader. We systematically look at female participation in Wikipedia through the lens of
this framework.
Readers. Becoming a consumer of social media is a typical firststep toward active participation. Existing survey research yields
mixed findings about the share of females in Wikipedia’s readership [16, 35, 10]. The most accurate and unbiased of these appears
to be [35], which used random telephone dialing along with statistical correction techniques to account for non-response bias. It
indicates a female readership share of approximately 47%, whichsuggests that Wikipedia is effective at drawing a relatively genderbalanced population of readers. The present research does not consider readership directly, but this figure will serve as a useful reference point.
Contributors. Perhaps the most challenging task for a social
media system is to convince a reader to start giving back to the community – that is, to turn readers into contributors, or in Wikipedia’scase, editors. Our results for H1a Gap-Exists show that Wikipedia
is much less successful in “converting” female readers than malereaders, dropping from a 47% female share in readership to a 16%
share in editors, a figure that has shown little to no change for years
according to our results for H1b Gap-Shrinking.
Collaborators. When multiple contributors come together towork toward a common goal, they become collaborators. In researching H2c F-Social we found that females edit more in theUser and User Talk namespaces, indicating a potential interest incollaboration. Our data do not contain metrics that directly describe collaboration activity, but a reasonable proxy is simply thepresence of sustained editing activity. Our survival analyses in

H1a Gap-Exists and H3b F-Reverted-More indicate that females
who become contributors stop editing Wikipedia sooner than males.
Furthermore, both H3b F-Reverted-More and H3d F-Blocked-Less(reversed to F-Blocked-More!) suggest that females encounter more
adversity in Wikipedia. Together, these data suggest that while females appear interested in becoming collaborators, they have moredifficulty in making the transition for a variety of reasons.
Leaders. Effective collaborators who are passionate about theirwork and who are interested in the system at a meta-level emerge
as community leaders. Preece and Shneiderman specifically examine Wikipedia administration as an exemplar of a leadership role inonline communities. In our analysis for H2c F-Social, we see thatfemales who reach a high level of participation are more likely thantheir male counterparts to take on a leadership and administrationrole. However, as we saw in H1a Gap-Exists only 6% of the editors
who have contributed more than 2,000 edits are female11 . Some
Wikipedians have observed that an administrator shortage may belooming as current administrators “retire,” but few new administrators are emerging to fill their shoes12 . Addressing the gender gap
in high-participation editors might be an opportunity to meet thisdemand for more administrators.
Implications. Overall, our findings indicate that there is a substantial male-skewed gender imbalance in English Wikipedia editors that does not appear to be closing at any appreciable rate. This
is at odds with observed participation rates in other forms of online social media that are gender-balanced or that are even femaleskewed [15, 19]. Furthermore, we find that the gender gap matters
to Wikipedia: there are gender-associated imbalances in coverage
quality, which impinges on Wikipedia’s goal of producing a highquality encyclopedia. Not only would addressing the gender gap
help resolve the quality disparity, it would also help increase diversity within Wikipedia’s collaborations, which prior research has
shown to improve group productivity and retention rates [6] as well
as decision-making quality [14].
The problem is subtle, and simple attempts at solution without
detailed understanding are likely to fail. Of our nine hypotheses,
all of which seemed plausible before we began the study, nearly
half were not supported by the data. How can it be that the gender gap in Wikipedia is not closing, though overall Internet usage has become gender-balanced? Taken together, our results for
RQ3: Gender-Conflict hint at a culture that may be resistant to female participation. More research, including interviews, surveys,
and focus groups, is needed to determine the underlying causes ofthe problems evidenced by our findings, and to determine what canbe done to improve the situation. We hope this research is a firststep toward addressing the gender imbalance – and the problems it
causes – in Wikipedia.
11 Nearly all administrators have at least 2,000 edits.
12 http://en.wikipedia.org/w/index.php?oldid=393297323

6. ACKNOWLEDGEMENTS
We are grateful for the support of the members of GroupLens Research, particularly Angela Brandt and Katherine Panciera for fruitful discussions about the gender differences literature. We thankthe Wikimedia Foundation for facilitating access to public Wikipedia
data. This work is funded by the NSF (grants IIS 08-08692, 0968483, 10-38277) and by the China Scholarship Council.

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