# Taboo and Collaborative Knowledge Production: Evidence from Wikipedia

Kaylea Champion  University of WashingtonSeattle, WAUnited States
<EMAIL_ADDRESS>and  Benjamin Mako Hill  University of WashingtonSeattle,
WAUnited States<EMAIL_ADDRESS>
(July 2022; January 2023; March 2023)

###### Abstract.

By definition, people are reticent or even unwilling to talk about taboo
subjects. Because subjects like sexuality, health, and violence are taboo in
most cultures, important information on each of these subjects can be
difficult to obtain. Are peer produced knowledge bases like Wikipedia a
promising approach for providing people with information on taboo subjects?
With its reliance on volunteers who might also be averse to taboo, can the
peer production model produce high-quality information on taboo subjects? In
this paper, we seek to understand the role of taboo in knowledge bases
produced by volunteers. We do so by developing a novel computational approach
to identify taboo subjects and by using this method to identify a set of
articles on taboo subjects in English Wikipedia. We find that articles on
taboo subjects are more popular than non-taboo articles and that they are
frequently vandalized. Despite frequent vandalism attacks, we also find that
taboo articles are higher quality than non-taboo articles. We hypothesize that
stigmatizing societal attitudes will lead contributors to taboo subjects to
seek to be less identifiable. Although our results are consistent with this
proposal in several ways, we surprisingly find that contributors make
themselves more identifiable in others.

peer production; Wikipedia; online communities; taboo; privacy; anonymity;

††copyright: rightsretained††ccs: Human-centered computing Empirical studies
in collaborative and social computing††ccs: Human-centered computing
Collaborative and social computing theory, concepts and paradigms††ccs: Human-
centered computing Empirical studies in HCI††ccs: Security and privacy Social
aspects of security and privacy††ccs: Human-centered computing Wikis††ccs:
Human-centered computing Collaborative content creation††journal:
PACMHCI††journalyear: 2023††journalvolume: 7††journalnumber: CSCW2††article:
299††publicationmonth: 10††doi: 10.1145/3610090

##  1\. Introduction

Taboos are behavioral prohibitions characterized by the notion that violations
of the prohibition create symbolic uncleanness or pollution (Allan, 2019;
Douglas, 1978). Although taboos vary enormously across cultures, they exist in
virtually all societies (Allan, 2019; Douglas, 1978). People try to avoid
being polluted by taboo in a range of ways—frequently by not talking about or
even mentioning taboo subjects. When taboo subjects are discussed, people
refer to them indirectly or vaguely. As a result, access to high-quality
information on taboo subjects is frequently difficult. This is problematic
because taboo subjects often include important subjects such as mental health,
reproduction, menstruation, abuse, extremism, and human rights violations.

The growth of the Internet and the development of free high-quality knowledge
bases like Wikipedia have made a wealth of information widely available. Are
Internet knowledge bases a promising approach to providing people with
information on taboo subjects? In that they provide opportunities for privacy
in information consumption, social computing systems have been cited as
promising avenues for accessing information on a range of taboo subjects like
menstruation (Søndergaard et al., 2021; Campo Woytuk et al., 2020; Tuli et
al., 2018; Almeida et al., 2016), sexual abuse, and harassment (Moitra et al.,
2020; Andalibi et al., 2016). On the other hand, many online knowledge bases
rely on volunteers who choose their own tasks and subject areas, and it seems
reasonable to assume that at least some volunteers may be reticent to
contribute to resources about taboo subjects. In either case, there is reason
to believe that taboo shapes knowledge production in social computing systems.
That said, we know of no work that explores the role that taboo plays in
online knowledge production.

One challenge to studying taboo as a more general phenomenon is to develop a
way to identify taboos systematically. Examining taboo from a systematic
perspective allows us to develop evidence about taboo as a broad social
phenomenon. To do so, we draw inspiration from work in linguistics to develop
a novel computational approach (Burridge, 2015). Our method uses a supervised
machine learning classifier trained on words in dictionary definitions
associated with euphemisms—i.e., subjects that speakers go out of their way to
describe indirectly, often at the cost of clarity. To understand how taboo
might be shaping activity in social computing, we use these words from
dictionary definitions to identify a set of articles on taboo subjects in
English Wikipedia as well as a comparison set of otherwise similar articles.
For example, since the term “passed away” is a euphemism that would include
the word “dead” or “death” in its definition, we might identify the Wikipedia
article on “death” as taboo.

Using detailed digital trace data on articles and contributors from Wikipedia,
we test five hypotheses derived from theory about how taboo will change the
way that Wikipedia articles are consumed and produced. We find support for
hypotheses that articles on taboo subjects are more popular than non-taboo
articles and that taboo articles are more frequently vandalized than non-taboo
articles. Despite these frequent attacks, we also find that taboo articles are
of higher quality than non-taboo articles, contrary to our hypothesis. We
hypothesize that societal attitudes against contact or association with these
subjects would lead contributors to taboo subjects to seek to be less
identifiable, and although this is sustained in part by our results, it is
also contradicted in part. These surprising results suggest that contributors
to the public production of taboo knowledge navigate more complex privacy
trade-offs than previously theorized.

The remaining sections of the paper are structured as follows. In §2, we
examine theories of peer production, taboo, social norms, and
identifiability/anonymity, including what these theories suggest about the
peer production of information about taboo subjects. In §3, we describe our
empirical setting Wikipedia. We then describe the novel technique we develop
to identify taboo and the construction of our analytic sample in §4, and our
analytical process in §5. Section §6 reports results from our hypothesis
tests. We discuss the significance and implications of our findings in §7,
some important limitations of this work in §8, and conclude in §9.

##  2\. Background

###  2.1. Commons-Based Peer Production

As Benkler (2002) describes, one of the technological and social advances of
the last few decades is the creation of a novel form of production: commons-
based peer production. Individuals participating in commons-based peer
production activities online are volunteers who self-organize around a goal,
select their own tasks, and engage in the creation or maintenance of
information goods. These volunteers are sometimes motivated by generosity and
altruism, or simply pursuit of their own passions—which may be in
contradiction to dominant social and market logics. Often these information
goods are also public goods, as in the case of Wikimedia projects such as
Wikipedia and Wiktionary, the mapping project Open Street Map, and free/libre
open source software such as GNU/Linux and the Python programming language.
These valuable projects are critical parts of our digital infrastructure:
delivering knowledge that answers our searches online, the software that
organizes and displays those answers, and the servers and protocols that
convey them from distant data centers to our fingertips. Although these
projects are rightfully hailed as transformative, their results—and the peer
production process that produces them—are far from perfect.

Studies of peer production projects have pointed out numerous examples of bias
and neglect. The participant pool of many prominent examples of peer
production is generally male, including Wikipedia (Ford and Wajcman, 2017;
Hill and Shaw, 2013; Collier and Bear, 2012), Linux (Reagle, 2013; Nafus,
2012), and Open Street Map (Thebault-Spieker et al., 2018). The products
developed through peer production may also be biased or neglect important
materials. In Wikipedia, previous work has found neglect of articles about
women (Tripodi, 2021), non-English languages (Khatri et al., 2022; Miquel-Ribé
and Laniado, 2020; Hecht and Gergle, 2010), and countries, religion, and LGBTQ
subjects (Warncke-Wang et al., 2015). In Open Street Map, research has found a
lack of map information about the global South (Thebault-Spieker et al.,
2018). These biases in peer-produced resources are troubling because they
reflect and may serve to perpetuate existing societal biases, inequalities,
and hegemonic structures. Despite these flaws, however, peer production has
such advantages as self-organization and self-selection of tasks.

Examining the question of taboo knowledge in peer production offers us insight
into the extent to which this novel organizational form is simply serving to
reproduce and magnify existing features of society, or whether participants
may perhaps be finding ways to resist cultural norms.

###  2.2. Taboo—and Taboo in HCI

Taboo has been the subject of an enormous amount of scholarship in
anthropology, sociology, and linguistics (Allan, 2019; Douglas, 1978). Taboos
demarcate the forbidden and unspeakable parts of existence from those that are
recognized as sanctified, worthy, or simply acceptable. A taboo acts as a
behavioral prohibition and is characterized by the notion that violations of
the taboo create a sense of uncleanness or pollution. The uncleanness can be
literal (e.g., contact with something covered in germs may transfer those
germs) or may be symbolic (e.g., make one’s prayers unacceptable in the eyes
of God). Because making contact with something taboo makes a person unclean,
taboo spreads its uncleanness through interaction. Although the taboo itself
may make someone feel personally uncomfortable or embarrassed even if they are
alone, taboo is often enacted socially. Although speakers may use euphemism to
protect the sensibilities of others when discussing taboo subjects, McGlone
and Batchelor (2003) found through an experimental manipulation that speakers
are more likely to use euphemism when discussing taboo subjects if they expect
to be identified to the listener, concluding that the reticence to speak about
taboo subjects is more a matter of a speaker trying to save face than
protecting others. Taboo may manifest in complex ways in social computing
settings. We may not only have a sense of being visible to friends, but also
imagine a broader audience, which might be profoundly public (Litt, 2012;
Marwick and boyd, 2011). We are further visible to our technology—not only in
the form of history and device logs, but also in the ways our behaviors train
the algorithms that curate our experience—any of which may serve to embarrass
us, restrain us, and drive us to seek privacy (Toch et al., 2012).

Mary Douglas’s work in Purity and Danger: An Analysis of the Concepts of
Pollution and Taboo points out that when “pollution rules” that constitute
taboo are examined with care, social order is constantly implicated in what we
consider clean or unclean, acceptable or forbidden. Ultimately, she says, we
should understand taboo as part of a symbolic system in which “uncleanness is
matter out of place” (Douglas, 1978, p. 41) and a violation of “cherished
classifications” (ibid, p. 37). Hence, to the extent that people interact with
subjects that are taboo, they are in some way challenging social order and by
extension society itself. As a result, reference materials associated with
taboo subjects may be restricted, censored, or banned outright (Allan, 2019).

The English word “taboo” originated in the voyages of Captain Cook who derived
the word from the Tongan word “tapu” meaning sacred or forbidden. The term
came to be used in colonial and anthropological accounts of non-Western
cultures, often as part of characterizing those cultures as “uncivilized”
(Allan, 2019). Despite this history, taboo has come to be understood as
existing in virtually all cultures and societies throughout history (Allan,
2019; Crespo Fernández, 2011; Douglas, 1978). Although every member of every
society has some internal and personal sense of taboo, taboos vary by culture,
religion, relative levels of privilege, and more. Like early colonial
anthropologists, we may fail to recognize our own taboos because they seem
natural or even objective to us (Douglas, 1978). Seeking out a more systematic
way to identify taboo may therefore serve to de-center and de-naturalize our
own cultural position.

Despite the rich literature in other disciplines, we believe that ours is the
first article in social computing to approach the study of taboo in general,
instead of focusing on specific taboo subjects. To what extent is “taboo” a
subject of concern for research at the intersection of society, technology,
and design? Although a search of the over 2.9 million articles in the ACM
digital library returns 1,849 results for ‘taboo’, most of these articles
refer to an algorithmic search strategy in which potential solutions are
marked as forbidden or to a party game in which players try to guess a word
when prompted with clues that cannot include the word itself. A closer
examination of the 174 research articles published in SIGCHI venues revealed
that most of the remaining articles mentioned the term “taboo” only once and
in passing.

Although none of these articles sought to understand or measure the effect of
taboo in general, our search did reveal a number of articles on systems and
designs that are intended to counter or transcend the harmful effects of taboo
on information environments in specific cases such as menstruation
(Søndergaard et al., 2021; Campo Woytuk et al., 2020; Almeida et al., 2016),
sexuality (Kannabiran et al., 2011; Rahman et al., 2021), toilet training
(Helms, 2019), and AIDS education (Sorcar et al., 2017). Another line of
research seeks to understand how people use existing social computing systems
in ways that are shaped by specific taboos—e.g., sharing information about
menstrual health (Tuli et al., 2018) and menopause (Lazar et al., 2019), or
describing experiences such as sexual abuse (Andalibi et al., 2016), sexual
harassment (Moitra et al., 2020) and pregnancy loss (Andalibi, 2020).

One related body of work is the study of stigma, which is a connected but
distinct concept. A taboo is a behavioral prohibition, whereas stigma refers
to an identity state, perhaps but not always associated with violating taboos.
One may be stigmatized for a congenital condition or by violating a range of
different types of social norms. In Stigma: Notes on the Management of Spoiled
Identity, Erwin Goffman describes stigma as “an undesired differentness from
what is expected” (Goffman, 1963, p. 5) and, like Douglas, invokes the role of
social order. For Goffman, a stigmatized identity means that one’s social
identity as perceived externally is in some way different from what is
perceived internally. For example, a formerly incarcerated person may be
stigmatized—they see themselves as ‘like everyone else’ and trying to live a
normal life, but to their neighbors they may forever be discredited as
criminal. Goffman observes that stigma in society is transferred via
association: the child of a person known to be a criminal may also be the
subject of suspicion.

Although none of this work takes up the concept of taboo in general, these
papers point to the fact that taboo is an important feature of online
information seeking and production in at least several specific cases. Taking
a systematic approach is complementary to these studies that focus on a priori
taboos by supporting comparative analysis across contexts and across a range
of taboos, as well as potentially expanding the set of taboos explored through
various methods.

###  2.3. Taboo Content Consumption

Avoidance of taboo is rarely universal. We may simply need information related
to taboo subjects, such as those related to such everyday activities as
urination, defecation, menstruation, and sexual intercourse. Some people
deliberately violate taboos and seek out community in doing so. Others are
struggling with taboo issues or have survived taboo experiences and are
seeking supportive environments. Further, the forbidden nature of taboos may
lead some people to feel drawn toward the subject—they may titillate or
fascinate or offer an avenue to rebel against society’s norms.

Privacy may also play a role in taboo violation. For example, Malinowski
(1926) famously observed in his 1926 account of the people of Trobriand Island
that a taboo may be widely violated in private and the subject of gossip and
humor, yet still have severe consequences when its violation is turned into a
public spectacle. Likewise, support groups often have rules about maintaining
participant anonymity, and some sites have norms and affordances that support
anonymized interactions about taboo subjects (Ammari et al., 2019).

Web browsing may feel relatively private, especially when we are only
consuming content. We may feel less inhibited in an online environment and
more willing to engage with material that we would be reluctant to be seen
reading about or heard speaking about publicly (Suler, 2004). One might expect
that feelings of safety to be higher in websites—like Wikipedia—that are
accessible without paywalls or registration requirements which signal to a
reader that consumption is being tracked. Given that information seekers may
have relatively few alternative sources for high-quality information about
taboo subjects and the relative privacy of reading Wikipedia, we suggest H1:
peer produced resources about taboo subjects will receive higher readership
than peer produced resources about other comparable subjects.

###  2.4. Taboo, Public Knowledge, and Identifiability

Although consuming content hosted in knowledge bases like Wikipedia may feel
private, producing knowledge resources is often a publicly visible act. In
Wikipedia, every article has a history tab containing each past version of
itself. A “diff” view allows anyone to examine the letter-by-letter impact of
every change made. Beyond the contribution itself, the account or IP address
of every contributor is tracked and made visible. Both the article’s revision
history and an individual contributor’s contribution history can be reviewed
by anyone. Contributors can offer additional information about themselves by
customizing their user page, by registering an email address that can be used
to contact them, and by disclosing their gender. Although the effort required
to identify contributors varies, Wikipedia’s high level of transparency means
that every contributor is identifiable in some dimension. Users of tools to
protect personal privacy, including anonymity-preserving proxies such as the
popular Tor browser, are banned from contributing to Wikipedia (Tran et al.,
2020).

On Wikipedia, people edit only what they want to edit (Benkler, 2006). Because
taboos are so powerful and pervasive, they may become internalized. As a
result, contributors may seek to avoid taboo subjects even in private
settings. A high level of public visibility and the voluntary nature of peer
produced projects suggest that people may be reluctant to choose to contribute
to taboo subjects out of a reluctance to contaminate themselves. Therefore, we
propose H2: peer produced resources about taboo subjects will receive fewer
contributions than other comparable peer produced subjects.

Online knowledge bases are typically secondary or tertiary sources of
information that depend on the availability of other sources to establish
credibility (Ford et al., 2013). Taboo subjects are often the targets of
censorship efforts (Allan, 2019). For example, materials about taboo subjects
like violence, sexuality, race, and religion are regular targets of book
banning campaigns in K-12 schools in the United States (Aucoin, 2021).
Blocking of political, religious, and social norm-violating content (including
pornography, drugs, LGBTQ content, and online dating) is widespread (Zittrain
et al., 2017). At the state level, for example, China suppresses information
on human rights abuses and collective action, and Norway blocks many
pornography and gambling websites (Sundara Raman et al., 2020; Zittrain et
al., 2017; King et al., 2013). Although social pressure and a lack of
available secondary sources might lead to lower-quality contributions, the
nature of taboo subjects themselves may tend to draw in or inspire bad faith
contributions—e.g., writing sexual slurs on pages about sexual subjects. Given
societal and governmental opposition to materials on taboo subjects, the
suppression of primary and secondary sources that might otherwise be used to
build knowledge bases, and the role of troll, griefers, and vandals, we
propose H3: peer produced resources about taboo subjects will receive lower-
quality contributions than peer produced resources about other comparable
subjects.

Some theorists of open content production have made direct connections between
the quality of a resource and the volume of contributors. Raymond (2001)
famously described this process as “Linus’s Law:” “given enough eyeballs, all
bugs are shallow.” Empirically, Haklay et al. (2010) found that positional
accuracy in open mapping projects increases with the number of participants.
Greenstein and Zhu (2016) found that neutrality in point of view in Wikipedia
articles improved with increases in the number of contributors. Synthesizing
these observations—and as a consequence of H2 (fewer overall contributions)
and H3 (lower-quality contributions)—we propose H4: peer produced resources
about taboo subjects will be lower quality than peer produced resources about
other comparable subjects.

Finally, we consider the impact of taboo on the willingness of contributors to
identify themselves. Possessing knowledge about a taboo subject may lead to
stigma (Goffman, 1963). Social embarrassment and opprobrium can result from
being known to have violated taboos leading to loss of reputation and
position, or in extreme cases, to incarceration and violence. For example,
Wikipedia editors have been harassed for their contributions both on and off
Wikipedia (Jacobs, 2019; Brown, 2016; Paling, 2015). In some cases, editors
have even been prosecuted for their edits, as in the case of the Belarusian
government arresting a Wikipedian for writing that Russia has invaded Ukraine
(Song, 2022). Hence, people contributing knowledge about a taboo subject to
online knowledge bases may engage in a range of strategies to control
information about their identity and behavior (Semaan et al., 2017). For
example, the Russian language edition of Wikipedia has suppressed the public
edit logs for pages associated with the Russian invasion of Ukraine and
encouraged editors to switch to alternate, less identifiable accounts as a
means to protect their safety (Bri and EpicPupper, 2022; noa, 2022).

If users participate at all, they may only engage with taboo when they feel
they have control over information about themselves: by interacting
anonymously or developing a new or special-purpose account or profile (Taber
and Whittaker, 2020; Menking et al., 2019; Kang et al., 2013; Forte et al.,
2017; Birnholtz et al., 2015). Because all these factors suggest the
desirability of partially controlling or fully avoiding personal association
with taboos, we propose H5: peer produced resources about taboo subjects will
be more likely to receive contributions from less identifiable personas than
those about other comparable subjects.

##  3\. Empirical Setting

Wikipedia is a vital source of information worldwide, with 318 language
editions as of 2023.111https://en.wikipedia.org/wiki/List_of_Wikipedias In
December 2022 alone, Wikipedia received more than 24 million contributions and
served up more than 23 billion page views across all projects, with 255,255
new user accounts created.222https://stats.wikimedia.org The largest language
edition, English, contains more than 7.5 million articles as of January 2023
and averages 559 new articles created each
day.333https://en.wikipedia.org/wiki/Wikipedia:Statistics Wikipedia entries
are often the first result for a query on Google; text from relevant Wikipedia
pages may be surfaced in search engine sidebars without the user needing to
dig any further (Vincent et al., 2018).

##  4\. Methods

###  4.1. Technique for Identifying Taboo Subjects

Our research questions concern taboo subjects in general. Therefore, we seek
to minimize the assumptions we make about what is and is not taboo based on
our own cultural context. Work in linguistics concerning taboo subjects has
described how people use figurative language and euphemism to deal with taboo
(Crespo Fernández, 2011). For example, we may say “passed on” instead of
“died.” Burridge (2015) describes euphemisms as “a verbal escape hatch in
response to taboos.”

To build our dataset of taboo articles in English Wikipedia, we made use of
English Wiktionary.444https://en.wiktionary.org/wiki/Wiktionary:Main_Page
Wiktionary is a sister project to Wikipedia, building a peer produced
dictionary. Wiktionary has received comparatively less attention from
researchers when compared to the encyclopedia-building Wikipedia
project.555For example, Wiktionary appears four times among the 1,972 examples
of Wikipedia-related research listed in the Wikipedia page about Wikipedia
research:
https://en.wikipedia.org/wiki/Wikipedia:List_of_academic_studies_about_Wikipedia
Because Wiktionary entries are somewhat free-form, we used the wiktextract
Wiktionary parser and associated dataset to separate entries into key
components for analysis: word, definition, and tags.666See
https://github.com/tatuylonen/wiktextract and https://kaikki.org/dictionary/
In Wiktionary, definitions and tags are associated with each “sense” of a word
to manage polysemy. Our corpus is composed of definitions remaining after we
filtered the dataset to remove non-definitions (e.g., redirects to other
words, definitions stating only that a word is a synonym or initialism of
another word) and non-English definitions when the entry was supplying a
translation. This filtration of 1,099,350 dictionary entries from the
September 1, 2021 version of Wiktionary left us with 404,304 unique entries,
each composed of a word, a definition, and, in some cases, associated tags.

Many dictionaries tag particular word usages as euphemistic. This makes
dictionaries a source of general data on taboo because definitions marked as
euphemism contain a description of subjects considered taboo. For example, the
term “member” has multiple definitions, one of which is marked as
“euphemistic” in English Wiktionary (see Figure 1). The term being defined
(i.e., “member”) is not taboo—that is precisely why it is effective at
creating distance from the taboo concept in the definition. We were inspired
in this approach by Buscaldi and Hernandez-Farias (2015) who used the fact
that Italian Wiktionary contributors have explicitly tagged some words as
“taboo” to train a classifier for sentiment analysis.

Our approach involved collecting only the text of definitions, not the words
being defined. Tags in English Wiktionary can be applied to any definition. In
Figure 1, “(euphemistic)” and “(logic)” are tags. We treated all definitions
tagged as “euphemistic” as indicative of taboo. For example, in Figure 1, the
definitions of “member” that refer to group association, a part of a whole,
animal parts, and logic are each marked as not indicative of taboo in our
dataset. However, the definition that refers to genitalia is tagged
“euphemistic” and is marked as indicative of taboo in our dataset. We removed
numbers and stop words, using the English stop words list in the Python
Natural Language Tool Kit (NLTK), from all
definitions.777https://www.nltk.org/ We also removed the words ‘term’, ‘used’,
‘usually’, ‘particularly’, ‘etc’, ‘extremely’, ‘especially’, ‘one’, ‘en’,
‘something’, ‘often’, ‘synonym’, ‘like’, and ‘person’ because these words
recur very frequently in Wiktionary entries but convey only intensity.

Figure 1. The Wiktionary definition of “member”—which has meanings that range
from group association to anatomical.

We used term frequency-inverse document frequency (TF-IDF) (Rajaraman and
Ullman, 2011) and ridge regression from the scikit-learn python package
(Pedregosa et al., 2011) to assess which words, bigrams, and trigrams
(“n-grams”) were more commonly present in the definitions tagged as
euphemistic. We sorted these terms by their coefficient from ridge regression
to develop a list of 500 n-grams indicative of taboo.

Table 1. A random selection of articles from our taboo set. We have omitted racial/ethnic slurs, explicit sexual acts, and profanity in this table but included them in our dataset. Sex differences in human physiology | Genitourinary system | Love God | Cat meat
---|---|---|---
Gastrointestinal tract | Damnation | Alive and Dead | Old Age
Monosodium glutamate | Being Bobby Brown | Abdominal Obesity | Feral cat
Amazon Women on the Moon | Menstruation | Love and God | Layoff
Mental disorder | Unemployment | Head on Collision | Vulva

To test our hypotheses, it is not necessary that we detect all taboo articles
in Wikipedia. Instead, we need only generate a dataset composed of two
samples, one of which has a higher proportion of taboo articles. In machine
learning terms, we sought precision at the cost of recall. As a result, we
performed an exact matching between Wikipedia article titles (minus stop
words) and the 500 n-grams we identified as most associated with taboo. This
conservative approach left us with a relatively small but high-confidence
sample. Examples of these articles are listed in Table 1.888Inclusion of the
article on the reality show “Being Bobby Brown” in the taboo set initially
surprised us. The article is included because the article “Hell to the no” (a
phrase the article says was popularized by the show) is a redirect that lands
on this article. The words “to”, “the” and “no” are stop words, leaving us
with a match on the term “Hell.”

For the comparison set, we considered using a sample of random Wikipedia
articles. However, in exploratory analyses, we realized that this sample is
inappropriate because most article titles in Wikipedia are not n-grams that
appear in dictionary entries and could never have been selected by our taboo
article identification process. Although many Wikipedia articles are phrases,
events, locations, and proper names, the articles in our taboo set have a
“dictionary definition-esque” quality. To ensure a comparable sample, we limit
our comparison set to a random selection from the population of 115,681
articles in English Wikipedia with titles (minus stop words) that match an
n-gram found in English Wiktionary definitions.

Figure 2. Our analytical pipeline first extracts n-grams, labeling them taboo
if they are drawn from definitions tagged as euphemistic. Our samples are
drawn from those articles that match these n-grams.

We conducted additional processing on both the comparison set and the taboo
set. Many Wikipedia pages serve only to disambiguate a term or provide a list
of other articles. These non-articles were omitted. Further, some Wikipedia
articles are “redirects”—they serve as largely behind-the-scenes connectors to
other articles and require special handling (see Hill and Shaw (2014).) When a
term in our n-gram list was the title of an article serving as a redirect, we
followed the redirect and extracted data for the target article. When the
target of a redirect was a subsection of a longer page, we dropped the article
from our sample because our measures cannot be easily constructed for article
subsections. This left us with 74 taboo articles and 3,255 randomly selected
comparison articles.

####  4.1.1. Validation

Evaluating our approach is difficult given the culturally varying nature of
taboo and the lack of ground truth data. As a simple validity check, we test a
hypothesis that articles in our taboo set will be more likely to have been
assigned Wikipedia categories related to sex—a subject that appears frequently
in taboos across many cultural contexts (Moitra et al., 2020; Andalibi et al.,
2016; Kannabiran et al., 2011; Crespo Fernández, 2011; Douglas, 1978;
Malinowski, 1926)—than those in our comparison set.

Categories in Wikipedia are used for a wide range of purposes and are largely
free form (Thornton and McDonald, 2012). Past research by Chen et al. (2010)
and by Asthana and Halfaker (2018) has shown the utility of categories
associated with topically-focused WikiProjects (Morgan et al., 2013) are a
source of reliable information about an article’s subject. We used Wikimedia
Foundation public APIs to obtain a list of 5,575 unique categories from the
3,329 articles in our datasets as well as their associated talk pages.

We then used a logistic regression model to assess the relationship between
sexual topics and membership in the taboo or comparison set in our sample.
Articles can, and often do, exist in multiple categories simultaneously. As a
baseline, taboo articles comprise 2.2% of our sample. Given an article in our
dataset that has been assigned to the scope of WikiProject Sexology and
Sexuality, our model suggests that there is a 44% chance of it being in the
much smaller taboo set ($\beta=3.4$, $z=8.43$, $p<0.001$). This reflects very
strong evidence that our taboo set is more likely to contain articles about
sex and sexuality than is our comparison set.

###  4.2. Data and Measures

Having identified our taboo and comparison samples, we obtained the full text
and metadata for revisions made to all articles in the samples by parsing the
XML database dumps released by the Wikimedia
Foundation.999https://dumps.wikimedia.org Given that our interest is in human
behavior, we attempt to omit revisions made by bots. To identify edits made by
bots, we used a list we scraped from a Wikipedia page listing current
registered bots as well as a historical dataset produced and released as part
of Geiger and Halfaker (2017). Our final article and revision datasets
contained 177,974 revisions made to taboo articles and 2,052,209 revisions
made to articles in the comparison set. We used these data to construct
longitudinal measures for each article in our sample: measures of quality of
each article each month, the contribution history for each article, each
contribution as well as its contributor, and the per-month viewership of the
article. We describe each in turn.

In H1 we suggest that taboo subjects will receive higher readership than
comparable subjects. We operationalize readership using aggregate article view
count data published by the Wikimedia Foundation to obtain the total number of
views for each article in our sample for each month. We obtained 541,913
article-month measures of viewership and then calculated the mean view rank
for each article.

To test H2 on the number of contributions, we count the number of
contributions made to each article in our sample over the life of each
article.

In H3, we take up the question of whether taboo subjects receive lower-quality
contributions. We operationalize contribution quality using two measures. The
first, was reverted, indicates that a given contribution was rejected after it
was published. A contribution is said to have been reverted if someone
restored an article to its state before the contribution in question. We
looked 10 contributions forward to see if a contribution was reverted. The
second measure we use, available through the ORES machine learning
classifier’s “revision” model, offers a prediction of whether or not a given
contribution was “damaging” (Yang et al., 2017). In Wikipedia, damaging
contributions are defined as those contributions that may need to be removed
because they do not conform to Wikipedia guidelines. Damaging contributions
can range from vandalism and misinformation to naive statements or formatting
errors. Although many damaging contributions are simply reverted, damaging
contributions can instead be revised to be acceptable or be treated by
community members as an opportunity to teach a new contributor (Halfaker et
al., 2011).

Our hypothesis H4 concerns the quality of an article. We measure quality using
the assessments generated using ORES’ quality classifier (Halfaker, 2017). The
classifier was trained using the work of Wikipedia contributors and classifies
revisions based on several structural features of articles, including length,
the presence of pictures, and the use of links to other sources.

In H5, we hypothesized that contributors to taboo subjects will be less
identifiable. Because identifiability has many dimensions including the
presence of unique personal identifiers, location, the presence or absence of
a consistent pseudonym, and behavioral patterns (Marx, 2016), we
operationalize identifiability in several ways derived from prior work about
identifiability on Wikipedia: (H5A) editing without an account (Forte et al.,
2017; Anthony et al., 2009), (H5B) editing using a new account or one with
fewer contributions (Forte et al., 2017; Rizoiu et al., 2016; Kraut et al.,
2012), (H5C) revealing relatively little information on one’s profile (Rizoiu
et al., 2016; Brückner et al., 2021), (H5D) identifying one’s gender (Brückner
et al., 2021), and (H5E) setting one’s account as “emailable,” which also
requires a confirmed email address.

We measure whether a contribution was made with or without an account using
metadata associated with each revision (H5A). We analyze contributor
experience levels by counting the number of revisions each contributor has
made to identify each revision as the contributor’s “$n$th edit” (H5B). We use
the XML database dumps to determine whether contributors had a user profile
page at the time they were revising the articles in our samples (H5C), setting
a dichotomous variable if they had a user page at any of these time points.
Contributor gender and being emailable by others can be set by users in their
Wikipedia setting page. We obtained this information via queries to the
Wikimedia public API (H5D, H5E).

Whether a contribution is made by a user with or without an account is highly
visible to other Wikipedia users. Previous editing by a user is less visible
but is still readily accessible. These signals have been previously found to
influence how work is received in Wikipedia, with non-accountholders and
accounts with lower editing counts being moderated more strictly (TeBlunthuis
et al., 2021). These two identifying signals are also recorded automatically
by the Wikipedia website software. The other signals are “opt-in.” User pages
are free text profile pages and exist only if a user chooses to make one.
Gender and emailability are only available via an API call, although
emailability can be inferred by attempting to email the person via the website
interface that is available to all logged-in users.

There is a potential confounder in our tests for H5A and H5B because some
articles are “protected” so that they cannot be edited by contributors without
accounts or by newcomers (those with accounts less than 4 days old and fewer
than 10 edits).101010https://en.wikipedia.org/wiki/Wikipedia:Protection_policy
In this way, page protection directly determines whether some anonymity
seekers can participate.

Following the method described in Hill and Shaw (2015), we identify
“protection spells”—periods wherein a given page was protected. As Hill and
Shaw (2015) describe, page protection data are left censored because of
missing logs before 2008. As a result, we limit our assessment to the point at
which reliable logs are available. We use these data to calculate the
proportion of time each article in our dataset was protected since 2008.
Articles varied in protected proportion from 0 (e.g., the articles Abdominal
Obesity in our taboo set and Abbot in our comparison set that were unprotected
for the entire period) to more than 0.99 (e.g. the articles Hell in our taboo
set and Messianic Judaism in our comparison set that were protected for almost
the entire period). We use protected proportion as a control variable in our
analyses for H5A and H5B.

###  4.3. Ethics

This study was conducted entirely using publicly available data published by
the Wikimedia Foundation and does not involve any interaction or intervention
with human subjects. This type of research using these data has been reviewed
by the IRB at our institution and has been determined to not be human subject
research. However, this work removes public digital trace data from its
original context. Additionally, computational approaches have the potential to
reveal behavioral trends in ways that individuals may find uncomfortable,
especially given the subject of this study. As a result, we have redacted
account names and IP addresses of the individuals who contributed to the
articles in our sample. Article view data were fully anonymized by the
Wikimedia Foundation prior to release.

Finally, we recognize that the use of taboo language may have differing
impacts on those reading our work in various contexts. To make our work as
available as possible while minimizing harm, we have redacted or omitted the
use of racial/ethnic slurs and profanity in Table 1 as well as in the text of
our paper. However, all terms were included in the underlying analysis with no
omissions. In the interest of accountability and open science, our full
results and data are available in our dataset release and online supplement in
an unabridged form.

##  5\. Analytic Plan

With respect to H1 on viewership, our dataset includes articles written at
different times and Wikipedia itself has received varying levels of traffic in
the last two decades. Therefore, we calculated the rank in terms of views of
each article with respect to other articles in our samples. We compared the
mean within-month view rank of each article in our taboo and comparison
samples across all months and tested for statistical significance using a
Mann-Whitney U-test. The unit of analysis is the article, with mean rank
across months as the outcome variable and taboo as our key predictor.

To test H2 about contribution quantity, we examine the median number of
contributions in our two samples and tested statistical significance using a
Mann-Whitney U-test. The unit of analysis is the article, with total
contributions as the outcome variable and taboo as our key predictor.

To test H3 on contribution quality, we compared the median rate of reverted
contributions of the two samples (total reverted/total contributions) and the
median rate of damaging contributions, and tested statistical significance
using a Mann-Whitney U-test. The unit of analysis is the article, with total
reverted (damaging) contributions as the outcome variable and taboo as our key
predictor. As a robustness check, we conducted further analysis adding total
contribution volume as a control variable in a linear regression.

To test H4 about article quality, we compared the median quality of the two
samples and tested for statistical significance using a Mann-Whitney U-test.
The unit of analysis is the article, with our measure of average article
quality as the outcome variable and taboo as our key predictor.

To evaluate H5A on the probability of someone contributing without an account
varying between contributions to taboo and non-taboo articles, we fit a
logistic regression on account status with taboo and a predictor and with
protection as a control. The unit of analysis for this test is the revision.
Because we have repeated measures of articles, we fit a multilevel model with
a random intercept term for each article. To evaluate H5B about the
relationship of an article contribution being to a taboo article to
contributor experience level, we fit a logistic regression on the logged
average contributor experience with taboo as a predictor and page protection
percentage as a control. The unit of analysis for this analysis is the
article.

The identity affordances used as outcomes in tests for H5C, H5D, and H5E (user
page, gender, emailability) are only available to accountholders, so we
restrict these analyses to contributions made by accountholders. For H5C, H5D,
and H5E, we used a user-level dataset. Our analysis of H5C (user page) uses a
dichotomous variable based on whether the contributor had a user page while
making a contribution to any article in our sample. We use a chi-squared test
to evaluate the relationship between having a user page and ever editing a
taboo article. Our analysis of H5D (gender) uses two measures: first, someone
must opt in to the gender-specifying feature, and second, they must choose a
gender from the menu (the options given by the interface are “male” and
“female”). We tested these aspects separately. We evaluated the relationship
between specifying a gender, specifying a gender that is female, and being
emailable using a chi-squared test.

##  6\. Results

###  6.1. Public Interest in Taboo Subjects (H1)

Our analysis of page views provides evidence in support of H1 that taboo
articles are viewed more frequently than those in our comparison set. The
median view rank for taboo articles is 11,525 and the median view rank for the
baseline comparison set is 39,017 ($U=189,375$, $p<0.001$). These data are
represented in the boxplots in top panel of Figure 3. Note that the highest
possible rank in this analysis is 1. We observe that although both sets have
long tails including very unpopular articles, the higher popularity of taboo
articles is such that the interquartile ranges of the two sets overlap only
slightly.

Figure 3. Boxplots showing the distributions of article-level variables for
five hypothesis tests. From top to bottom: (a) view rank of articles where
view rank is calculated within a given month across all articles where the
most viewed article would rank 1 (H1); (b) quantity of contributions (H2); (c)
article-level revert rates (H3); (d) article-level damaging contribution rate
(H3); and (e) quality of the articles (H4). Small vertical lines in the boxes
indicate medians. Triangles are located at the mean.

###  6.2. Contribution Quantity (H2)

Contrary to our expectations in H2, our analysis provides evidence that the
quantity of contributions to taboo subjects is substantially higher than to
the comparison set. The median number of contributions for taboo articles is
1,620, and the median number of contributions for the comparison set is 143
($U=53,010$, $p<0.001$). These data are represented in the boxplots in the
second panel of Figure 3.

| Revert Count
---|---
(Intercept) | $-27.3728^{*}$
| $[-30.6155;-24.1300]$
Taboo | $49.5379^{*}$
| $[29.9640;69.1117]$
Contribution Count | $0.2791^{*}$
| $[0.2771;0.2812]$
R2 | $0.9590$
Adj. R2 | $0.9590$
Num. obs. | $3299$
∗ 0 outside the confidence interval.
Table 2. Linear regression model estimating effects for taboo on article
revert count, with control for overall contribution count (H3).

###  6.3. Contribution Quality (H3)

Our analysis of contribution quality provides support for H3. We find that
contributions to taboo articles are more frequently reverted than
contributions to those in our comparison set. The median revert rate for taboo
articles is 26.3% and the median revert rate for the baseline comparison set
is 10.1% ($U=48,566$, $p<0.001$). These data are represented in the boxplots
in the third panel of Figure 3.

We also measured contribution quality using the ORES model predicting
“damaging” revisions and found that revisions to taboo articles are more
frequently damaging than those to articles in our comparison set. The median
damaging contribution rate for taboo articles is 20.3% and the median damaging
contribution rate for the baseline comparison set is 10.8%, ($U=56,225$,
$p<0.001$). The distribution of the two sets are depicted in the fourth panel
of Figure 3. These estimates are similar in size to our estimates for our
first hypothesis test for H3.

Both of these results for H3 provide evidence in support of our hypothesis
that taboo subjects are the target of lower-quality contributions. However,
given that our test of H2 showed that the number of revisions to taboo
articles is an order of magnitude higher than the number of revisions to
articles in our comparison set, edit volume appears to be a possible
confounder. Indeed, revert rate and overall quantity of revisions are
moderately positively correlated ($\rho=0.505$, $p<0.001$). We report the
results of our robustness check—a linear regression with a control for
contribution count—in Table 2. We observe that our prior finding that taboo
articles receive more damaging contributions than do non-taboo articles is
robust to the control for overall contribution volume.

###  6.4. Article Quality (H4)

Opposite to our proposal in H4, we find that articles addressing taboo
subjects are higher quality than those in our comparison sample. The median
quality level for taboo articles is 2.2 while the median quality for the
comparison set is 1.7 ($U=87,747$, $p<0.001$). These data are visualized in
the bottom panel of Figure 3.

###  6.5. Contributor Identifiability (H5)

In H5A-E, we proposed that contributors to taboo subjects would be less
identifiable. The results for H5A about users with accounts are shown in Table
3 and suggest that a revision to a taboo article is more likely to be made by
a user without an account than a revision to the comparison set. The effect is
as we hypothesized. Although the size of this effect is small (<1%), a 0.05%
change in the 24 million contributions received in December 2022 is 120,000
contributions.

| Contributing Without an Account
---|---
(Intercept) | $-0.9948^{*}$
| $[-1.0141;-0.9755]$
Taboo | $0.1746^{*}$
| $[0.0547;0.2944]$
Protection Level | $0.0791^{*}$
| $[0.0351;0.1231]$
AIC | $2749327$
BIC | $2749377$
Log Likelihood | $-1374659$
Num. obs. | $2219242$
Num. groups: encodedTitle | $3293$
Var: encodedTitle (Intercept) | $0.2639$
∗ 0 outside the confidence interval.
Table 3. Hierarchical logistic regression model estimating the effects for
taboo on whether or not a revision is made by someone contributing without an
account (H5A). Fixed effects for the article are not shown.

In terms of H5B about editor experience, we find that the average editor to a
taboo article has made fewer edits (21,621) than the average editor to our
comparison set (36,176) ($U=171,617$; $p<0.001$). Further, in a model where we
control for page protection (which blocks non-registered and low-experience
contributors), contributors to taboo articles still tend to have fewer
contributions. This result is surprising because one might expect that
including this control would have reversed this relationship; blocking the
least experienced contributors seems like it would be associated with
contributors with more contributions rather than fewer, however this is not
the case. Page protection is also associated with fewer contributions, as
expected. Table 4 shows the results of a linear model with taboo as a
predictor of (logged) contribution count and protection level as a control.

| Contribution Count Model
---|---
(Intercept) | $10.6406^{*}$
| $[10.6122;10.6689]$
Taboo | $-0.2318^{*}$
| $[-0.4143;-0.0492]$
Protection Level | $-1.2622^{*}$
| $[-1.4679;-1.0565]$
R2 | $0.0477$
Adj. R2 | $0.0471$
Num. obs. | $3299$
∗ 0 outside the confidence interval.
Table 4. Results of a linear model examining the relationship between number
of contributions a contributor has made (log scale) and whether they are
contributing to a taboo subject with 95% confidence intervals (H5B).

Our analysis contradicts what we proposed in H5C. Of the 181,597
accountholders about whom we have user page data, 35.3% of them had user pages
at the point when they made any of the contributions in our sample. We found
that 51.9% of the contributors who ever contributed to a taboo article had a
user page during one of their overall contributions to our samples, whereas
only 32.8% of those who did not contribute to a taboo article had user pages
while contributing to a page in our sample, a statistically significant
difference ($\chi^{2}=3267.2:p<0.001$).

These results provide evidence that accountholders who contributed to a taboo
subject in our sample were more likely to have a user page than contributors
in our sample who did not contribute to a taboo subject in our sample.

Next, we observe that of the contributors who had ever contributed to the
taboo articles in our sample, 17.7% of them specified a gender, while of the
contributors who did not contribute to the taboo articles, 10.0% specified a
gender ($\chi^{2}=1045.9:p<0.001$). This result contradicts H5D and suggests
that people who contribute to taboo articles using an account are more likely
to specify their gender. Out of curiosity, we also examined the relationship
between taboo and the reported gender of those 19,678 individuals who did
choose to report it. We found that 8.6% of contributors to taboo articles who
specify their gender specified female as compared to 8.9% in the comparison.
We found that among account-holding contributors who choose to specify their
gender, the relationship between reporting one’s gender as female and
contributing to a taboo subject is not statistically significant
($\chi^{2}=0.345:p=0.557$).

Finally, we consider emailability. We find that 42.6% of contributors to taboo
articles have made themselves emailable, while 38.4% of contributors to the
comparison set have done so. We found that the relationship between editing a
taboo article and setting oneself to be emailable is statistically significant
($\chi^{2}=149.8:p<0.001$). This result is contrary to H5E; contributors to
taboo subjects are more likely to be emailable rather than less.

Of the five measures we used to operationalize different facets of
identifiability, two were in the hypothesized direction: contributors to taboo
subjects are less likely to have accounts and have less experience. We were
surprised to find that accountholders who contribute to taboo subjects were
more likely to have user pages, to reveal their gender and to make themselves
emailable.

##  7\. Discussion

###  7.1. The Success of Taboo Articles on Wikipedia

Wikipedia articles on taboo subjects are very popular. Although one might
suspect that societal opprobrium would suppress the quality of articles about
taboo subjects, our results suggest the opposite is true. On Wikipedia,
articles about taboo subjects receive more contributions and are higher
quality than other similar articles. However, these articles also receive more
low quality contributions than non-taboo articles do. We investigated whether
techniques for limiting identifiability served as an important factor in the
success of these articles and concluded that this relationship is not a simple
one. Although contributors to taboo subjects were less likely to use accounts
and tended to have less experience when compared to those who contribute to
non-taboo subjects, we also found that that among the accountholding editors
in our sample, those editing taboo subjects were more likely to have user
pages, to specify their gender, and to make themselves emailable than those
who did not edit taboo subjects.

One potential explanation for our results with respect to identifiability may
be the phenomena described in Menking et al. (2019) who described
sophisticated means that women contributing to Wikipedia employ in order to
contribute safely. Although Menking et al. (2019) describes “choosing what to
edit” as a safety strategy, they also describe personal qualities that allow
women to persist in the face of attacks. Contributors to taboo subjects may
conceive of themselves as “people who can take it” in the words of one of
Menking et al.’s (Menking et al., 2019) interviewees.

Or, in that the success of peer production projects may be tied to the
diversity of motives from participants (Benkler, 2006), it may be that
contributors to taboo subjects are specifically motivated with respect to the
taboo subjects to which they choose to contribute. For example, they may take
an advocacy position with respect to women’s health information. Investigating
this subject further might require additional methods such as participant
interviews. Of course, an interview approach may struggle to include those who
contribute without accounts and more casual contributors who may be harder to
reach and recruit for interviews.

Taken as a whole, our findings introduce an empirical puzzle: How is it that
taboo articles are higher quality than non-taboo articles despite the larger
number and rate of poor quality and damaging contributions that these articles
receive from less identifiable contributors? One possible explanation may be a
mechanism Gorbatai (2014) elaborates: perhaps these unhelpful contributions
serve as a signal of public interest in a subject and draw in experienced
editors to clean up the mess left by less experienced contributors and who
also, perhaps, further improve the article while doing so. Our result provides
further evidence that the end result of unhelpful contributions may depend on
how the community responds, and reinforces the observation from Hill and Shaw
(2021) that increasing barriers to novice contributions (which may indeed be
lower quality) may have deleterious effects.

###  7.2. Further Exploration of Article Quality

Figure 4. Visualization of average article quality over time as predicted by
the Wikimedia ORES API shown using generalized additive model (GAM) smoothers.
We see that in the first several years of their existence, taboo subjects grow
somewhat more quickly in quality, but that their quality growth over time
begins to track more closely to the comparison set. Figure 5. Average quality
of the first version of new articles over time shown using generalized
additive model (GAM) smoothers. The rug along the axes identifies the areas
with the greatest concentration of data.

Given our surprising finding on the quality of articles about taboo subjects,
we conducted additional analysis. Figure 4 visualizes the quality growth
trajectory of taboo and comparison articles over time using local regression
(LOESS) models. Quality is as predicted by the Wikimedia ORES model on a per-
article-per-month level. Articles in these two sets are different early in
their existence but generally follow similar trajectories after several years.
Figure 5 shows the average quality of articles when they are created. The
initial quality of taboo articles and comparison articles in the early period
of Wikipedia were fairly similar, but recently created articles are higher
quality at the point of creation. Additionally, we have included a cross-
tabulation of whether contributors were accountholders, which sample they
appear in, and whether their contribution was reverted, in our online
supplement. These results suggest that one potential explanation for the high
quality of taboo articles is survival bias. For an article to survive long
enough to enter our dataset, especially those articles created recently, it
must have had sufficient quality when created in order to convey its value.
Initial quality and growth place taboo articles further ahead in their
development, even as maintenance and enhancement of both groups of articles
eventually follow similar trajectories. One explanation for the preliminary
differences we observe may be that individual editors exercising quality
control in Wikipedia may scrutinize new taboo articles even more closely than
they do other articles, perhaps drawn in by their own interest in taboo
subjects or a sense that taboo subjects may draw in the creation of spurious
articles.

Another part of the explanation for the quality of taboo articles may be the
presence of health-related content in the taboo set. Wikipedia has specialized
guidelines around health and medicine sourcing and is home to an organized
effort to uphold those guidelines (WikiProject Medicine) (James, 2016;
Trevena, 2011). However, this is a question in need of additional research
because many such organizations and content guidelines exist in Wikipedia, and
because the existence of strict guidelines and the presence of a group
enforcing those guidelines could serve to decrease the proportion of
contributions ultimately deemed acceptable.

###  7.3. Implications For Design

Our results indicate that there is substantial reason for optimism with
respect to the availability of knowledge about taboo subjects and that online
communities like Wikipedia can, in some cases, resist societal norms in ways
that support human thriving. This result is particularly striking because our
setting is Wikipedia. As a tertiary source, an encyclopedia is dependent on
the availability of secondary content in the form of publications from
reliable sources. There may be much more we can learn from this success as we
seek to replicate it in other settings.

Contributors to taboo subjects contradicted our expectations of low quality
and maximal privacy. Our finding that contributors to taboo subjects used some
privacy affordances but not others suggests that collaborative platforms
should be wary of assuming that participants have a uniform perspective on
their privacy. Indeed some may prefer to make themselves more identifiable
despite the potential increased risk. However, policy changes that would
increase requirements for identifiability may endanger this success, at least
with respect to the contributors who appear in our data as non-accountholders
or who have a lower overall edit count. Many peer production projects,
including some language editions of Wikipedia, have policies requiring that
participants make themselves even more identifiable than they are required to
be on English Wikipedia. Our finding that contributors take a variable
approach to privacy suggests designers exercise caution when considering
privacy-sacrificing features because a lack of privacy may limit the
participation of people seeking to engage with taboo subjects. Further, these
results suggest that future studies of taboo in social computing systems
should examine privacy-seeking behaviors among different roles (e.g., reader,
contributor, administrator).

We also observe that identifiability is multifaceted. In this case, we find
differences even in these relatively weak identifiers: these are not “real
name” policies, user pages could contain content of any kind, none of the
information being provided is verified, and although participants can opt into
emailability, doing so does not expose their email address. Affordances that
seek to make participants more identifiable on platforms have been found to
have unwanted side effects that go beyond discouraging participation (Hill and
Shaw, 2021). Additionally, diminishing contributor choices on what information
about them is collected and shared may diminish their willingness to
contribute to subjects that may place them at risk. In turn, this could lead
to lower-quality, neglected articles. Such an outcome would disproportionately
harm information-seekers most in need of reliable information.

###  7.4. Implications for Future Research

Our methods have potential utility for future studies of taboo across HCI and
across languages. Although we selected English corpora, Wiktionary exists in
186 different languages and Wikipedia in more than 300. Further, although
there may be some advantage to the fact that both Wiktionary and Wikipedia
operate under the same umbrella foundation and have overlapping communities,
other dictionaries and sites of collaborative production could be examined in
a similar manner.

Numerous other dictionaries use sense tagging to indicate that a definition
corresponds to euphemistic usage. Further, there are many other sense tags in
use, including the identification of slang, expletives, epithets, jargon,
dialect, and archaic usage. Connecting linguistic scholarship about these
forms of language to other social computing phenomena may be fruitful.
Further, a computational approach of this kind is scalable across multiple
settings, including those where human inspection is infeasible because of
volume. We have made an anonymized dataset and our analytical code available
for replication and reuse of our findings at
https://doi.org/10.7910/DVN/5OKEEO.

##  8\. Limitations

Our method of taboo identification through NLP techniques lacks important
nuance and context. Because taboo varies across cultures, behaviors that are
profoundly polluting for some may carry little or no taboo for others. We are
researchers with relatively high levels of privilege with respect to freedom
of inquiry; this positionality has inevitably influenced our results. Our
profession, discipline, and employers act to legitimize our engagement with
taboo subjects, which may in turn desensitize us to the risks of violating
taboo.

Our ability to generalize these results to other contexts is limited by the
fact that we only examined English language Wikipedia. This limitation
reflects our language knowledge, given the deeply linguistic nature of the
subject matter. Given that taboo is substantially influenced by local and
cultural factors, our findings might be different in other language contexts.
We hope that authors with a broader range of language backgrounds will take up
similar questions in the future.

Additionally, contributions to Wiktionary, from which we drew our original
list of key words indicating taboo, may be biased. We know that Wikipedia
suffers from numerous participation gaps with respect to gender, skills, and
race and we imagine these gaps might extend to Wiktionary as well in ways that
might shape the kinds of taboo that is reflected in our dataset (Shaw and
Hargittai, 2018; Hargittai and Jennrich, 2016; Hargittai and Shaw, 2015; Hill
and Shaw, 2013). Although we were unable to find data on participation gaps in
Wiktionary, surveys conducted across all Wikimedia Foundation projects (which
include Wikipedia and Wiktionary) have found gaps in
participation.111111https://meta.wikimedia.org/wiki/Community_Insights/Community_Insights_2021_Report
Given that taboo functions as social control, insofar as similar contributor
demographic gaps are present in Wiktionary, our identification of taboo
subjects likely reflects the attitudes of the people who are more privileged
and have more means to resist the consequences of taboo violations. Although
this remains an important unaddressed threat to validity, we believe that it
likely makes our method of identifying taboo conservative, if potentially
biased.

Further, Wiktionary has limitations as a source of data on taboo in that its
self-organized volunteer contributor nature and openness to non-experts may
lead to inconsistencies; euphemistic definitions may go untagged while non-
euphemisms may be erroneously tagged as such. For example, our taboo dataset
includes the article Super Bowl. The bigram “super bowl” is in our taboo
n-grams list because out of the five times it appears in definitions in
Wiktionary, two of them (the terms “superb owl” and “Big Game”) have
definitions that are tagged as euphemism. Although we would argue that this is
not a correct usage of the euphemism tag (instead, these terms seem more like
slang), we have included the Super Bowl article out of fidelity to the source
material. Taking on this descriptivist approach can add noise, but can also
expand our perspective. When we observed that our dataset included euphemisms
around nuclear weapons and napalm, we realized that these topics were not what
we had been expecting when we first began to explore taboo. Indeed, they might
have escaped our notice. However, once sensitized to this taboo through our
systematic approach, we were able to validate it and found that prior work has
indeed explored the taboo surrounding nuclear and chemical weapons (Schelling,
2007; Price, 2007; Cole, 1998).

Our measures are limited as well. Views data only examines page hits—not
whether the article was read. Contributions vary in their size, and their
assessed quality is subject to community values and biases. The measure of
article quality we have used, although common in Wikipedia research, only
assesses articles based on observable features such as length, links, and the
presence of images: it does not gauge content, and collapses quality down to a
single continuous measure despite being derived from an ordinal system of
quality classes that are not evenly spaced (TeBlunthuis, 2021).

We used several measures to assess contributor identifiability. By
constructing a user-level dataset with dichotomous variables for H5C, D, and
E, we omit variation over time, and we do not consider the inequalities in
participation between participants; some descriptive analysis of a revision-
level dataset is in our online supplement. Although we use the presence of
profiles as a sign of identifiability, people tell “privacy lies” by entering
false information into online profiles—sometimes in order to to avoid being
stigmatized (Sannon et al., 2018). We make no effort to address whether user
pages contain identifying information or whether the information provided is
correct. Selecting a gender from a drop-down menu may or may not align with
aspects of someone’s gender identity. Additionally, although we treat
contributing without an account as a sign of lower identifiability, the IP
addresses made visible as part of that contribution can be highly identifying.
Finally, we only have information on whether these traits were present at the
time we made our API queries in June 2022, and do not have historical
information as to whether gender or emailability had been set at the moment of
contribution. We attempt to mitigate these limitations through our use of
several different measures to assess identifiability.

Finally, although our method seeks to minimize our assumptions about what is
taboo, we made many choices throughout the process that may have influenced
our results. We chose stop words. We relied on our own sense of what qualifies
as taboo when checking our results for face validity during all points of
conducting this research project. Although we believe that these ongoing
informal assessments were consistent with the work of prior researchers, our
own perspective inevitably entered into the conduct of this work.

##  9\. Conclusion

Information-seekers turn to social computing systems to learn about subjects
that society has gauged to be taboo, and Wikipedia contributors have
successfully built resources to serve this need. This work makes three
contributions: (1) we elaborate a computational technique for detecting taboo
based on euphemistic dictionary definitions, (2) we illustrate this approach
by applying it to English Wikipedia, and (3) we analyze distinctions in both
how taboo subjects develop and who contributes to the development of
information about taboo subjects. Future work should explore the relationships
between identifiability and taboo subjects in other environments, continue to
unpack the social dynamics of taboo, and consider the impacts of policy
changes that diminish access to privacy-preserving technologies. Taboo
subjects connect to some of the most fundamental pieces of human existence and
both some of the most distressing and most uplifting parts of our social
experience. Although we as researchers may recoil from doing work on “not safe
for work” subjects, facing taboos and tackling them as area of inquiry is
worth the discomfort it may give us. Taboos shape our consumption and
production behaviors across social computing systems. Exploring taboo subjects
through the lens of HCI is a vital and tranformative area of future work for
our field.

###### Acknowledgements.

This work benefited substantively from the guidance of anonymous reviewers and
the program committee at CSCW. The creation of our dataset was aided by the
use of advanced computational, storage, and networking infrastructure provided
by the Hyak supercomputer system at the University of Washington and high-
performance computing systems supported by the Northwestern School of
Communication and the National Science Foundation. This work was supported by
the National Science Foundation (awards CNS-1703736 and CNS-1703049).

##  10\. Data and Code

A replication dataset including data, code, and other supplementary material
has been placed in the Harvard Dataverse archive and is available at:
https://doi.org/10.7910/DVN/5OKEEO

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