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Issue Cover
Volume 2
Issue 12
December 2023
(In Progress)
Article Contents
* Abstract
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Journal Article
Toxic comments are associated with reduced activity of volunteer
editors on Wikipedia
Ivan Smirnov,
Ivan Smirnov
Graduate Research School, University of Technology Sydney
,
15 Broadway
, Sydney 2007,
Australia
To whom correspondence should be addressed: Email:
ivan.smirnov@uts.edu.au
ORCID logo https://orcid.org/0000-0002-8347-6703
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Camelia Oprea,
Camelia Oprea
Department of Computer Science, RWTH Aachen University
,
Ahornstrasse 55
, Aachen 52074,
Germany
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Markus Strohmaier
Markus Strohmaier
Business School, University of Mannheim
,
L 15 1-6
, Mannheim 68161,
Germany
GESIS--Leibniz Institute for the Social Sciences
,
Unter Sachsenhausen 6-8
, Koln 50667,
Germany
Complexity Science Hub Vienna
,
Josefstaedter Strasse 39
, Vienna 1080,
Austria
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Competing Interest: The authors declare no competing interest.
Author Notes
PNAS Nexus, Volume 2, Issue 12, December 2023, pgad385, https://
doi.org/10.1093/pnasnexus/pgad385
Published:
05 December 2023
Article history
Received:
30 June 2023
Accepted:
30 October 2023
Corrected and typeset:
05 December 2023
Published:
05 December 2023
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Ivan Smirnov, Camelia Oprea, Markus Strohmaier, Toxic comments
are associated with reduced activity of volunteer editors on
Wikipedia, PNAS Nexus, Volume 2, Issue 12, December 2023,
pgad385, https://doi.org/10.1093/pnasnexus/pgad385
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Abstract
Wikipedia is one of the most successful collaborative projects in
history. It is the largest encyclopedia ever created, with millions
of users worldwide relying on it as the first source of information
as well as for fact-checking and in-depth research. As Wikipedia
relies solely on the efforts of its volunteer editors, its success
might be particularly affected by toxic speech. In this paper, we
analyze all 57 million comments made on user talk pages of 8.5
million editors across the six most active language editions of
Wikipedia to study the potential impact of toxicity on editors'
behavior. We find that toxic comments are consistently associated
with reduced activity of editors, equivalent to 0.5-2 active days per
user in the short term. This translates to multiple human-years of
lost productivity, considering the number of active contributors to
Wikipedia. The effects of toxic comments are potentially even greater
in the long term, as they are associated with a significantly
increased risk of editors leaving the project altogether. Using an
agent-based model, we demonstrate that toxicity attacks on Wikipedia
have the potential to impede the progress of the entire project. Our
results underscore the importance of mitigating toxic speech on
collaborative platforms such as Wikipedia to ensure their continued
success.
Significance Statement
While the prevalence of toxic speech online is well studied, its true
impact on the productivity of online communities remains largely
unexplored. In this study, we focus on Wikipedia, which as the
largest and most-read online reference, serves as a vital source of
knowledge for millions of users worldwide. By analyzing all comments
made over 20 years on user talk pages of 8.5 million editors across
multiple language editions, we demonstrate that toxic speech is
associated with a significant loss in the productivity of Wikipedia
editors. These findings may have broad implications for large-scale
collaborative projects and online communities, emphasizing the need
to promote healthy and sustainable communication practices to protect
crucial online information ecosystems and ensure their long-term
success.
Introduction
Wikipedia is arguably one of the most successful collaborative
projects in history. It has become the largest and most-read
reference work ever created, and it is currently the fifth most
popular website on the Internet (1). Millions of users worldwide rely
on Wikipedia as their first source of information when encountering a
new topic, for fact-checking and in-depth research (2). Even if
caution might be required when consulting less actively maintained
pages (3), numerous studies have shown that Wikipedia is a reliable
source of information in areas ranging from political science (4) to
pharmacology (5) and its accuracy is comparable to traditional
encyclopedias (6) and textbooks (7).
One of the most remarkable aspects of Wikipedia's success is that its
content is exclusively created and curated by volunteer editors,
known as Wikipedians. The English edition alone has more than 120,000
active editors (8). However, this volunteer-driven model also makes
Wikipedia susceptible to the inherent challenges associated with
maintaining such a large online community (9, 10). For example, it
has been previously observed that Wikipedia is not free of conflict,
particularly in the form of so-called edit wars (11), which impose
significant costs on the project (12) and could negatively affect the
quality of Wikipedia articles (13).
In this paper, we focus on the impact of toxic comments directed
toward editors on their activity. This aspect is less studied, but
potentially not less important, as affected by toxic comments,
Wikipedians might reduce their contributions or abandon the project
altogether, threatening the success of the platform (14).
Toxicity has been extensively studied on popular social media
websites such as Twitter (15, 16), Reddit (17, 18), and similar
platforms (19, 20). However, much of these research focuses on
automated toxicity detection and prevalence estimation rather than on
evaluating its impact (21). As an online encyclopedia, Wikipedia is
often perceived as immune to toxicity and has a strict "No personal
attacks" policy (22). Despite that, toxic speech and harassment have
been previously observed on the platform (23-27). The effects of such
behaviors on editors' contributions are, however, not well understood
nor well studied. The largest study to date relies on a voluntary
opt-in survey of the 3,845 Wikipedians conducted in 2015 (24). It
reports that 20% of users witnessing harassment have stopped
contributing for a while, 17% considered not contributing anymore and
5% stopped contributing at all.
In this paper, we analyzed all 57 million comments made on user talk
pages of editors on the six most active language editions of
Wikipedia (English, German, French, Spanish, Italian, Russian) to
understand the potential impact of toxic speech on editors'
contributions (see Methods and materials section for our definition
of toxic comments). User talk pages are a place for editors to
communicate with each other either on more personal topics or to
extend their discussion from an article's talk page. The majority of
toxic comments are left on user talk pages (28). The comments we
study were extracted from revision histories of talk pages and, thus,
include even those toxic comments that were later archived or deleted
by the page owner.
Figure 1 shows the activity of 50 randomly selected users who have
received exactly one toxic comment. While some users are seemingly
unaffected by a toxic comment, others temporarily reduce their
activity or leave the project completely. The aim of our paper is to
quantify this effect on the entire population of editors.
Fig. 1.
After receiving a toxic comment many users temporarily reduce their
activity or leave the project completely. The figure shows the
activity of 50 randomly selected users who received exactly one toxic
comment. Blue squares indicate an active day, i.e. a day when at
least one edit was done, starting from the first contribution of a
given user. Red triangles correspond to toxic comments. Note that
while some users are resilient and their activity is seemingly
unaffected by toxic comments, many users temporarily reduce their
activity or stop contributing altogether.
Open in new tabDownload slide
After receiving a toxic comment many users temporarily reduce their
activity or leave the project completely. The figure shows the
activity of 50 randomly selected users who received exactly one toxic
comment. Blue squares indicate an active day, i.e. a day when at
least one edit was done, starting from the first contribution of a
given user. Red triangles correspond to toxic comments. Note that
while some users are resilient and their activity is seemingly
unaffected by toxic comments, many users temporarily reduce their
activity or stop contributing altogether.
We estimate the number of lost active days associated with a toxic
comment by comparing the number of active days before and after
receiving a toxic comment. To account for potential baseline change,
we have matched editors that received a toxic comment with similarly
active editors who received a nontoxic comment. We have separately
studied if toxic comments increase the probability of editors leaving
the project altogether. Finally, we have used an agent-based model to
model the potential impact of an increased number of toxic comments
on Wikipedia.
Results
Loss of editor activity
To estimate the potential effect of a toxic comment, we compute the
proportion of users who were active on day X before or after
receiving a toxic comment (Fig. 2). We find that, on average, editors
are more active near the time when they receive a toxic comment, with
a peak at 24 h prior to the comment. At this time point, more than
40% of editors were active, as shown by the red line in Fig. 2a. This
is a rather unsurprising observation since toxic comments are often
made as a reaction to an edit made by a user and, thus, users are
expected to be active around the time of a toxic comment. Note that
if the timestamps around which the curve is centered are shuffled
(black line in Fig. 2a) then this pattern disappears completely as
expected.
Fig. 2.
After receiving a toxic comment, users become less active. On
average, users are more active near the time when they receive a
toxic comment (peak at zero for the red line in panel a). Average
activity across all users who have received a toxic comment is lower
in all 100 days after the event compared to the corresponding days
before (dashed and solid red lines in panel b). This cannot be
explained by a baseline drop in activity after a nontoxic comment
(dashed and solid blue lines in panel b). Similar results hold not
only for the English edition but also for the other five editions
(c-g).
Open in new tabDownload slide
After receiving a toxic comment, users become less active. On
average, users are more active near the time when they receive a
toxic comment (peak at zero for the red line in panel a). Average
activity across all users who have received a toxic comment is lower
in all 100 days after the event compared to the corresponding days
before (dashed and solid red lines in panel b). This cannot be
explained by a baseline drop in activity after a nontoxic comment
(dashed and solid blue lines in panel b). Similar results hold not
only for the English edition but also for the other five editions
(c-g).
We also find that average activity across all users who have received
a toxic comment is lower during all 100 days after the event compared
to the corresponding days before (dashed and solid red lines in Fig.
2b), e.g. smaller number of users is active five days after receiving
a toxic comment than five days before receiving it. To rule out the
possibility that this is due to a general drop in activity over time
or a drop in activity after any comment, we select a control group of
users who have received a nontoxic comment, and whose average
activity in the 100 days before the comment is the same as the
average activity of users who received a toxic comment (see Methods
and materials section for details).
We observe a similar characteristic peak around the nontoxic comment,
likely due to both toxic and nontoxic comments being reactions to a
contribution made by an editor. However, in contrast to a toxic
comment, a nontoxic comment does not lead to a significant decrease
in activity (dashed and solid blue lines in Fig. 2b). Similar results
hold for all six language editions that we have examined (Fig. 2c-g).
We then estimate the lost activity associated with a toxic comment by
computing the decrease in activity after a toxic comment, taking into
account a potential baseline drop, i.e. by computing D=
(Aftertoxic-Beforetoxic)-(Afternontoxic-Beforenontoxic). We find
that this loss is statistically significant for all language editions
studied (Table 1). We further explored the robustness of this result
with respect to the toxicity threshold and potential filtering of
users according to their activity. As expected, for higher toxicity
thresholds, i.e. for more severely toxic comments, the effect is
stronger (Supplementary Fig. S1). Considering only active users also
leads to higher estimates; however, here we are reporting a
conservative estimate, i.e. no filtering is used for results
presented in Fig. 2 and Table 1.
Table 1.
Lost active days in the 100 days following a toxic comment.
Edition . D . P-value . Nusers .
English -1.207 2.6x10-66 36,332
German -0.546 1.5x10-7 10,346
French -1.851 4.8x10-9 2,239
Spanish -0.563 8.6x10-3 2,446
Italian -0.336 2.3x10-2 3,567
Russian -1.219 7.8x10-4 1,134
Edition . D . P-value . Nusers .
English -1.207 2.6x10-66 36,332
German -0.546 1.5x10-7 10,346
French -1.851 4.8x10-9 2,239
Spanish -0.563 8.6x10-3 2,446
Italian -0.336 2.3x10-2 3,567
Russian -1.219 7.8x10-4 1,134
The lost active days are estimated by computing the difference
between the number of active days during 100 days after a toxic
comment and the number of active days during 100 days before a toxic
comment. This difference is then compared with the baseline drop
after a nontoxic comment, i.e. D=(Aftertoxic-Beforetoxic)-
(Afternontoxic-Beforenontoxic). The P-value is computed using
Student's t-test.
Open in new tab
Table 1.
Lost active days in the 100 days following a toxic comment.
Edition . D . P-value . Nusers .
English -1.207 2.6x10-66 36,332
German -0.546 1.5x10-7 10,346
French -1.851 4.8x10-9 2,239
Spanish -0.563 8.6x10-3 2,446
Italian -0.336 2.3x10-2 3,567
Russian -1.219 7.8x10-4 1,134
Edition . D . P-value . Nusers .
English -1.207 2.6x10-66 36,332
German -0.546 1.5x10-7 10,346
French -1.851 4.8x10-9 2,239
Spanish -0.563 8.6x10-3 2,446
Italian -0.336 2.3x10-2 3,567
Russian -1.219 7.8x10-4 1,134
The lost active days are estimated by computing the difference
between the number of active days during 100 days after a toxic
comment and the number of active days during 100 days before a toxic
comment. This difference is then compared with the baseline drop
after a nontoxic comment, i.e. D=(Aftertoxic-Beforetoxic)-
(Afternontoxic-Beforenontoxic). The P-value is computed using
Student's t-test.
Open in new tab
While these results demonstrate that our findings are not limited to
one language, they should not be used to compare effects between
language editions, as there is no guarantee that the same toxicity
threshold for the toxicity detection algorithm will have the same
meaning in different languages.
Note that given that thousands of users have received at least one
toxic comment (Supplementary Table S1), even a moderate loss per user
could result in many human-years of lost productivity for Wikipedia
in the short run. By multiplying the estimated loss per user from
Table 1 by the number of users who have received at least one toxic
comment, we could estimate the total loss of activity that is ranging
from 5 human-years for Russian Wikipedia to 265 human-years for the
English edition. The reason for the lasting effect of toxicity is
that some new users might be discouraged by a toxic comment and
choose to leave the project altogether after just a few
contributions. This means that a single toxic comment could deprive
Wikipedia of a potentially long-term contributor.
To further investigate this effect, we compare the probability of
leaving Wikipedia after receiving a toxic comment with the
probability of leaving Wikipedia after receiving a nontoxic comment.
Leaving Wikipedia
We observed that the probability of leaving Wikipedia after N
contributions declines with N. PN(leaving) is approximately
proportionate to N-a, where a ranges from 0.89 to 1.02, indicating
a long-tailed distribution. While the probability of leaving the
project after the first and only contribution is high (P1=47% for
English Wikipedia), the risk of leaving Wikipedia drops to 0.7% for
users who have made 100 contributions. To study the potential effects
of toxic comments, we separately consider contributions that are
followed by a toxic comment and contributions that are not followed
by a toxic comment (see Methods and materials section for details).
We find that the risk of an editor leaving after a toxic comment is
consistently higher for all editions and regardless of the
contribution number, see Fig. 3. We provide an analysis of the
significance of these findings in Supplementary Fig. S4.
Fig. 3.
The probability of leaving Wikipedia after receiving a toxic comment
is substantially higher than might be expected otherwise. For all six
editions the probability of leaving declines with the number of
contributions. At the same time, this probability is substantially
higher after receiving a toxic comment than might be expected
otherwise. Dots are probability estimates and solid lines are the
best linear fit on a log-log scale.
Open in new tabDownload slide
The probability of leaving Wikipedia after receiving a toxic comment
is substantially higher than might be expected otherwise. For all six
editions the probability of leaving declines with the number of
contributions. At the same time, this probability is substantially
higher after receiving a toxic comment than might be expected
otherwise. Dots are probability estimates and solid lines are the
best linear fit on a log-log scale.
Agent-based modeling
As has been demonstrated above, toxic comments increase the
likelihood of editors abandoning Wikipedia. If enough editors leave,
this could potentially impede the progress of the project as a whole.
In order to estimate the potential impact of toxic comments, we model
users' behaviors by varying the toxicity of the environment, ranging
from a nontoxic environment, where the probability of a user leaving
follows the empirically observed nontoxic probability distribution,
PNnon (blue dots in Fig. 3), to a highly toxic environment, where the
probability of leaving corresponds to an empirically observed toxic
probability distribution, PNtox (red dots in Fig. 3). We also
consider a potential attack targeted at new users. In this scenario,
each user receives a toxic comment after their first and second
contributions, e.g. their probability of leaving after the first and
second contribution is defined by PNtox, and after that follows the
empirically observed PN.
For our modeling, we focus on a cohort of users who made their first
contribution between the 4,000th and 6,000th day from the first
recorded contribution to English Wikipedia in our dataset. We opted
for this timeframe as it reflects Wikipedia's current phase
characterized by a relatively consistent number of active editors.
This period follows the site's initial exponential growth and a
subsequent decline but comes before the anomalous increase in
activity due to the COVID-19 pandemic (see Discussion section for
details on these stages).
For our modeling, we employed an agent-based approach. Each day,
agents (representing users) join Wikipedia and make their first
contribution. The number of agents joining each day is equal to the
actual count of first-time contributors to English Wikipedia on that
particular day. After their first contribution, agents keep
contributing, following a Poisson process, i.e. in such a way that
the distance between two consecutive contributions, D, follows an
exponential distribution: D~Exp(l), where l is estimated from
empirical data. After each contribution, the agent's probability of
leaving the project is determined by the toxicity level, T, and the
empirically observed distributions PNnon and PNtox. In particular,
after N's contribution the user leaves the project with probability
T*PNtox+(1-T)*PNnon. If the toxicity level is 0, then the
probability of leaving follows the nontoxic distribution PNtox, and
if the toxicity level is 1, then the probability of leaving follows
the toxic distribution PNtox.
After the initial 2,000 days, no new agents join the project;
however, we continue to model the behavior of the remaining agents
for the subsequent 2,000 days, for which we have available empirical
data for comparison.
Our model generally reproduces the dynamics of user activity (Fig. 4
), though, as expected, it cannot account for a later
COVID-19-induced spike in activity. We find that an extreme level of
toxicity could effectively reduce the cohort to almost no users in
the long run, compared to the sustained numbers in a nontoxic setting
or as observed in the data. Additionally, targeted attacks on
newcomers have the potential to significantly decrease the number of
active users, posing a risk to the project. The detailed results of
our modeling, showing the effects of different toxicity levels on
user count, are presented in Supplementary Fig. S6.
Fig. 4.
High levels of toxicity and targeted attacks could significantly
reduce the number of active editors. Modeling results for a cohort of
editors making their first contribution during the relatively stable
phase of Wikipedia (shaded region in the inset). The model reproduces
the general dynamics of user activity (blue line) but, as expected,
cannot capture the COVID-19-related spike in activity. An extreme
level of toxicity (red line) could reduce the cohort to virtually no
active users, contrasting with a nontoxic environment (green line) or
actual activity (blue line). Targeted attacks on newcomers (orange
line) have the potential to significantly reduce the number of active
contributors.
Open in new tabDownload slide
High levels of toxicity and targeted attacks could significantly
reduce the number of active editors. Modeling results for a cohort of
editors making their first contribution during the relatively stable
phase of Wikipedia (shaded region in the inset). The model reproduces
the general dynamics of user activity (blue line) but, as expected,
cannot capture the COVID-19-related spike in activity. An extreme
level of toxicity (red line) could reduce the cohort to virtually no
active users, contrasting with a nontoxic environment (green line) or
actual activity (blue line). Targeted attacks on newcomers (orange
line) have the potential to significantly reduce the number of active
contributors.
Discussion
We conducted a large-scale analysis, covering all comments made on
user talk pages of the six most active language editions of Wikipedia
over a period of 20 years, and found that toxic comments are
associated with a decreased activity of editors who have received
these comments and an increased risk of them leaving the project
altogether. Additionally, via agent-based modeling, we showed that
toxicity attacks on Wikipedia have the potential to impede the
progress of the entire project.
The main limitation of our study is its relatively narrow scope, as
it focuses solely on the association between toxic comments left on
user talk pages and the subsequent decrease in users' activity.
However, this approach allowed us to formulate our findings with
precision and ensure their robustness. We believe that our study
complements and extends existing studies on Wikipedia and online
communities more broadly, and may serve as a foundation for further
exploration of the effects of toxicity, as we discuss in this
section.
Conflict on Wikipedia
Conflict on Wikipedia has already been a subject of numerous studies,
with particular attention given to so-called "edit wars" (11, 29, 30
). These arise when groups of editors, disagreeing about page
content, repeatedly override each other's contributions. It has been
estimated that edit wars can impose substantial conflict and
coordination costs on Wikipedia (12). Furthermore, it has been
demonstrated that these costs increase over time and a smaller
proportion of the total work by Wikipedians directly contributes to
new article content. Conflict could also undermine content quality.
For instance, the level of conflict on discussion pages, as assessed
by raters, has been shown to negatively correlate with the quality of
the corresponding Wikipedia articles (13).
In contrast to previous studies, our focus is on comments left on
user talk pages rather than article talk pages. While this narrows
the scope of our study, it also ensures that the comments we examine
are directly addressed to a specific editor. Our approach also
mitigates potential bias that could be introduced by the topic of an
article. For instance, comments on talk pages linked to articles
about violence might be misclassified as toxic by an algorithm due to
the presence of highly negative keywords.
It is possible that toxic comments we observe on user talk pages are
not independent from a broader conflict occurring elsewhere on
Wikipedia. Therefore, it is conceivable that the effect we observe is
not purely explained by toxic comments, but also by a broader
conflict which leads both to a toxic comment on a user talk page and
decreased activity of this user. Future research is needed to address
this limitation and explore the context in which toxic comments
occur.
It is worth noting, however, that it has already been established
that toxicity on its own could lead users to stop contributing either
temporarily or permanently, as this is what editors themselves report
in surveys (24). Our study complements such studies by providing an
estimate of the potential effects while also being performed on a
scale that is not achievable by survey methods.
Stages of Wikipedia life cycle
Wikipedia has not grown linearly but has instead passed through
several stages. It began with exponential growth (31), which
subsequently slowed (32). Following that, the number of active users
declined before Wikipedia entered its current stage, characterized by
a relatively stable number of active users (33), with a slow decline
observed in some language editions. A notable exception was a
temporary spike in activity due to the COVID-19 pandemic (34). See
Supplementary Fig. S5 for an illustration of these patterns in the
editions studied in this paper.
It has been found that the main reason for halted growth is a sharp
decline in the retention of newcomers (35). Specifically, with the
project's development, the rejection of newcomer contributions has
increased, demotivating them and driving them away. Our results
complement these findings by highlighting that newcomers are also
particularly vulnerable to toxic comments. If users receive a toxic
comment after their first or second contributions, their chances of
continuing to contribute are 1.8 times lower compared to users who
did not receive toxic comments.
Diversity of editors
Wikipedia is often considered a neutral and unbiased source of
knowledge. In fact, this is ingrained in its "Neutral point of view"
policy, which is officially one of the five fundamental principles of
Wikipedia (36). However, the claim of neutrality should not be
accepted uncritically (37). For instance, while Wikipedia mandates
that its content is supported by reliable sources, the selection of
these sources can significantly deviate from the norms of the expert
knowledge community, introducing biases to Wikipedia content (38).
Even if the content of articles is neutral, their coverage may be
biased. It is well documented, for example, that biographies of women
are underrepresented on Wikipedia (39). Wikipedia's own rules might
contribute to such biases. For instance, providing reliable sources
as required by Wikipedia for biographies of women might be
challenging because fewer sources exist on women due to historic
inequalities (40). Another case in point is the Oral Citations
project, which aimed to use oral citations for content on countries
that are underrepresented in other sources (41). However, this
initiative was met with opposition by the English Wikipedia
community.
These content biases are closely connected to the lack of diversity
among editors (38, 42). While estimates vary, the vast majority of
Wikipedians are men (43). Notably, Wikipedia did not achieve its own
goal of having at least 25% women editors by 2015 (44). This
shortfall is a significant concern for the project, as diversity can
improve the quality of content and reduce its biases (13, 45). While
multiple barriers confront women editors on Wikipedia (40, 46, 47),
toxicity is likely to be one of key factors contributing to the
observed gender imbalance. Specifically, research has shown that
while men and women are equally likely to face online harassment and
abuse, women experience more severe violations (48). They are also
more likely to be affected by such incidents and to self-censor in an
attempt to prevent potential harassment (48). This has been confirmed
in the Wikipedia context as well, where it has been demonstrated that
the psychological experiences of women and men editors differ,
leading to higher attrition rates among women (49). Similar results
were found in another survey (24), showing that women experiencing
toxicity are more likely to stop contributing in the future.
Overall, there are reasons to believe that toxicity might
significantly undermine the diversity of Wikipedia editors, which
can, in turn, compromise the quality of Wikipedia articles and
introduce biases in its coverage. This underscores the importance of
our findings. While most of the existing studies focus on the gender
gap, we want to emphasize that the Wikipedia diversity problem goes
beyond that, including racial, nonbinary, and other biases as well (
50-52). For instance, we observed that many of the toxic comments in
our data set include ethnic slurs. Future studies are needed to
better understand the experiences of minority groups on Wikipedia and
the effects that toxicity has on them.
Interventions
The Wikipedia community is well aware of the aforementioned problems,
and there have been multiple efforts to address them through various
interventions. Research into reward systems showed that while they
might work effectively for already highly productive editors, they
fail to motivate less active editors (53). Another study found no
significant effect of positive rewards in online communities (54).
To address the gender gap in Wikipedia content, numerous events
dedicated to creating entries about women were organized (46). An
analysis of such interventions, which focused on two popular feminist
interventions, confirmed that they succeeded in introducing content
about women that would otherwise be missing (55). However, there is
still a need to address the gender gap on a more systematic and
sustainable level. For instance, one study showed that most of the
women activists who attended editing workshops later chose not to
continue contributing to Wikipedia, citing safety concerns as their
primary reason (46). This issue was echoed in another study which
identified safety as a core concern for women editors (56).
A suggested solution to this problem has been the red-flagging of
harassment and harassers (46). However, the opinion that toxic
comments are negligible and should be seen as merely
over-enthusiastic participation is still present among editors (25).
Furthermore, various anti-harassment measures have been declined
multiple times by the community, as they were seen to slow the
process of content creation (57, 58). Based on our findings, we
believe there is a need to reevaluate these policies, and more
research attention is required to understand the impact of potential
interventions.
The wider role of peer-production systems
Wikipedia plays a crucial role in the global information
infrastructure, aiming to provide millions of people with access to
free, unbiased knowledge. Due to its reputation as a neutral and
comprehensive information source, it has become a trusted first
choice source of knowledge for many and its articles frequently
appear in top search engine results (59, 60). In fact, studies have
shown that Google search results rely heavily on Wikipedia, and the
quality of these results significantly diminishes without Wikipedia (
61). Beyond search engines, Wikipedia was shown to be valuable to
other online communities such as Stack Exchange and Reddit (62).
While Wikipedia is arguably the most successful peer-production
system, it is certainly not the only one. Others include hundreds of
wikis hosted by Fandom, the numerous question-and-answer communities
of Stack Exchange, and various other platforms ranging from online
maps to online learning (33). Interestingly, for these projects, the
same patterns that are typical of Wikipedia have been observed (63),
i.e. the initial growth in number of contributors is followed by a
decline characterized by a decreased retention of newcomers. This
suggests that our findings might have broader implications for
large-scale collaborative projects and online communities. It
emphasizes the need to promote healthy and sustainable communication
practices to protect crucial online information ecosystems and ensure
their long-term success.
Methods and materials
Data and preprocessing
Comments on user talk pages
The Wikimedia Foundation provides publicly accessible dumps of all
the different wikis' content.^a These dumps are updated on a regular
basis, with complete revision history dumps generated once per month.
For this paper, we used the English dump from 2021 November 1, the
German dump from 2022 August 1, the French, Italian, and Spanish
dumps from 2022 August 1, and the Russian dump from 2022 July 1. The
data was obtained from a mirror hosted by the Umea University,
Sweden.^b
From the dumps, the user talk pages were extracted. A user's talk
page is a place where other editors can communicate with the user
either on more personal topics or to extend their discussion from an
article talk page. When the comments left on the talk page are
resolved or become too old, users can choose to archive them. This
helps them keep better track of new incoming topics. Once archived,
the old comments are not displayed on the talk page anymore but are
rather linked in a subpage. Nevertheless, the entire history of the
user talk page, as of any other page on Wikipedia, can be fully seen
under the tab of revision history. The revision history records one
entry for every edit made on the page saving each time the complete
content of the page. Thus retrieving a single comment requires
performing the difference between two consecutive revisions. The
Wikimedia API does offer a method to compute the difference between
two revisions, however, applying it on a scale that was necessary for
this research was unfeasible. For that reason, we developed our own
parser to extract comments as a difference between two versions of
the page (64).
We excluded from our analysis talk pages that belong to unregistered
users, e.g. users who are represented only by an IP address rather
than a user name, because IP addresses are dynamic and it can not be
assumed that one address represents a single user throughout
Wikipedia history. Additionally, we have excluded comments made by
officially registered bots. Comments that were made by users on their
own pages are also not considered.
When extracting comments, we cleared wiki-specific formatting and
HTML markup, i.e. removed links, attachments, or other
formatting-specific sequences irrelevant to the actual content.
Contributions and active days
In order to extract information on users' contributions, i.e. edits
of Wikipedia pages made by them, we used the MediaWiki API to
retrieve timestamps for each edit made by a given user. The resulting
data set is publicly available in the project repository (64). The
timestamps of contributions were then converted into active days.
Specifically, each user i was represented as a binary vector ui=
(ai1,ai2,...,aiN), where aid=1 if user i made at least one
contribution, i.e. edited a Wikipedia page, within the 24-h period
corresponding to day d and aid=0 otherwise. N is the number of days
between the first recorded contribution in our data set and the last.
The conversion from contribution count to active days was performed
because it is hard to interpret and compare the total number of
contributions between users as one large contribution could be
equivalent to multiple smaller ones. Additionally, the size of a
contribution does not necessarily reflect the effort put into it.
While being active on a given day could still mean different levels
of activity for different users, it represents a certain level of
engagement with the project and is substantially different from not
contributing at all on a given day.
Toxicity
The automatic detection of offensive language in online communities
has been an active area of research since at least 2010 (65). Over
the past decade, researchers have focused on detecting
closely-related and intersecting types of offensive language such as
toxicity, abusive language, and hate speech (66), see (67) for an
overview of recent advancements in the field. In this paper, we use a
model from the Perspective API (68) to identify toxic comments. This
is a state-of-the-art toxicity detection algorithm that obtained
competitive results at OffensEval-2019 competition (69) without any
additional training on the contest data and is often used as a
baseline system for toxicity detection (66). Perspective API is used
across multiple platforms, including The New York Times, Der Spiegel,
Le Monde, and El Pais. It uses BERT (Bidirectional Encoder
Representations from Transformers) architecture (70) and is trained
on comments from a variety of online sources, including Wikipedia.
Each comment is labeled by 3-10 crowdsourced raters. Perspective
models provide scores for several different attributes, see
Supplementary Table S2 for the list of attributes and their
definitions, see Supplementary Table S2 for examples of toxic
comments, and see Supplementary Table S3 for the AUC (Area Under the
Curve) scores for those languages and attributes that were used in
this paper.
We define a toxic comment as a comment that has a score of at least
0.8 on any of the six dimensions provided by Perspective API. The 0.8
score means that on average 8 out of 10 raters would mark it as
toxic. As this threshold can be considered arbitrary, we perform
additional robustness checks using different toxicity thresholds. In
particular, we compute activity loss not only for the threshold of
0.8 (Table 1) but for thresholds from 0.2 to 0.9. Additionally, we
applied different activity filters, e.g. we separately compute an
estimate only for those users who were active at least X days in the
past 100 days where X varies from 0 to 50. This is done in order to
ensure that the results are not exclusively driven by those users who
had made few edits and then stopped contributing to the project. We
perform this analysis for English Wikipedia as it is the largest
edition. As shown in Supplementary Fig. S1, the estimate is typically
in the range from -0.5 to -2 and significantly lower than zero for
all activity thresholds and all toxicity thresholds higher than 0.3.
Similarly, we have checked how the toxicity threshold affects the
probability of leaving the project. As might be expected, results
remain qualitatively the same for different toxicity thresholds but
higher thresholds lead to more extreme results, e.g. the probability
of leaving after a toxic comment with 0.9 score is even higher than
after a toxic comment with toxicity score of 0.8 (Supplementary Fig.
S3).
We also evaluated the robustness of our results with respect to
misclassification errors. To achieve a realistic distribution of user
activity, we repeatedly sampled 100,000 editors and their activity
histories from the English Wikipedia data set. These sampled users
were then divided into two groups: treatment and control. We
investigated two distinct scenarios: one involving an equal split
between the treatment and control groups and a second, more
realistic, scenario where the treatment group constituted 1% of the
control group.
In the treatment group, we randomly removed one active day from each
user, thereby generating a true effect of one lost active day per
user. We then introduced misclassification errors by generating false
positives (moving users from control to treatment group) and false
negatives (moving users from treatment to control group). Finally, we
compared the estimated effect, as a function of the error rate, with
the true effect.
We find that, generally, misclassification leads to the
underestimation of the true effect, becoming more pronounced with
higher error rates (Supplementary Fig. S2). The only exception is in
the case of false negatives, i.e. undetected toxic comments, in the
realistic scenario. Here, misclassification does not significantly
bias the estimate, though it does increase its variance.
Perspective API accepts texts up to 20,480 bytes. As the majority of
comments are well below this limit, we have excluded those that are
larger.
Activity loss
Users who have received at least one toxic comment constitute our
treatment group. For each user in this group, we select a random
toxic comment they have received. We then center user activity around
the timestamp, titox, of that toxic comment and convert the result
to active days by calculating
sign(|{t[?]Ti:t[?][titox+d*24*60*60,titox+(d+1)*24*60)}|),
where Ti is the set of timestamps of all contributions made by user i
, and d is a day ranging from -100 to 100. Finally, the results are
averaged over all users. We repeat the procedure of selecting a
random toxic comment 100 times and report average results. However,
since most users received only one toxic comment, there is little
variation across simulations and the average over 100 simulations is
almost identical to the result of a single simulation.
We then compare these results with a control group comprised of users
who did not receive any toxic comments. However, a direct comparison
is complicated because users who have received a toxic comment are,
on average, more active than those who have not. This is probably due
to the fact that each contribution could lead to a toxic response
with a certain probability. Hence, the more contributions a user
makes, the higher the likelihood of receiving a toxic comment and
thereby being in the treatment group.
Specifically, if each contribution can lead to a toxic comment with a
probability p, then the probability of receiving at least one toxic
comment depends on the number of contribution, N: P(gettoxiccomment)=
1-(1-p)N(1).
To ensure our control group is similarly active as the treatment
group, we randomly select users with a probability based on the
number of their contributions using formula (1). Users selected in
this manner form the control group. For these users, we then pick a
nontoxic comment at random, center their activity around its
timestamp, and follow the same procedure used for the treatment
group.
To test for the significance of the results, we compute 95%
bootstrapped confidence intervals for each estimate.
Probability of leaving
For each toxic comment, we find the closest in time contribution that
precedes that comment. We define such contributions as "contributions
followed by a toxic comment" and compare the probability of leaving
after such contributions with the probability of leaving after other
contributions. The probability of leaving after N contributions is
estimated as a fraction of users who have made exactly N
contributions among users who have made at least N contributions. As
the probability of leaving strongly depends on N, we make a
comparison separately for each contribution number N[?][1,100]. For N>
100 the number of users is too small to provide reliable estimates
for comparison.
a
https://meta.wikimedia.org/wiki/Data.dumps [accessed on 2023 January
20].
b
https://mirror.accum.se/mirror/wikimedia.org/
Acknowledgments
We acknowledge the Master's thesis by Bruckner (71), which identified
a potential pattern in data and provided an inspiration for the
design of the study presented in this paper. The initial data
collection and experiments were carried out as part of Camelia
Oprea's Master's thesis (72). We thank Liubov Tupikina and David
Garcia for their valuable discussions regarding the results presented
in this article. We thank the anonymous reviewers for their
insightful comments and suggestions.
Supplementary Material
Supplementary material is available at PNAS Nexus online.
Funding
The publication of this article was funded by the University of
Mannheim.
Author Contributions
I.S., C.O., and M.S. designed the study; I.S. and C.O. collected and
analyzed the data; I.S., C.O., and M.S. wrote the manuscript; I.S.
revised the manuscript.
Previous Presentation
These results were previously presented at International Conference
on Computational Social Science 2023.
Preprints
A preprint of this article is published at https://doi.org/10.48550/
arXiv.2304.13568
Data Availability
The data underlying this article is available in Open Science
Framework at https://osf.io/2qyxj/.
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