Volunteer Contributions To Wikipedia Increased During Covid 19 Mobility Restrictions
Summary
A research article, Volunteer contributions to Wikipedia increased during COVID-19 mobility restrictions, by Thorsten Ruprechter, Manoel Horta Ribeiro, Tiago Santos, Florian Lemmerich, Markus Strohmaier, Robert West and Denis Helic, published in Scientific Reports (2021) 11:21505. The authors analyze 223 million edits made from 2018 to 2020 across twelve Wikipedia language editions, grouped as large, medium and small. Using difference-in-differences regressions, they report significant increases in edit volume for all large and most medium Wikipedias and more newcomers in large Wikipedias. They report that English Wikipedia contributions rose by over 20% against pre-pandemic expectations, while Finnish was the only edition showing a significant decrease over longer stretches. The article closes with acknowledgements, author contributions and a license statement.
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“Volunteer Contributions To Wikipedia Increased During Covid 19 Mobility Restrictions.”
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OPEN Volunteer contributions
to Wikipedia increased
during COVID‑19 mobility
restrictions
Thorsten Ruprechter1*, Manoel Horta Ribeiro2, Tiago Santos1, Florian Lemmerich3,
Markus Strohmaier4,5,6, Robert West2 & Denis Helic1
Wikipedia, the largest encyclopedia ever created, is a global initiative driven by volunteer
contributions. When the COVID-19 pandemic broke out and mobility restrictions ensued across
the globe, it was unclear whether contributions to Wikipedia would decrease in the face of the
pandemic, or whether volunteers would withstand the added stress and increase their contributions
to accommodate the growing readership uncovered in recent studies. We analyze 223 million edits
contributed from 2018 to 2020 across twelve Wikipedia language editions and find that Wikipedia’s
global volunteer community responded resiliently to the pandemic, substantially increasing both
productivity and the number of newcomers who joined the community. For example, contributions to
the English Wikipedia increased by over 20% compared to the expectation derived from pre-pandemic
data. Our work sheds light on the response of a global volunteer population to the COVID-19 crisis,
providing valuable insights into the behavior of critical online communities under stress.
Wikipedia is the world’s largest encyclopedia, one of the most prominent volunteer-based information systems in
existence1,2, and one of the most popular destinations on the Web3. On an average day in 2019, users from around
the world visited Wikipedia about 530 million times and editors voluntarily contributed over 870 thousand edits
to one of Wikipedia’s language editions (Supplementary Table 1).
Amidst the COVID-19 pandemic and the “infodemic”4 that ensued, Wikipedia played and continues to play
an important role in supplying information about the COVID-19 crisis5–8. Notably, although readers accessed
medical articles more frequently than in non-pandemic times9, the increase in readership for all kinds of arti-
cles—not only those related to the pandemic—suggests that Wikipedia’s role in this time of crisis transcends
mere COVID-19-related information seeking10. However, page views are but a single aspect of the pandemic’s
impact on Wikipedia, which ignores the fundamental contribution of editors who perform unpaid volunteer
work to maintain and develop content on the website. If the pandemic negatively impacted the productivity and
number of editors on Wikipedia, the world’s largest online encyclopedia could be in peril11,12.
We can devise two competing hypotheses on how the COVID-19 crisis may have impacted editors on Wiki-
pedia. First, editing activity on Wikipedia may have declined in response to COVID-19. The negative economic
and social ramifications of the pandemic13–15 that ensued after governments enforced mobility restrictions16–18
may have adversely affected Wikipedia volunteers. Particularly, the challenges associated with this new reality
may have led to an overall decrease in contributions and fewer people joining Wikipedia. For example, potential
contributors might have focused their efforts on personal issues and on coping with the crisis rather than volun-
teering for Wikipedia. Alternatively, volunteer contributions and the number of newly recruited editors may have
increased due to mobility restrictions resulting in individuals spending more time at home in front of computer
screens19 or on the Internet20. Moreover, as previously observed during locally confined disease outbreaks21 and
extraordinary events22, the Wikipedia community could have responsed to the heightened demand for high-
quality information with increased volunteer activity.
Similar to the importance of understanding the response to COVID-19 in other societal or economic
contexts14,15,23,24, we believe that investigating whether volunteer contributions to Wikipedia decreased or
1
Graz University of Technology, 8010 Graz, Austria. 2EPFL, 1015 Lausanne, Switzerland. 3University of Passau,
94032 Passau, Germany. 4RWTH Aachen University, 52062 Aachen, Germany. 5GESIS – Leibniz Institute for the
Social Sciences, 50667 Cologne, Germany. 6Complexity Science Hub Vienna, 1080 Vienna, AT, Austria. *email:
ruprechter@tugraz.at
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Figure 1. Edit volume in the English Wikipedia increased during COVID-19 mobility restrictions. We visualize
the rolling 7-day average edit volume in the English Wikipedia from January to October 2020 alongside the
daily mean of 2019 and 2018, only considering non-bot edits to Wikipedia articles. Vertical lines mark major
developments during the COVID-19 pandemic in 2020 (Via https://wikimediafoundation.org/covid19/data).
After the first Western countries (e.g., Italy) enforced mobility restrictions in early March, edit volume stagnated
briefly before rising sharply—a trend that prevailed until late May, where the maximum difference in rolling
7-day average edit volume reached 20,970. Although this initial sharp increase in edits later declined, a surplus
persisted until October. Until September 31st, editors produced 8.4% (7.3%) more edits in 2020 than in 2019
(2018), an increase of 2.2 million (2 million) edits (Supplementary Table 2). Much of this edit surplus appears to
stem from periods of mobility restrictions in the spring of 2020.
increased during this pandemic is critical to assessing the online encyclopedia’s ability to continuously provide
information to a global audience of readers, even during worldwide disasters.
After careful quantitative analyses of large-scale edit logs on Wikipedia, we present robust evidence that
volunteer contributions significantly increased during the COVID-19 crisis across many language editions. During
the pandemic, the Wikipedia editor community not only generated many more edits than what we would expect
given historical baselines, but also acquired many more newcomers than in recent history, demonstrating the
resilience of this online community in the face of adverse conditions.
Figure 1 depicts the increase in volunteer edits in the English Wikipedia during the COVID-19 timeline in
2020 compared to previous years. Whereas no increase in edit volume was apparent in early 2020, the mobility
restrictions in Western countries seemed to first slightly dampen edit activity, before triggering a strong upward
trend towards the end of March. In the weeks thereafter, a considerable edit surplus developed in comparison to
previous years, which lasted until its peak in late May. As the pandemic subsided over the summer, the growth
in edit volume also continuously decreased until fall. By October, the relative increase in edit volume, and thus
volunteer contribution, from 2019 to 2020 (about 7.9%, or 2.1 million edits) was about double that from 2015 to
2019 (about 4.2%, or 1.5 million edits; see Supplementary Table 2). In summary, a visual representation of edit
volume in the English Wikipedia suggests a considerable contribution surplus in 2020.
Beyond the mere descriptive analysis of a single Wikipedia language edition, we systematically analyzed a
varied sample of 12 Wikipedia language editions (“Wikipedias”), consisting of four large, medium, and small
language editions each (“Methods”), with over 223 million edits spread through 24.6 million articles. In accord-
ance with the descriptive analysis shown in Fig. 1, our quasi-experimental difference-in-differences analysis
finds a significant increase in edit volume after COVID-19 mobility restrictions came into effect for many of
the Wikipedia editions, and an influx of new editors that is particularly salient for larger Wikipedias. Our study
sheds light on the impact of the COVID-19 mobility restrictions on Wikipedia volunteer contributions and
provides a reusable framework to measure user activity under stress. More broadly, the evident increase in edit
volume and newcomers across most observed Wikipedias is a finding of interest not only to Wikipedia itself but
also to researchers and managers of other online collaboration systems, as it provides valuable insight into user
behavior during a global crisis.
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(a) (b)
Figure 2. Edit volume during COVID-19 mobility restrictions. We show edit volume findings in large (top),
medium (middle), and small (bottom) Wikipedias during COVID-19 mobility restrictions, which in the figure
we delineate using mobility (when restrictions become effective) and normality (when restrictions are lifted)
changepoints. (a) We show rolling 7-day average edit volume generated by human editors for 2018, 2019, and
2020 until October. After a slight retraction of editing around mobility changepoints in most Wikipedias, the
number of contributions recovers to previous levels within a few days. Editors contribute substantially more
in all large and some medium Wikipedias in the weeks after the mobility restrictions in 2020, compared to
historical baselines (Serbian Wikipedia experienced several days of high edit activity in May 2018 and February
2019, resulting in the data shown). (b) We depict the relative change in edit volume (ev) as retrieved from DiD
via δev (95% confidence interval as two standard deviations) and plot δev for 120 left-aligned 7-day windows (see
“Methods”), with the x-axis describing days after the respective mobility changepoint. We observe that editing
in large and medium Wikipedias significantly increases after their mobility changepoint, while most small
Wikipedias show neither significant increase nor decrease.
Results
Edit volume during COVID‑19 mobility restrictions. We observe an increase in edit volume (the
number of edits made by non-bot users) on Wikipedia during the period of COVID-19 mobility restrictions in
the spring of 2020, which is particularly evident in large and medium Wikipedias. Figure 2a depicts the rolling
7-day average edit volume for large (top), medium (middle), and small (bottom) Wikipedias in the context of
COVID-19 mobility restrictions, which we delineate via automatically detected mobility (i.e., restrictions take
effect) and normality (i.e., restrictions are lifted) changepoints (see “Methods”). We also report edit volumes for
2018 and 2019 as a reference for 2020. We observe substantial drops in edit volume around the mobility change-
point for almost all Wikipedias, indicating a shock to the Wikipedia ecosystem. In particular, larger Wikipedias
experience a considerable short-lived decrease in edit volume but are able to recover quickly. English, Italian,
German, French, Korean, and Japanese even clearly surpass their pre-shock volume levels, leading to an overall
edit surplus. On the contrary, some smaller Wikipedias (e.g., Finnish) exhibit a steady decline in edit volume
after the mobility changepoint. To better relate edit volume during the COVID-19 pandemic to reference values
from previous years and pre-pandemic periods, we employ a difference-in-differences regression (DiD) that
controls for the year, period, and language, as well as their interactions. For all Wikipedias, we compute the effec-
tive change in edit volume (ev) after the mobility changepoint from the three-way interaction of year, period,
and language, and denote this effective change as δev . We apply the DiD analysis to a sequence of 7-day windows
post-changepoint, always retaining the 30-day pre-changepoint period, and plot the time series of logarithmic
effects for edit volume according to δev in Fig. 2b. We describe this DiD setup in more detail in “Methods”. The
DiD analysis validates that all large and most medium Wikipedias significantly increase their edits following
the mobility restrictions according to δev (95% confidence interval), while no general statement can be made for
small Wikipedias.
For the rolling 7-day average edit volume in large Wikipedias, we identify an upward trend in 2020 imme-
diately after mobility restrictions took place (Fig. 2a, top). In the English, French, and Italian language editions,
edit volume steadily increases for nearly 2 months after a dip around the date of the mobility changepoint, before
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slowly reverting to prior levels. The steady initial increase in edit volume leads to high peaks—approximately
120,000 edits for English, 28,000 for French, 15,000 for Italian, and 24,000 for German, which exhibits a decline
back to pre-crisis levels earlier than other large Wikipedias. DiD results confirm the edit volume surplus visible in
the time series for large Wikipedias in 2020 (Fig. 2b, top). δev for French, Italian, and English depicts an immediate
relative increase in edits after the mobility restrictions take place, leading to over 100 days of significant increases
for all three of these Wikipedias, whereas German declines earlier. Approximately 35 days after the mobility
restrictions take effect, French (e0.337 = 144%, a surplus of 44%), Italian (+42%), and German (+25%) reach
their highest significant relative increase for edit volume. The higher short-term increases in French, German,
and Italian may be related to more detailed reporting of local issues in these language editions. On the contrary,
English shows a longer, sustained upward trend for δev , with a maximum significant increase of 23% after 69 days.
In conclusion, edit volume significantly increases in large Wikipedias after mobility restrictions come into effect.
Edit volume in most medium and small Wikipedias slightly drops around the respective mobility change-
points in 2020. However, virtually all Wikipedias quickly recover from the initial shock, with most maintaining
a stable edit volume in the ensuing weeks and some even generating an edit surplus. While Fig. 2a (middle)
shows that medium Wikipedias do not homogeneously increase their edit volume, Korean and Japanese surpass
their pre-mobility-restriction levels about a month post changepoint, peaking at about 5400 and 14,500 edits,
respectively. For small Wikipedias, edit volume only decreases slightly right after the mobility changepoint
(Fig. 2a, bottom). Afterward, edit volume recovers to previous baselines within 30 days, before following similar
trends and levels as in previous years. DiD analysis and corresponding values for δev reveal that, in fact, medium
Wikipedias experience varying periods of significant relative increases in edit volume (Fig. 2b, middle). For
example, when compared to pre-pandemic years around the same time period, the Korean and Dutch Wikipe-
dias produce a consistent relative increase (peaking at +40%), whereas Swedish and Japanese exhibit shorter
significant periods (+30% and +38% in maximum, resp.). Furthermore, the relative change for small Wikipedias
(Fig. 2b, bottom) signals brief periods of substantial relative increases for Danish and Norwegian (peaks of +69%
and +43%, resp.). Most notably, Serbian Wikipedia exhibits a considerable increase during the first month after
mobility restrictions take place, with volume nearly tripling (logarithmic effect of 1.03). Lastly, we note that out
of our twelve investigated Wikipedias only Finnish shows a significant decrease in δev over longer stretches of
the observed period. In any case, small and medium Wikipedias are mostly resilient to the initial shock to edit
volume triggered by COVID-19, with some even surpassing their pre-pandemic baselines after a few weeks.
Newcomers during COVID‑19 mobility restrictions. We find that all large and medium Wikipedias
acquire considerably more newcomers (the number of registered users who made their first edit) for most of the
study period, while the remaining Wikipedias exhibited resilience and do not decrease their levels significantly.
We visualize the 7-day rolling averages for newcomer counts during the COVID-19 pandemic for large (top),
medium (middle), and small (bottom) Wikipedias in Fig. 3a, while also showing values for previous years as
well as mobility and normality changepoints. Newcomer counts plummet around the mobility changepoint, in
particular for large Wikipedias, but this attenuation in newcomer recruitment only persists for a brief period.
Shortly thereafter, newcomer counts increase considerably in all but a few medium and small Wikipedias (e.g.,
Swedish or Finnish). Again, we build a DiD model for newcomers (nc) to quantify effective changes during the
period of COVID-19 mobility restrictions in spring 2020, controlling for year, period, and language. We again
perform our DiD analysis for a sequence of 7-day windows after the mobility changepoint (see “Methods”) and
show the logarithmic effects for newcomers (δnc ) in Fig. 3b. This newcomer DiD analysis confirms that while all
large Wikipedias acquire significantly more new editors after mobility restrictions take effect, some medium and
small Wikipedias seem to be resilient and exhibit no significant long-term changes (95% CI).
Large Wikipedias appear to recover rapidly from the initial negative effect of mobility restrictions in terms
of newcomer counts (Fig. 3a, top). Most notably, Italian Wikipedia registers a newcomer surge until late April,
recruiting over 150 newcomers on a rolling 7-day average. English and French show similar patterns of perpetual
increases, reaching respective peaks of approximately 2100 and 330 new editors. Although German exhibits
nearly 200 newcomers shortly after the mobility changepoint, the surplus in 2020 seems not as considerable
as for other large Wikipedias. We further note that newcomer counts for large Wikipedias start to steadily
decline in May. However, this seasonal trend also appears to be prevalent in previous years. Our DiD analysis,
which captures the change in newcomers via δnc , for the most part confirms these findings (Fig. 3b, top). Dur-
ing the first 2–3 weeks past the mobility changepoint, large Wikipedias steadily recover from the COVID-19
shock without significant overall gains according to δnc . However, right after this recovery phase significant
peaks arise for English (e0.283 = 130% of previous levels), French (138%), and German (139%). For the Italian
Wikipedia, which belongs to a region with particularly strict mobility restrictions, we confirm an even stronger
newcomer surge, leading to a 80% relative increase. Furthermore, English generates a notably stable, significant
long-term growth in newcomers that is possibly owed to editors from all over the world joining this language
edition during mobility restrictions in their regions, as the English Wikipedia serves as a global repository of
knowledge. Ultimately, positive effects prevail for large Wikipedias and solidify a newcomer surplus after the
mobility restrictions come into force.
Similar to large Wikipedias, most medium and small Wikipedias experience a decline in newcomers right
around their mobility changepoints before then increasing their counts to previous baselines (Fig. 3a, middle
and bottom). Some of these Wikipedias (e.g., Norwegian, Finnish, Danish, Swedish) recover to previous levels
within the first month and exhibit no long-term effects afterwards. However, others recruit a surplus of new-
comers during this crisis. Japanese, Dutch, Korean, and Serbian show short-term newcomer influxes about 1–2
months after the initial mobility restrictions take effect, with maximum respective values of approximately 170,
60, 50, and 30 daily newcomers. We also observe these effects in δnc as captured by DiD (Fig. 3b, middle and
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(a) (b)
Figure 3. Newcomers during COVID-19 mobility restrictions. We visualize newcomer results in large (top),
medium (middle), and small (bottom) Wikipedias during COVID-19 mobility restrictions, which we delineate
via mobility (when restrictions become effective) and normality (when restrictions are lifted) changepoints.
(a) We depict rolling 7-day average newcomer counts until October of 2018, 2019, and 2020. For many
Wikipedias, newcomer acquirement strongly declines right around their mobility changepoint, but then quickly
rises to or even exceeds pre-pandemic baselines. (b) We investigate the relative change in newcomers (nc) via δnc
as computed from DiD analysis (95% confidence intervals as two standard deviations) and plot δnc for 120 left-
aligned 7-day windows (see “Methods”), starting with the respective mobility changepoint. In large Wikipedias,
considerably more newcomers join in the weeks after mobility restrictions come into effect, relative to before
the changepoint and previous years. Results for medium and small Wikipedias are non-conclusive, with some
showing increases in the number of newly acquired editors and others not significantly changing their values.
bottom), which confirms brief relative increases for Japanese (+30%), Dutch (+47%), Korean (+28%), and Ser-
bian (+179%). Finally, the newcomer DiD analysis corroborates that some medium and most small Wikipedias
do not significantly deviate from baselines prior to the mobility restrictions over much of the observed time span.
Discussion
As the COVID-19 pandemic erupted on a global scale, it was unclear how this incisive event would affect
Wikipedia’s volunteer community. Over the course of the last few years, both human editing25 and newcomer
recruitment26 on Wikipedia have stagnated or even decreased (Supplementary Table 2). Accordingly, the pan-
demic could have accelerated the decline of the online encyclopedia as the hardships of this global crisis may
even further decrease volunteer activity. However, our study, in which we analyze 223 million edits from 12
Wikipedia language editions, reveals that the COVID-19 pandemic and its accompanying mobility restrictions
have substantially boosted volunteer activity on Wikipedia. By performing a difference-in-differences analysis,
we show that edit volume as well as the influx of newcomers has generally increased after COVID-19 mobility
restrictions went into effect. In what follows, we discuss the implications and limitations of this finding.
Mechanisms behind contribution growth. We observe significant increases in edit volume and new-
comers during the COVID-19 pandemic across multiple Wikipedias, making it their most active period in at
least the last 3 years. While our quantitative study sheds light on the extent of contribution growth, there are
several possible mechanisms behind this effect, which may or may not impact the collaborative structure of edi-
tor communities.
Firstly, Wikipedia received significantly more page views during the COVID-19 crisis10. The increase in edits
and newcomers may partially be due to the prior increase in Wikipedia readership, as a certain proportion of
readers turns into contributors because of various motivational factors27,28. In addition, we theorize that increased
screen time and Internet exposure19,20 during the mobility restrictions lead to Wikipedia readers spending more
time editing, possibly increasing the reader-to-editor turnover rate. Tracing the transformation of readers into
editors during this pandemic in more detail is a promising avenue for future work.
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Secondly, the increase in contributions may be due to the rapidly changing information and new knowledge
that the COVID-19 pandemic generates about the world. Past literature has suggested that Wikipedia growth is
constrained by the amount of knowledge available, as editors have already contributed most of the easily obtain-
able and verifiable information25. The fact that volunteers have been “running out of easy topics” to contribute to
has made it difficult for non-specialists to provide new content with little effort11. As the COVID-19 pandemic
dramatically changes the status quo of our world today, it is generating new knowledge about many fields and
thus may provide fresh opportunities for both novel and veteran editors to contribute to Wikipedia.
Moreover, the observed edit surplus may have been caused by the high-intensity activity of a core group of
editors rather than the broader editor population. We therefore investigate the number of editors active on any
given day according to their activity level: 1–4, 5–24, 25–99, or more than 99 daily edits (“Methods”). The DiD
analysis for editor counts depicts increases across all activity levels after mobility changepoints for all large and
most medium Wikipedias, while small Wikipedias show non-conclusive effects (Supplementary Figs. 1, 2, 3 and
4). This corresponds to an overall increase in active editors during the pandemic (Supplementary Fig. 5), indicat-
ing that the general editor population intensified its contribution during the COVID-19 pandemic.
On top of that, we detected a contribution disparity with respect to Wikipedia size, meaning that the smaller
Wikipedias we studied did not benefit to the same degree as larger or medium Wikipedias. The observed dis-
crepancy in edit and newcomer increases for large, medium, and small Wikipedias may stem from a difference
in community size and structure, or these Wikipedias’ specific rules11,29–32. This discrepancy could be further
reinforced by the culture and perception of what constitutes good knowledge representation in the different
Wikipedia language editions33. Moreover, the amount of content for certain topical categories diverges due to
cultural contextualization in different language editions34. Specifically, a strong (hypothetical) affinity for topics
not directly related to the pandemic (e.g., Sports) in medium or smaller Wikipedias might change the effect of
this crisis on their edit volume, in comparison to larger Wikipedias. As an example, in case such Wikipedia lan-
guage editions focused more on updating sports articles, edit volume would decrease more during the pandemic.
The magnitude of such an effect may further depend on a region’s more (e.g., Italy) or less strict (e.g., Sweden)
mobility restrictions. Future research may explore language-specific collaboration mechanisms in more detail,
for example by attempting to topically analyze Wikipedia contributions during the pandemic.
Lastly, in this study we investigate contribution growth in terms of the number of edits and newcomers, rather
than alternative measures such as the total sum of contributed or surviving content. To also touch on this aspect
of Wikipedia contributions, we coarsely analyze the contributed information in bytes during COVID-19 mobil-
ity restrictions (Supplementary Figure 9). While the surplus in contributed content largely does not match the
surplus found for edits and newcomers, we observe an increase for several languages after the imposed mobility
changepoints. Furthermore, we find no significant decreases for all Wikipedia editions. It might prove fruitful to
extend this quantitative analysis by examining the actual dynamics and structure of the contributed information.
Resilience of Wikipedia communities. Although we did not find the same surplus in contributions
across large, medium, and small Wikipedia language editions, volunteer communities in all studied Wikipedias
demonstrated resilience by quickly recovering from the initial negative impact of the pandemic on their con-
tributions. While slow response to negative events or other shocks causes severe problems in social-ecological
systems35,36, resilient systems are adaptable and manage to withstand such shocks, even bearing the capacity to
cross previous performance thresholds37—a behavior observed in this study. The strongest resilience and subse-
quent crossing of earlier thresholds in large Wikipedias during the pandemic may be partially explained by the
difference in community size38. For example, in larger communities it may not be as problematic that leaders are
limited due to the pandemic, as a greater number of other veteran members can take over their work. This con-
jecture borrows from critical mass theory, in the sense that a critical mass of core members is the fundamental
source of content27. Future research might investigate the aspect of Wikipedia resilience during the pandemic in
more detail, for example by considering threat rigidity38 or building a model39 that considers COVID-19 as an
attack on the community structure.
Revert rate during COVID‑19 mobility restrictions. The observed simultaneous increase in newcom-
ers and edits may indicate that the edit surplus was partially caused by first-time editors. Although past research
has suggested that new or one-time editors often produce high-quality contributions40, veteran editors or bots
would frequently completely undo (i.e., identity revert) these newcomer revisions, which represents a com-
mon behavioral pattern on Wikipedia11,26,41 that in turn generates further revisions. To investigate whether an
increase in reverts occurred, we performed a cursory analysis of the revert rate, which is defined as the ratio of
reverted edits to edit volume (see “Methods” and Supplementary Information). Supplementary Figure 6a visual-
izes the rolling 7-day average revert rate, while Supplementary Figure 6b plots the relative change in revert rate
as captured by a DiD analysis (“Methods”). It is noteworthy that we only detect a significant increase of the revert
rate in one language (Korean). By contrast, several Wikipedias exhibit significantly decreased revert rates shortly
after the mobility restrictions come into force. For example, the large Italian, French, and German Wikipedias
all show reduced revert rates by about one quarter. This suggests that less valuable revisions, possibly made
by newcomers, and their immediate reversal do not cause the reported increase in edit volume. Furthermore,
potential misbehavior or conflict on Wikipedia, such as vandalism or edit wars, is prominently characterized
by large numbers of identity reverts, as they undo these unwanted contributions42–46. Therefore, reduced revert
rates may indicate that editors refrain more from confrontational behavior and thus demonstrate higher levels
of solidarity during the pandemic, which is a common phenomena within collectives during crises47. However, a
decline in revert rate could also imply that bots and administrators may be unable to keep up with the influx of
edits, leaving low quality or malicious edits undetected and thus diminishing quality in the long term. Moreover,
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differences in revert rates across Wikipedia editions could also be explained by divergent page protection and
blocking policies, where either certain groups of editors or even all public editors are prevented from editing
articles that are subject to increased attention. We see the detection and analysis of behavioral patterns and
collaborative structure of online communities as a promising path for future research. Additionally, it may be
valuable to further study the treatment and retention of newcomers26,30,48 during and after the pandemic once
more longitudinal data is available.
Contribution to COVID‑19 articles. One might speculate that the increase in edit volume is mostly due
to edits in articles that are strongly related to COVID-19. However, many of those articles were protected from
public editing early in the pandemic to prevent spread of misinformation49, and we find that only a negligibly
small fraction of edits (at most 1% for most Wikipedias) goes towards articles with a primary focus on COVID-
19 (see “Methods”) between January 1st and September 31st 2020 (Supplementary Table 4; Supplementary Fig. 7).
A clear outlier in that regard is German, where 2.5% of edits performed in 2020 by the end of September concern
themselves with such articles. This may indicate higher coverage of local COVID-19 outbreaks in German than
in other languages. We consequently repeat our DiD analysis for edit volume, this time excluding edits to articles
strongly related to COVID-19 (Supplementary Fig. 8). The results support the previous findings and confirm
that the reported edit volume increase is not due to COVID-19 articles. In this way, our work extends previous
studies, which focused on a smaller subset of pandemic-related articles7,49.
Other limitations. Even though our work covers a large portion of Wikipedia’s content and editor popu-
lation, it comes with several limitations. First, we do not consider a variety of different Wikipedias associated
with languages widely spoken in the Global South, including Spanish, Portuguese, Arabic, Hindi, or any African
Wikipedias (see “Methods” for how we chose language editions). Future work analyzing these Wikipedias could
improve our understanding of the impact of the pandemic on volunteer contribution in other parts of the world.
Second, content on Wikipedia is predominantly edited by white males between the ages of 17 and 4050,51. It may
be that the COVID-19 crisis has disparately impacted contributors of less represented demographics, as certain
racial or socioeconomic groups are particularly disadvantaged by the pandemic23,24,52. In addition, bots have an
important role in the creation and management of Wikipedia content41,53. We excluded bots from our analysis
as we specifically focused on edits performed by human volunteers. Nevertheless, other studies may choose to
consider bot activities as valid contributions to Wikipedia.
In conclusion, our study provides evidence for a substantial surplus of volunteer contributions to multiple
Wikipedia language editions during COVID-19 mobility restrictions, which shines light on the resilience of
the Wikipedia community under times of stress. The methodological framework used in this work can easily
be adapted for similar domains. We believe that our work provides valuable insights into contributor behavior
on online platforms during the COVID-19 pandemic and illustrates a plethora of possibilities for future work.
Methods
Data procurement and preprocessing. We utilize the openly available MediaWiki history dumps
dataset54 to analyze a varied sample of 12 Wikipedia language editions (“Wikipedias”).
Wikipedia language editions. We investigate 12 Wikipedias (Supplementary Table 3), consisting of languages
primarily spoken in European countries that were exposed to the outbreak of COVID-19 in the spring of 2020,
as well as two Asian Wikipedias. Our choice of language editions takes into consideration: (1) the size of the
Wikipedia edition, (2) whether the language is spoken in relatively few countries, and (3) the mobility restric-
tions imposed in these countries—three criteria that are often very difficult to simultaneously satisfy. Overall,
we aim to capture relevant Wikipedias that represent different attitudes towards the crisis, preferably from lan-
guages easily attributable to a single country or region. Accordingly, our sample contains regions with strict
(e.g., Italian, Serbian, or French) and less stringent mobility restrictions (e.g., Japanese, Korean, or Swedish).
Although it can not be attributed to a single country and about half of the contributions come from editors who
are not based in English speaking regions55, we include the English Wikipedia because it is the largest language
edition and is considered a global project. We employ the number of edits in 2019 as a metric to categorize the 12
Wikipedias we studied as either large (English, French, German, Italian, with more than 5 million edits), medium
(Swedish, Korean, Japanese, Dutch, with 1.5 million to 5 million edits), or small (Serbian, Norwegian, Danish,
Finnish, with less than 1.5 million edits).
Our work only covers an arbitrary part of the Wikipedia ecosystem, as we have argued above. To allow exten-
sion of our analysis to other Wikipedia language editions or Wikimedia projects (e.g., Wikidata, Wikimedia
Commons, or Wiktionary), we make our code publicly available (see Code availability). As an example, we have
provided a demonstration in our GitHub repository for adapting our code pipeline to explore Wikipedia editions
covering large parts of Eastern Europe (Polish, Czech, Ukrainian, and Russian Wikipedia).
MediaWiki history dataset dumps. We retrieve the monthly updated MediaWiki history dataset dumps54 pro-
vided by the Wikimedia Foundation (WMF) and perform additional preprocessing before computing as well as
plotting our results. The denormalized MediaWiki history dumps are generated from the full history logs stored
in the WMF’s MediaWiki databases. During their generation, WMF’s automatic scripts reconstruct and enrich
user and page history with additional data, and also automatically validate the dumps to prevent errors. After
WMF’s preprocessing, the dataset contains fields with precomputed standard metrics, such as revert informa-
tion, bot users, number of user contributions, or time since a user’s last revision. Overall, each entry in the dump
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consists of 70 fields with event information. Fields are grouped into entities, bearing information about either
revision, page, or user.
Preprocessing. In the MediaWiki history dataset, we only consider edits to articles by excluding all pages not in
the Wikipedia article namespace (“ns0”), thus removing revisions to talk pages or other content. Furthermore,
we utilize corresponding dataset fields to distinguish human editors (anonymous or registered) from bots and
mark certain revisions as reverts. Moreover, we convert MediaWiki history timestamps from Coordinated Uni-
versal Time (UTC) to the timezone of the local Wikipedia language edition. For Wikipedias in which languages
can not be attributed to a single timezone (e.g., French), we choose the timezone with the highest volunteer
population for the given Wikipedia. We do not apply timestamp conversion for the English Wikipedia. Lastly,
we detect articles which are strongly related to COVID-19 via an algorithm by Diego Sáez-Trumper56, which
recognizes COVID-19 articles based on their Wikidata57 links to the main COVID-19 pages.
Metrics. To make sense of which exact data fields in the MediaWiki history dumps we utilize to compute our
metrics, please refer to the code repository (see Code availability).
Edit volume. We define edit volume as the number of daily revisions to pages in the article namespace (“ns0”)
by non-bot users (anonymous or registered).
Newcomers. For each Wikipedia language edition and day, we specify the amount of newcomers as the number
of registered editors which perform their first article edit in that language edition on the given day. Through
recognizing new editors by their first edit, we measure the exact day they become a contributor in a language
edition. Note that the number of daily registered users is generally much higher than the number of newcomers
as computed in this work. However, as our study aims to quantify volunteer contribution, we choose to identify
newcomers by their first actual contribution in a given Wikipedia.
Revert rate. Editors and bots revert article revisions to undo changes which they deem unwarranted. Fre-
quently, these reverts correct revisions which arise from conflicts, edit wars, or vandalism42. Additionally, litera-
ture shows that revisions by newcomers are more likely to be reverted than those of veteran editors26. For this
research, we only consider reverts to articles that undo all changes and subsequently create a new revision which
exactly matches a previous article version (i.e., identity reverts). We calculate the daily revert rate by dividing the
number of identity reverts (by humans or bots) by the number of non-bot edits on this given day. Correspond-
ingly, revert rate relates the amount of reverts to the amount of human contribution.
Daily editors by activity level. We measure daily active editors in a Wikipedia by counting the number of reg-
istered, non-bot users which perform revisions in the article namespace. In addition, to detect effects across the
editor population, we collect data for multiple activity levels, keeping count of how many editors perform 1–4,
5–24, 25–99, or more than 99 daily edits. In contrast to other metrics, we do not compute the number of daily
editors from the Wikimedia history dumps, but retrieve it via the Wikimedia REST API instead58.
Contributed information in bytes. As a parsimonious representation of the amount of information editors con-
tribute to Wikipedia, we measure the daily changes in bytes to all articles in each language edition by summing
up the difference in bytes between each revision and its parent revision. We include bot edits to account for the
automated correction of vandalistic content additions and removals, which would otherwise distort the results.
Although this metric does not provide information about the actual value (or longevity) of the contributions, it
does provide an overview of the editors’ workload on any given day.
Changepoint detection. We adopt the approach by Horta Ribeiro et al.10 to detect mobility and normal-
ity changepoints via Google and Apple mobility reports, as detailed in Supplementary Figure 10. The mobility
reports capture population-wide movement patterns based on cellphone location signals and specify, on a daily
basis, the percentage of time spent in variety of locations, for example residential areas, workplaces, or retail
(https://www.apple.com/covid19/mobility; https://www.google.com/covid19/mobility). Government-man-
dated lockdowns and self-motivated social distancing measures manifest themselves as sharp changes in these
mobility time series. To detect changepoints in mobility, the approach consists of a simple binary segmentation
algorithm59. For Wikipedias of languages widely spoken across many countries (e.g. English, German, etc.),
we determine a changepoint by aggregating mobility reports for the countries in which the language is official
with weights proportional to the population of each of these countries. Notice that the link between Wikipedia
and language editions is merely approximate—in particular for English, which is accessed from all over the
world. We use the changepoints at which mobility drops as heuristics for dates when people started spending
substantially more time in their homes and term them mobility changepoints. To detect normality changepoints,
we compute the point in time for which the future average mobility remains within a 10% band around baseline
levels before the initial mobility changepoint (defined as pre-pandemic mobility levels by Google and Apple).
For languages spoken across multiple countries, we maintain the same aggregation scheme as before. Compared
to choosing specific dates, this changepoint detection approach leads to more comparable treatments across dif-
ferent regions. Supplementary Table 3 summarizes the detected changepoints, which we also make available in
our code repository.
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While we solely utilize the normality changepoints as visual guidance in the figures, our employed causal infer-
ence strategy described below strongly relies on the mobility changepoints. Therefore, we perform a sensitivity
analysis with varying mobility changepoints for all main results. We vary the mobility changepoints by ±7 days
and also consider a distinct method of aggregation for languages spoken in multiple countries (Supplementary
Fig. 10). As the latter method yields a maximum difference of 5 days across all languages, we only report results
for robustness experiments using the 7-day difference.
Difference‑in‑differences setup. To compare values of metrics during the COVID-19 pandemic with
reference values from previous years and pre-pandemic periods, we employ a difference-in-differences regres-
sion (DiD). DiD analysis allows us to quantify changes in these metrics in multiple Wikipedia language editions
around times of region-specific mobility changepoints in early spring, while controlling for (long-term) tempo-
ral trends.
Our basic DiD equation models a dependent variable’s value (V) as a function of the independent variables
year (Y), period (P), and Wikipedia language (L), as well as their interactions. Year is a binary variable which
differentiates between pre-pandemic (2018 and 2019) and pandemic years (2020), whereas period encodes the
treatment period via a binary variable, in our case represented by the pre- and post-phases of the region-specific
mobility changepoints. Lastly, we model our 12 Wikipedia language versions with a categorical variable to control
for language-specific effects. To account for outliers and normalize regression results across various-sized Wikipe-
dias, we use logarithmic scales for V. Literature often refers to our setup, which uses three independent variables,
as “triple-difference” or “difference-in-difference-in-difference” estimators60,61. Mathematically, our DiD setup is:
V = β0 + β1 ⊤ L + β2 Y + β3 P + β4 ⊤ (Y L) + β5 ⊤ (PL) + β6 (YP) + β7 ⊤ (YPL) + ε (1)
We depict the 12 Wikipedia language versions as a vector of 11 binary indicators (L ). Scalar coefficients (β0, β2,
β3, β6) describe effects for the reference language (i.e., baseline). Coefficient vectors (β1, β4 , β5, β7 , printed in
bold) collect language-specific effects of non-baseline Wikipedias. Lastly, ε is the normally distributed residual.
Given this mathematical formulation, the coefficient β7 captures the change in V post mobility changepoint
relative to the baseline Wikipedia, after accounting for differences stemming from year or period alone (β4 and
β5, resp.). We therefore compute the effect of interest for all Wikipedias via summation of β6 and β7 . For each
Wikipedia, we term this effective change in V as δm, where m stands for the metric representing the dependent
variable.
Interpretation of DiD coefficients. We now elaborate in more detail on how to interpret the coefficients of our
DiD model. We model the categorical language variable via vector L containing 11 binary indicator variables
for the 12 Wikipedias. As is customary, the regression utilizes a “reference Wikipedia” baseline. In our setup,
we arbitrarily choose Danish as the baseline. Consequently, β1 describes the respective difference between the
baseline Wikipedia and the 11 non-baseline Wikipedias using indicator variables. Thus, adding β0 and β1 yields
the intercept of each language’s sub-model.
The binary year variable (Y) indicates whether a data point lies in 2020 (= 1) or in the previous two pre-
pandemic years (= 0 ), regardless of period. As Danish represents the arbitrary baseline, the corresponding
coefficient β2 is a scalar which describes the overall change between the pre-pandemic years (2018 and 2019)
and 2020 for Danish. For non-baseline Wikipedias, the interaction Y L models the language-specific effects for
the change in years relative to the baseline Wikipedia and is quantified by the corresponding coefficient vector
β4 . Therefore, the summation of β2 and β4 is equal to the effective overall difference of 2020 to the previous 2
years for all Wikipedias.
We model seasonal differences between pre- and post-changepoint windows via the binary period indicator
(P). The corresponding scalar coefficient (β3) measures the difference between before and after the mobility
changepoint over all years for the baseline. Consequently, PL and coefficient vector β5 describe the period effect
for non-baseline Wikipedias in relation to the baseline. Calculating the sum of β3 and β5 then gives the total
pre- and post-changepoint effects.
Lastly, the interaction between year and period (YP) enables our model to capture the change in V for the
baseline Wikipedia via β6, after accounting for change in Y (via β2) and P (via β3) alone. To measure this effec-
tive change for all Wikipedias, we employ the coefficient vector β7 of the three-way interaction YPL . While β6
describes the baseline’s effect, β7 contains the aforementioned change relative to the baseline Wikipedia. There-
fore, the sum of β6 and β7 captures the effective change in V for all Wikipedias. For a single Wikipedia and metric
m, we name this effect of interest δm. Correspondingly, δm describes language-specific post-changepoint effects
in 2020, as it excludes differences that are due to year or period alone.
Quantifying changes in volunteer contribution. Wikipedia is a dynamic ecosystem, in which edit
behavior and the amount of volunteer contribution can change rapidly—especially in times of turmoil. To track
these changes and detect short-, medium-, and long-term effects of mobility restrictions on volunteer contri-
butions, we fit our statistical model on different data-points obtained from the same longitudinal dataset. This
methodology, pioneered by Gelman and Huang62, allows us to observe trends rather than mere point estimates.
We compute our DiD analysis for a sequence of post-changepoint windows, always retaining the Wiki-
pedias’ pre-changepoint periods. For each language version, we choose a fixed 30-day period before the
respective mobility changepoint as the pre-changepoint baseline. As post-changepoint analysis intervals,
we then extract a sequence of 120 overlapping left-aligned 7-day windows starting with the changepoints.
Mathematically, we set the treatment period to days {n, n + 1, . . . , n + 6}, ∀n ∈ {0, 1, . . . , 119} . For each
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post-changepoint window n, we perform a separate DiD analysis across all languages using the retained base-
line periods. By doing so, each DiD analysis compares the week starting at day n after the language-specific
changepoint to the baseline periods. In this default setup, each of the 12 Wikipedias is represented by 37
data points for every year in the DiD regression (2018, 2019, and 2020), yielding a total of 1332 data points
( = (30 pre-changepoint days + 7 post-changepoint days) × 3 years × 12 Wikipedias ) for each of the 120
experiments. For each Wikipedia, we conservatively detect outliers via the Median Absolute Deviation (MAD)
approach63 with a threshold of 5 ∗ MAD from the monthly median and replace such outliers by the monthly
median. We then build a time series of the 120 DiD results using δm and approximate the 95% two-sided con-
fidence intervals (CI) as two standard errors. As robustness checks, we compute variations of our DiD experi-
ments with wider window size (14 days) and slightly varied mobility changepoint dates (±7 days) as described
in Supplementary Information (Supplementary Figs. 11, 12, 13, 14, 15, 16, 17, 18, 19). These results corroborate
the findings reported under “Results”.
Identifying assumptions of DiD estimators. Triple-difference and difference-and-difference models in general
underlie strong identifying assumptions. In particular, our methodology is based on the assumption that the
difference between treatment and control group would stay constant without an intervention64. Additionally,
the condition of parallel trend in slopes prior to the treatment must be fulfilled65. For our model, we therefore
assume that for each Wikipedia language version, the difference between treated (i.e., 2020) and control years
(i.e., 2018 and 2019) would remain constant in the absence of an intervention (i.e., mobility changepoint). Fur-
thermore, our model trivially fulfills the parallel trend condition: Given that our model only considers a single
period before ( P = 0) and after ( P = 1) the intervention, testing for parallel trends prior to the intervention64
results in failing to reject the null hypothesis (that there are no violations of parallel trends). In other words, as
these single pre-intervention data points do not produce trends prior to the changepoints, they do not, by defini-
tion, deviate significantly from parallelism. Finally, as explained above, we fit several DiD estimators to derive
longitudinal intervention effects, which provides additional robustness with respect to the assumptions of such
models.
Data availability
The openly accessible MediaWiki history dataset dumps are available at https://dumps.wikimedia.org/other/
mediawiki_histor y/readme.html54. We further provide preprocessed data and results relevant to the manuscript
in the code repository at https://github.com/ruptho/wiki-volunteers-covid.
Code availability
The code repository for this paper can be found at https://github.com/ruptho/wiki-volunteers-covid.
Received: 31 March 2021; Accepted: 14 October 2021
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Acknowledgements
Supported by TU Graz Open Access Publishing Fund. R.W.’s lab was partly funded by the Swiss National Science
Foundation (grant 200021_185043), the European Union (TAILOR, grant 952215), Collaborative Research on
Science and Society, and by generous gifts from Microsoft, Facebook, and Google.
Author contributions
T.R. retrieved the dataset, processed the data, and performed the experiments. T.R. and M.H.R. wrote the code.
T.R., M.H.R., and T.S. analyzed the data. T.R., M.H.R., T.S., F.L., M.S., R.W., and D.H. conceived and designed
the experiments, developed the arguments, and wrote the paper.
Competing interests
The authors declare no competing interests.
Additional information
Supplementary Information The online version contains supplementary material available at https://doi.org/
10.1038/s41598-021-00789-3.
Correspondence and requests for materials should be addressed to T.R.
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Scientific Reports | (2021) 11:21505 | https://doi.org/10.1038/s41598-021-00789-3 12
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