Linguistic Dead-Ends and Alphabet Soup: Finding Dark Patterns
in Japanese Apps
Shun Hidaka

Tokyo Institute of Technology
hidaka.s.ad@m.titech.ac.jp

Mizuki Watanabe

Tokyo Institute of Technology
watanabe.m.ca@m.titech.ac.jp

Sota Kobuki

Tokyo Institute of Technology
kobuki.s.aa@m.titech.ac.jp

Katie Seaborn

Tokyo Institute of Technology
seaborn.k.aa@m.titech.ac.jp

Figure 1: A new class of dark pattern, Linguistic Dead-Ends, and its subclasses, Alphabet Soup and Untranslation, identified
through a cultural- and language-sensitive approach within the Japanese app market context.

ABSTRACT
Dark patterns are deceptive and malicious properties of user interfaces that lead the end-user to do something different from intended
or expected. While now a key topic in critical computing, most work
has been conducted in Western contexts. Japan, with its booming
app market, is a relatively uncharted context that offers culturallyand linguistically-sensitive differences in design standards, contexts of use, values, and language, all of which could influence thepresence and expression of dark patterns. In this work, we analyzed
200 popular mobile apps in the Japanese market. We found thatmost apps had dark patterns, with an average of 3.9 per app. We
also identified a new class of dark pattern: “Linguistic Dead-Ends”

This work is licensed under a Creative Commons Attribution-Share Alike
International 4.0 License.
CHI ’23, April 23–28, 2023, Hamburg, Germany© 2023 Copyright held by the owner/author(s).
ACM ISBN 978-1-4503-9421-5/23/04.
https://doi.org/10.1145/3544548.3580942

in the forms of “Untranslation” and “Alphabet Soup.” We outline theimplications for design and research practice, especially for future
cross-cultural research on dark patterns.

CCS CONCEPTS• Human-centered computing → Human computer interaction(HCI); HCI theory, concepts and models; Interaction design; Interaction design theory, concepts and paradigms; Visualization;
Visualization design and evaluation methods.

KEYWORDS
Dark patterns, Deceptive design, Persuasive design, Ethical design,
App design, JapanACM Reference Format:
Shun Hidaka, Sota Kobuki, Mizuki Watanabe, and Katie Seaborn. 2023. Linguistic Dead-Ends and Alphabet Soup: Finding Dark Patterns in JapaneseApps. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23), April 23–28, 2023, Hamburg, Germany. ACM, New
York, NY, USA, 13 pages. https://doi.org/10.1145/3544548.3580942

CHI ’23, April 23–28, 2023, Hamburg, Germany

1

INTRODUCTION

Dark patterns (DPs) are a type of malicious interface design pattern that tricks or forces the end-user into taking an action that isdifferent from what they intend or expect, one that benefits the purveyor of the interface. DPs have rapidly gained attention in humancomputer interaction (HCI) research and practice [10, 16, 32]. Dark
patterns are a topic that should concern everyone involved withwebsites and apps: the users who may be affected by them, the researchers who should sound the whistle on them, the designers who
unintentionally create them, and everyone involved with websites
and apps. In recent years, research has been conducted that directly
involves designers [15], under government supervision [40], andfrom a combined perspective of law, ethics, computer science, HCI,
and others [17]. Previous research on DPs includes the proposal ofa DP taxonomy [5, 8, 16], the analysis of DPs in different categories
of apps [9], the analysis of DPs in specific contexts and the creationof a taxonomy from these designs [28], and research related to user
perception of DPs [9]. Several studies on the presence, frequency,
and user impact of dark patterns have been conducted, mainly inEurope and the United States, i.e., Western contexts, where DPs
have been grouped together to create distinct and easily understoodclasses1 , and axes have been established to determine if they are
DPs to be used in various studies, such as DP distribution studies and user research. However, Eastern contexts, such as Japan,
may approach DPs differently or not at all. Cultural differences,including with respect to design in general and specifically within
e-commerce contexts, as well as language differences, may influence whether and how DPs are employed. As such, the presence,
frequency, classes, and features of DPs within the Japanese context
may differ from what has been discovered in the Western researchso far. At present, there appears to have been no research on DPs
in Japan.
Opportunities for the Japanese public to encounter UIs that employ DPs are increasing every year. For example, according to research by Japan’s Ministry of Internal Affairs and Communications, the amount of time spent online per day on weekdays for all
age groups in Japan increased by 39.6% over five years from 2015
to 20192 . Furthermore, research by Japan’s Ministry of Economy,
Trade and Industry indicates that the size of the e-commerce market
has expanded by about 40.6% over five years from 2015 to 20193 .
While no research to date has evaluated the Japanese context, thebooming e-commerce market and widescale use of mobile devices
and apps raises grave concerns about the existence and extent towhich DPs influence Japanese consumers. To limit these risks andprevent harm, it is important to understand what DPs, if any, arebeing employed, to what extent, and how they might impact endusers. Moreover, if there are DPs particular to the Japanese culturalcontext and language, they are as yet unknown. However, marking
out scale and effects of DPs within the Japanese context will require
addressing these potential, hidden DPs.
1 For consistency and to avoid confusion between DPs and where the apps weresourced from (“app categories”), we refer to high-level DP categories as “classes” (rather
than “types” or “categories”) and DPs under these classes as “subclasses” (rather than“subtypes” or “subcategories”).
2 https://www.soumu.go.jp/johotsusintokei/whitepaper/ja/r02/html/nd252510.html
3 https://www.meti.go.jp/press/2021/07/20210730010/20210730010.html

Shun Hidaka et al.

To this end, we conducted the first study on DPs in Japan. We
aimed to clarify the distribution and frequency of dark patterns
in Japan for researchers, designers, and users. We also aimed toanalyze and clarify dark patterns from three perspectives: exploring
the Japanese context with comparison to previous studies; determining any differences in dark patterns over time; and analyzing
results based on differences in the devices used. As such, we sought
to answer the following three research questions: (RQ1) What kindsof dark patterns are present in Japanese apps? (RQ2) How are they
distributed by class, subclass, and across app categories? (RQ3) Whatdifferences are there in the presence and/or frequency of dark patterns
between the Japanese and the US app markets, if any? To answer
these questions, we replicated an existing dark patterns research
protocol for the context of Japan. Specifically, we used the qualitative expert classification protocol of Di Geronimo et al. [9]. For
this, we analyzed a total of 200 apps (25 apps in each of eight app
categories) and classified the DPs we found into six classes. Fiveclasses were devised by the formative research of Gray et al. [16]and Di Geronimo et al. [9], and one class, Linguistic Dead-Ends,
was newly discovered and defined by us during the course of thisstudy. Our work has three main contributions:(1) Discovery of a novel class of DP not previously found, which
we have termed the “Linguistic Dead-End,” and its two subclasses,“Untranslation” and “Alphabet Soup,” that may be found outside of
the Japanese context;(2) Empirical evidence of DPs across app categories that alignwith the DP taxonomy devised by Gray et al. [16] and refined by
Di Geronimo et al. [9], thereby extending an established model ofDPs to the context of Japan; and(3) A cross-cultural comparison of findings from previous research in Western and/or English-speaking app contexts, indicating
significantly less representation of DPs in the Japanese context,
specifically an average of 3.9 compared to 7.4.
This work acts as a bridge in critical computing and interaction
design between the East and the West. It is expected to influence
how research on DPs is conducted in Japan and around the world,
especially in terms of being sensitive to differing cultural and language contexts.

2

BACKGROUND

Critical computing scholarship has pointed to ethical issues in thedesign of modern interfaces and technologies [6]. Dark patterns
have subsequently become a feature of the discourse within HCI
spaces, both academic and otherwise [10, 32]. We review the workso far and discuss how we extend it to the case of the Japanese
app market. We also justify our positioning within a larger shift
towards inclusive and intersectional research in HCI [22, 24, 35].

2.1

Dark Patterns and Malicious Interfaces

Dark patterns have become a hallmark of research on ethical design
practices and ethical HCI. The term “dark patterns” was coinedby Brignull in 2010 when he registered darkpatterns.org [8] (nowcalled Deceptive Design). Dark patterns are part of the user interface(UI), typically as visual interactive elements embedded within auser function or responsive to user engagement. For instance, thePreselection DP [16] is a design pattern in which checkboxes forLinguistic Dead-Ends and Alphabet Soup: Finding Dark Patterns in Japanese Apps

app notification settings and permission to receive newsletters arechecked before they are selected by the user, which can lead to thedelivery of unsolicited notifications and newsletters that the user
does not want. Since 2010, ethical designers and researchers in HCI
and interaction design have focused on finding new classes of DPs[5, 11, 16], studying the distribution of DPs across websites and appsin specific contexts [3, 9, 28], investigating user perceptions andreactions to DPs [4, 9, 11, 14, 26, 30, 40], and better understanding
the perspective of designers on DPs [15]. Building on Brignull’s
initial examples of DPs, Bösch et al. [5] created classes related
to privacy. Gray et al. [16] then devised a hierarchical taxonomy
consisting of five classes: Nagging, Obstruction, Sneaking, Interface
Interference, and Forced Action. These were then refined by Di
Geronimo et al. [9]. The variety and nature of DPs are expected to
evolve as research progresses and technology changes—and newcontexts are explored. To this end, we use the version of the DP
taxonomy created by Gray et al. [16] and refined by Di Geronimo
et al. [9] as a starting point to explore the uncharted case of Japan.
Much of the work so far has focused on determining the existence
and distribution of DPs across a range of UIs and contexts of use.
In their study of the distribution of DPs, Mathur et al. [28] used an
automated crawling system to explore DPs on 11K shopping sites
and found that DPs were present on at least 11.1% of the sites, andthat the higher the site’s Alexa ranking, the more likely a DP wasto appear. DPs have also been explored in browser cookies, which
temporarily store user information on websites. Krisam et al. [21]found that many of the 500 most popular websites in Germany useda deceptive design that visually led the user to "allow all" cookies,
attempting to achieve the designer’s intent in a natural way, evenin UIs that involve user privacy. Di Geronimo et al. [9] detected
DPs in the recordings of user interactions across 240 free appsin eight major categories downloaded from the US-based Google
Play store. They focused on the 30 top-ranking apps per category,using a predefined procedure to find the types and number of DPs
that appeared in the app UIs. They showed that as many as 95% of
the apps used at least one DP, with an average of 7.4 DPs per app
category. We built on this work by considering the app market in
Japan, which has not yet been explored.
More and more research has been conducted on people’s reactions to DPs. Bongard-Blanchy et al. [4] found that generation z, y,
and x, in that order, were more likely to recognize DPs; moreover,college graduates were more likely to recognize DPs than those
who had not graduated college, i.e., age and education were correlated with the ability to identify certain DPs. In Di Geronimo etal. [9], people of different nationalities and backgrounds from over
40 countries were recruited to identify DPs. Of these, 25% noticed
malicious designs and 24% were able to correctly detect DPs. Thissuggests that most people will not be able to recognize DPs andother designs of malicious intent correctly. Gray et al. [14], througha cross-cultural and cross-geographic survey of “manipulation” experiences in English- and Chinese-speaking countries, found that
more than 80% of respondents had experienced distrust while using mobile apps and websites. Even when people feel unease, they
may be unable to recognize DPs when they experience them, aphenomenon known as “DP-blindness” [9, 25]. As Di Geronimo etal. [9] discovered, experts are not immune. As such, we have taken

CHI ’23, April 23–28, 2023, Hamburg, Germany

measures to avoid DP-blindness, specifically by evaluating apps for
DPs in pairs.

2.2Intersectionality and WEIRD Research for
Ethical HCI

Ethical design recognizes design as an act of power, where designers,
developers, and purveyors of technology aim to influence the user
experience (UX), embed values into interfaces and interactions, andshould reflect on what responsibility they have to those on thereceiving end [12, 18, 36]. DPs and by extension DP research are no
exception. Critical explorations of DPs are a social good. However,a reflexive, critical lens raises an issue about the work conductedso far. Since the introduction of DPs by Brignull in 2010 [8] andthe resulting take-up in HCI and adjacent spaces, DP research has
become an active area of study in the West. Research on DPs has
been conducted in or using material from the US [3, 9, 16, 25, 28, 40],
Germany [5, 21], the UK [4, 11], Italy [30], and French-, Italian,and English-speaking European nations [17]. However, to the bestof our knowledge, DP research elsewhere, notably in Japan andother Eastern nations with comparable industries, is virtually nonexistent. One known exception is from India [3] and another may
be the study by Di Geronimo et al. [9], but not enough details areprovided in the paper about the 46 nationalities recruited.
We can characterize this state of affairs as a form of sampling bias
called WEIRD: "Western," "educated," "industrial," "rich," and "democratic" nations [20]. While originally conceptualized by Henrich,
Heine, and Norenzayan within the broader scope of behavioural research [20], WEIRD sampling biases have been found across many
areas of study, including HCI and notably CHI [24]. Critical voices
and initiatives have called for an expansion of who we include in
research [22, 31, 35, 37]. While certainly a matter of inclusion andrepresentation, it is also a matter of rigour: we cannot necessarily
generalize from one population to the other, and at least we should
check. Indeed, Henrich, Heine, and Norenzayan’s seminal work
showed that generalizing across populations is risky if not outrightincorrect [19, 20]. Moreover, there are sure to be a range of populations experiencing DPs unawares and subject to any resulting
harms, as yet unknown and untraceable. From an ethical computing standpoint, we must take action. Japan, as we discussed above,
may be an ideal starting point, as a nation with a profile similar
to those nations previously targeted in DP research. Japan is an
Eastern nation with a technically savvy population and large-scale
app industry that includes offerings from abroad, such as Apple,
Google, and Amazon marketplaces. Exploring the case of Japan,
with its differences and similarities to the nations so far included in
DP research, may provide new and comparable insights, especiallyin the two major ways in which it differs: culture and language. We
offer this work as a first step.

3

METHODSOur goal was to replicate the qualitative expert classification study
by Di Geronimo et al. [9] for the Japanese app context so as to clarify
the similarities and differences in the subclasses and distribution of
DPs based on cultural and linguistic dimensions. This protocol was
registered on OSF4 before data collection and analysis on December
4 https://osf.io/jfs36

CHI ’23, April 23–28, 2023, Hamburg, Germany

14th , 2021, with an update on January 4th , 2022 to account for aslight change in procedure regarding the third rater.

3.1

Procedure

We aimed to replicate the procedure of the first study conducted by
Di Geronimo et al. [9]. However, we made some modifications due totime, budget, and device limitations, as well as to account for special
properties of the Japanese app market context, described below.
The general procedure followed the original study. We started bymaking screen recordings of the apps, according to the proceduredescribed in the Recording Methodology section below. Then, as
per the Classification Methodology procedure, pairs of researchers
identified the DPs in the recordings as a pair to account for potential
DP blindness [9, 25], as well as to identify new DPs or forms of
DPs related to the Japanese app context. We then analyzed the
distribution of the DPs by class and app category. We describe ourprocedure in detail next.

3.2

Corpus Generation

Three researchers gathered 200 apps from the Google App Store.
24 apps were selected based on their top Google Play ranking asof September 25th , 2021, and 176 apps were selected based on their
top Google Play ranking as of April 12th , 2022, using Sensor Tower
as a reference. The apps were selected based on the following threecriteria:
1. Must be available on the iOS platform and can be used onan iPad
2. Must be free to download
3. Must be trending in the Japanese market
We used Sensor Tower to, as much as possible, align the Japanese
app categories from the Google Play rankings with those sourced
in the Di Geronimo et al. [9] study. We used iPads because we wereable to procure Apple devices more quickly than other devices; notably, the usage ratio of iOS and Android devices in Japan was not
significantly different at the time of this research5 . Google Play has
34 major categories of apps. Following Di Geronimo et al. [9], we
tested 200 apps in eight major categories: Communication, Entertainment, Family, Music & Audio, News & Magazines, Photography,
Shopping, and Social. Similarly, we also excluded certain apps basedon the following six criteria:
1. Apps not currently available on the iPad
2. Apps not available in Japan
3. Apps that have already appeared in other categories
4. Apps that are launchers
5. Apps that are tied to a phone number and only one accountper person is allowed
6. Apps for which the main functions of the application can
only be used by logging in as a paying member

3.3

Recordings

Recording app use by video screen capture was done by threeresearchers. Three iPad Air (4th generation) were used for this,each installed with either iOS 15.5 or iOS 14.8 (resulting from an
iOS update during the research). Researchers either created a new
5 https://www.kantarworldpanel.com/global/smartphone-os-market-share

Shun Hidaka et al.

Apple ID using a new email address created solely for this researchor their own email address at the university. These were used todownload and create accounts in the apps, as needed. The screen
recording function of iOS was used to screen record the apps inuse. For this, researchers started the screen recording just before
app launch. All apps were recorded for 10 minutes. During these10 minutes, the researcher performed the following six operationsin this order, if they were available:
1. Create an account and log out
2. Close and reopen the application
3. Visit the product list page
4. Visit the settings page
5. Select the name of the product
6. Use the app according to its intended usage
This cross-app operation ensured consistency in the procedure.
However, it was not possible to discover dark patterns that do notappear within the first 10 minutes of typical app operation. None of
the apps used were opened before the start of the screen recording.
Additionally, we did not purchase any products or services throughthe apps. For e-shopping apps, there was a possibility that dark
patterns could be found during the purchase stage, but we did
not go that far and only went through the process just before
completion of a purchase. Finally, for apps that required subscribing,we subscribed to the free version.

3.4

Classification Process

After the Recording Method activity, pairs of three researchers
watched recordings of a total of 200 apps together and discussed
the DPs that they found. If the two could not agree on whether aparticular design was a DP or not, and/or which DP class it fell into,
we asked a fourth researcher to break the tie, as in Di Geronimo
et al. [9]. This method was used for all 200 apps in this study. We
aimed to make rational, consensus-driven decisions grounded in
the possibility of finding DPs not found in previous studies. We
did not count reappearances of the same DP that had already been
identified at least once in the app. That is, if both the design of the
DP and the context of its appearance were the same as a pattern that
had already appeared, we recognized it as the same DP and did notcount it as a new instance of a DP. This was done to ensure that theway the app was operated was not a factor in the resulting numberof DPs counted within that app. When different DPs appeared in
the same use case or when the same DP appeared under different
operations (e.g., a pop-up mode when opening the app or a pop-up
unexpectedly appearing when operating the app), each design was
identified as a different DP. When the same subclass of DP appearedin succession during a sequence of operations, it was recognized as
one DP if the transition was fixed as a flow, i.e., the user could notchange the app operation. If it was not fixed, and there was room
to open different screens depending on the user’s operation in the
middle of the transition screen, we counted each as a unique DP
instance.

3.5

Taxonomy

We used the DP classes and subclasses proposed by Gray and colleagues [16] for classification, and then fit each of the DPs we

Linguistic Dead-Ends and Alphabet Soup: Finding Dark Patterns in Japanese Apps

CHI ’23, April 23–28, 2023, Hamburg, Germany

detected to the subclasses later refined by Di Geronimo and colleagues [9]. Although well-described and applicable to most of theDPs that we found, we needed to extend these classes to fit theJapanese context. Specifically, we found a new class of DP, as wellas subclasses. We briefly describe the new DP that we discovered
here. We discuss findings related to this new DP in the Results andDiscussion sections later.

Linguistic Dead-End (Figure 1) is a new DP that
we discovered, referring to a design that prevents or
discourages users from understanding the interface
and/or content of an app by manipulating language ata fundamental level. This DP has two subclasses: Untranslation and Alphabet Soup. Untranslation DPs
act as a language barrier by using languages otherthan the language of the user for part or all of theapp so as to persuade or prevent understanding ofessential content or functions. Alphabet Soup refers
to information in the interface or content that is expressed using letters, numbers, and/or symbols fromthe local syllabary but without using real, understandable words or phrases from that syllabary, even whenthese exist and when the app otherwise correctly employs the local language, making the meaning difficult
or impossible to grasp.

Nagging refers to designs that use pop-ups or othermeans to persistently engage the user more than once
to realize the designer’s intentions.
Obstruction refers to an UI in which some barrier
makes it difficult for the user to perform the desired
operation smoothly. This includes Intermediate Currency, which allows users to make transactions using
only the in-app currency, not cash; Price Comparison
Prevention, which makes it difficult to compare product prices with other sites by making it impossible
to copy product names; and the Roach Motel, whichmakes it easy to get an account but difficult to delete
it or exit it.
Sneaking is a form of design in which the designer
attempts to make a profit by sneaking in information
relevant to the user without the user noticing. This
include Bait and Switch, which causes the user to dosomething different from his/her expected function,
Hidden Costs, which adds a portion of the price thatwas not shown to the user just before the purchase,
Sneak into Basket, where unwanted items are mixed
into the cart, and Forced Continuity, where the user
is not notified when the free or trial period ends.
Interface Interference is a design that tries to trigger a particular operation by making it easier or
harder to draw attention to a particular UI element.
This includes Hidden Information, which displays
small or grayed-out options that are not acceptableto make users accept terms of use and other conditions; Preselection, in which undesirable options forusers are selected even before they are manipulated;
and Aesthetic Manipulation, in which the use of the
UI disrupts users’ concentration. The child classesof the Aesthetic Manipulation class include Toying
with emotions, which uses UI design to induce specific actions, False Hierarchy, which makes one ormore choices stand out among multiple choices, andTrick Questions, which tries to mislead the user into
making a decision by using double negatives.
Forced Action is a design in which users who aretrying to perform a particular operation are given different tasks to go through to do so. It includes Social
Pyramid, in which users can earn rewards by inviting
friends to the app, Privacy Zuckering, in which users
are forced to provide more information than necessary, and Gamification, in which users are forced toperform tasks that are difficult to accomplish without
paying.

We used all six DP classes, treating the new Linguistic Dead-End
DP as an addition to Gray et al.’s [16] taxonomy. We discuss the
new DP and its subclasses in detail with examples in the Resultsand Discussion sections.

3.6

Deviations from the Original Protocol

Our methodology differed from that Di Geronimo et al. [9] in a few
ways that we can classify under two broad headings.
3.6.1 Changes to the Recording Methodology. We used the labowned devices available only at the university, which were iPad
Airs, rather than Google devices. As such, we had to use the AppleStore rather than the Google Store. Moreover, apps not compatible with iOS were excluded. Screen recording was done using thescreen recording function in iOS, although we do not expect thisto substantially deviate from screen recording functions in otherdevices. We gathered a total of 200 apps, slightly less than the
240 analyzed by Di Geronimo et al. [9]; this was due to time andresource constraints within the research team.
3.6.2 Changes to the Classification Methodology. We did not include the Price Comparison Prevention DP subclass because it
cannot be identified from recordings. We extended the methodology for determining whether a pattern is a dark pattern. As asecondary confirmation measure, Di Geronimo et al. [9] handled
disagreements in a pair about DP designations by having a third
party cast the final vote; they did this for 40 apps. We applied thismeasure to all 200 apps in our corpus as the main mode of DP verification. We also aimed to increase the objectivity of this approachby using three different pairs of researchers rather than one pairof two researchers. Time constraints and COVID-19 restrictions
forced us to work in pairs online (via Zoom).

4

RESULTS

A variety of DPs of varying frequencies across apps and app categories were identified. We provide our anonymous ratings data

CHI ’23, April 23–28, 2023, Hamburg, Germany

Shun Hidaka et al.

Figure 2: Examples of the Untranslation DP, the most common subclass of Linguistic Dead-Ends in the Japanese app corpus.
online6 . We start by covering the new DP and its subclasses before
going into the results in detail.

4.1

A New Dark Pattern Discovered in theJapanese App Context (RQ1)

Over the course of analyzing the Japanese apps for DPs, we founda new class of DP: Linguistic Dead-End. This is a design patternwherein language use prevents or makes it very difficult for theuser to understand crucial functionality, typically by using unfamiliar words of a foreign origin, even if the local syllabary is used.
Importantly, the purveyors of the app would benefit from users notaccessing, modifying, or leaving as default these features, functions,
and/or information in the app. The presence of proper Japaneseelsewhere in the apps suggests that the choices underlying theseDPs may have been on purpose, i.e., used in a deceptive way. Moreover, there are many free or affordable translation services available.
For example, iubenda7 and gengo8 can be used to translate evenlarge and complicated content, such as a TOS, faithfully and withrelative ease. Considering these points coupled with a clear benefit
to the purveyors, we conclude that these designs were chosen on
purpose. In other words, they are dark patterns. We found twovarieties, or subclasses, of Linguistic Dead-Ends in the Japanese
corpus: Untranslation and Alphabet Soup. We report on these DPs
in detail next. While we were not able to obtain ethics approval toshare specific examples in published reports, we have recreated themost common forms of Linguistic Dead-Ends in Figure 1, Figure 2,
and Figure 3.
6 Due

to ethics restrictions, we can only provide the anonymized version of
our data set, available on Google Sheets: https://docs.google.com/spreadsheets/d/
1KDqT8EAqjk48MpH65ZNp_kIwdMeC2hFNYezkSJlGpN4/edit?usp=sharing
7 https://www.iubenda.com
8 https://gengo.com

Untranslation refers to a design pattern in which part or all of
the app is in a language unfamiliar to the people using it, even if theapp is stated as available in the local language in the store (Figure2). Untranslation DPs prevent or make it exceedingly difficult for
the user to access or understand the necessary and/or sensitive
information that, by their ignorance, may benefit the purveyor.
Notably the apps do not provide a translation function for these
sections. A frequent example in our Japanese corpus was the Termsof Service (TOS) being in English. In other cases, almost all functions
were in a foreign language/s, even though the app description
indicated that the app should be in Japanese. In still others, chatmessages sent by the operator were in a foreign language.
The other variety of Linguistic Dead-End, Alphabet Soup, covers
cases where a word of foreign origin is expressed using the local
characters, symbols, or numbers (the correct syllabary), such thatthe meaning cannot be understood even though the characters canbe read (Figure 3). This gives the impression of legitimacy, since thecorrect syllabary is used. This DP may also exist alongside correctuses of the language. In our corpus, we found cases where certain
functions that may be understood in English were written in Japanese katakana, which is often used to represent foreign languages,
even though there are legitimate and common Japanese words forthese functions. Another example related to money formatting;for instance, ¥1.25 uses the Japanese yen prefix but the use of aperiod suggests that it is another currency, likely USD. We also
found cases where this DP was paired with other DPs, notably
Trick Questions and Double Negatives, making the UI even moredifficult to understand, even for Japanese users who know someEnglish.
Overall, we found 33 instances of Linguistic Dead-Ends across
200 apps, with 29 apps containing at least one Untranslation DP
and four with at least one Alphabet Soup DP; of these, two apps
contained both. Notably, the distribution of the Untranslation andLinguistic Dead-Ends and Alphabet Soup: Finding Dark Patterns in Japanese Apps

CHI ’23, April 23–28, 2023, Hamburg, Germany

Figure 3: Examples of the Alphabet Soup DP, another form of Linguistic Dead-Ends in the Japanese app corpus.
Alphabet Soups subclasses differed. The app categories with themost Untranslations were, in descending order, 8 in “Communication", 6 in “Photography", 5 in "Music and Audio", 4 in “Family" and“Entertainment", and 2 in “Social." The app categories in which themost Alphabet Soups were found were, in descending order, 2 in“Communication” and 1 in each of “Photography” and “Music andAudio.” A paired t-test found a significant difference in the numberof Untranslation and Alphabet Soup DPs across app categories, t(7)= -3.96, p = .006, indicating there were far more cases of Untranslation in our corpus. The lack of coappearance of these subclasses in asingle app suggests that they were not deployed as part of a unified
DP strategy. Rather, each uniquely takes advantage of deception
through linguistic means.
The app developer’s country of origin might also have played arole. For Untranslation, countries of origin were: 11 from the USA,
five from Korea, four from China, three from Japan, and one fromeach of Israel, France, Sweden, India, Belarus, and Norway. For
Alphabet Soup, two were made in the USA and one each were from
China and Israel. Notably, most instances of Untranslation were
found in apps created by American developers or Asian nations,
including but not limited to Japan. Also, all instances of Alphabet
Soup were created by non-Japanese developers. Additionally, thetwo apps in which both subclasses of Linguistic Dead-End were
present were created by a linguistically close Asian nation (China)
and a satellite location of a Japanese company (Israel). The numbers
are few, so we should take caution when interpreting these results.
Yet, they suggest a link between the linguistic savvy of outside
nations and deployment of more sophisticated Linguistic DeadEnd strategies involving Alphabet Soup or a combination of bothsubclasses.
We include these new DPs in our taxonomy and analysis, towhich we now turn.

4.2

Distribution by Dark Pattern Sub/Class(RQ2a)

We first considered the relative distribution of DPs by class andsubclass of DP. We begin by describing the cases in which DPs
were embedded within the app relative to other UI elements andfunctions. Table 1 illustrates the DPs cases that we found, what
sub/class/es of DP applied to each case, and a qualitative measureof the frequency of occurrence: single (S) or multiple (M). For this,we used the same categories defined by Di Geronimo et al. [9]. By“single” we mean that within one app, there was only one instanceof the given DP case. By “one” we mean that either (i) the caseoccurred only once, e.g., a single popup, or (ii) its occurrence wascontext-dependent, only found in one place within the app, e.g.,always on the checkout screen. By “multiple” we mean that there
were multiple instances of the given DP case within a single app,either in terms of (i) frequency of appearance or (ii) where it could
be found, i.e., the context. Note that both S- and M-type DP cases
could be found across several apps in our corpus.
Among the 200 Japanese apps in our corpus, 93.5% had one ormore DPs in their interface, which was close to the 95% found in the
US corpus of apps in Di Geronimo et al. [9] An average of 3.9 DPs
per app (SD: 2.7, MD: 3) were identified compared to an averageof 7.4 in by Di Geronimo et al. [9]. Figure 1 shows the number ofDPs found across all apps. 36.5% of the apps had two or fewer DPs,
46.5% had about 3-6, and 17% had 7 or more DPs.
Each app contained an average of 2.4 (SD: 1.1, MD: 2 ) DPs
categorized under one of the six DP classes, including the fiveclasses devised by Gray et al. [16] and the Linguistic Dead-End
DPs found in our study. Considering the 16 subclasses outlined
by Gray et al. [16], as well as Untranslation and Alphabet Soup,
each app contained an average of 2.85 DP subclasses (SD: 1.6, MD:
3). The subclasses of DPs that were found to be more prevalent

CHI ’23, April 23–28, 2023, Hamburg, Germany

Shun Hidaka et al.

Table 1: Specific DP cases, the DP classes or subclasses to which they belong, and their relative frequency.
DP Case

Sub/Classes

S/M

A pop-up appears and interrupts the user in their task
Pop-up to rate
Display of cookies, etc. that appear all the time on some parts of the screen, with no option toturn off the display.
Forced to watch a video for a few seconds when starting the app or after making certain
selections
Multiple currencies
When purchasing an item, user will be prompted to purchase more Intermediate Currency
than required.
Charging an amount that includes fees not previously shown on the screen just before orderconfirmation
User clicks a feature (which does not look like a premium) and get a PRO ad or open AppleStore
User can’t unsubscribe from the newsletter on the app
Unable to logout or delete account within the app
Terms of service is small and\or grayed out
Terms of use is not displayed during tutorial or member registration
The notifications (and\emails and SNS) are preselected
The option is preselected
Countdown offer
Persuade the user to make the desired choice just before begging for permission.
There are two or more options, but that is more beneficial for them is more prominent interms of font, button size, position and frame
Ad appears as normal content
Forced to watch a video for a few seconds when starting the app or after making certain
selections
In the negative form expression of the setting, the check box is not selected, and the one that
benefits the company is pre-selected
Private settings related dark patterns
Words and content in the UI and content are presented in a foreign language, and notranslation function is provided in the app.
The local syllabary is used to express the unfamiliar foreign words even when a word in thelocal language exists and the rest of the app is in the local language.

Nagging
Nagging
Nagging

M
SS

Nagging, Forced Action

S

Inter. Currency
Inter. Currency

S
S

Sneak into basket

S

Bait & Switch, Disguised Ads

M

Roach Motel
Roach Motel
Hidden Info.
Hidden Info.
Preselection
Preselection
Toying with Emotions
Toying with Emotions
False Hierarchy

S
S
S
S
M
M
M
S
M

Disguised Ads
Forced Action

M
M

Trick Questions

S

Privacy Zuckering
Untranslation

S
S

Alphabet Soup

S

throughout were, in descending order of frequency, False Hierarchy(n=204), Preselection (n=139), and Nagging (n=138). The bar graphin Figure 1 indicates how much of each subclass of each DP classappeared in each of the 200 apps in our corpus. Most apps (54.5%)
contained Preselection, where an option was pre-checked to set a
subscription to email newsletters, or where notification permissions
were selected before user direction in the notification settings. 52.5%
had False Hierarchy, in which the designers intentionally madesome choices stand out over multiple other choices. Nagging, foundin 43% of the apps, was the third most common DP subclass. This
DP includes designs that display ads immediately after app launchor during screen transitions, as well as designs that require app
ratings for continued use.

4.3

Dark Patterns and App Categories (RQ2b)

Next, we considered the relative presence of DPs by app category(Figure 4). Certain DPs could be more prevalent in certain kindsof apps, as indicated by previous research [9]. For example, thepresence or absence of a billing component could influence thetype and degree of DPs present, and some categories may featurea greater number of paid features or in-app purchase than others,
depending on the service model. A Shapiro-Wilk test confirmedthat the distribution did not significantly differ from normal (W =.99, p = .63). We then performed an ANOVA test. We found no statistically significant differences between any of the app categories
with respect to the distribution of the number of DPs included in
the app, F(7, 192) = .88, p = .53. In short, all kinds of DPs could befound across any app category.

4.4

Dark Patterns in the Japanese versus US
Contexts (RQ3)

Since we used the Japanese equivalents of the app store categories
covered in Di Geronimo et al. [9], we were also able to compare
the relative frequencies of DPs by app category in our corpus andthe corpus of Di Geronimo et al [9]. Figure 5 summarizes the results. We note that this is not a controlled comparison, especiallyLinguistic Dead-Ends and Alphabet Soup: Finding Dark Patterns in Japanese Apps

CHI ’23, April 23–28, 2023, Hamburg, Germany

Figure 4: Distribution of DPs among the app categories.
given that we used Apple devices and the Apple Store rather than
Google-compatible devices and the Google Store. However, as described above, we strived to keep our study setup and corpus ofapps as similar as possible, with few deviations outside of the main
point of comparison: the cultural context. Indeed, we have little
reason to think that the Google and Apple offerings would deviatesignificantly. Indeed, developers may choose to publish an app on
one or both platforms, while other factors, such as revenue models,
may play more of a role in UI design choices than the particular
type (or category) of app [33].
With these points in mind, we turn to our results on the relative
distributions of DPs by context. The values were obtained by calculating the rate of occurrence of each DP in our study minus therate of occurrence of those DPs given in Di Geronimo et al. [9]. A
positive value indicates a higher rate of occurrence in our study,
while a negative value indicates a higher rate of occurrence in theirstudy. As the figure indicates, the difference in the occurrence rateof the Aesthetic Manipulation, Privacy Zuckering, and Roach MotelDPs, in that order, was large. On the other hand, Sneak into Basket,
Aesthetic Manipulation and Trick Questions, Privacy Zuckering,and Social Pyramid were found in only 1% of the apps in this study,and Hidden Costs was not found in any app.

5

DISCUSSION

In the process of categorizing DPs in apps within the Japanesemarket, we found that a great number contained malicious patterns
that were designed to induce the user to act or not act in specificways. Additionally, we identified a new class of DP that previous
work, largely based in or on the West, was unable to find. Our
comparisons of the Western and Eastern contexts also revealed

that the occurrence rates of some DPs designs were very different.
We now discuss the implications of these results with respect toprevious and future work.

5.1

A New Class of Dark Pattern: “Linguistic
Dead-Ends”

A major contribution of this work is the discovery of a new classof DP: the “Linguistic Dead-end,” and its subclasses “Untranslation”
and “Alphabet Soup." One reason why this DP may have been
missed previously is that most DP work has primarily been studiedin countries described as WEIRD [19, 20]. Henrich, Heine, andNorenzayan originally found, circa 2010, that 86% of the data usedin psychological research samples were from only 12% of the world’s
population. Although over a decade has passed since this landmark
paper, this pattern has recently been found in CHI research. Linxen
et al. [24] found that across CHI human subjects studies publishedbetween 2016 and 2020, 73% of the participants were from WEIRD
countries. Moreover, 52% of all 195 countries in the world had
no participant representation in those five years. Yet, as WEIRD
research has found, we cannot always generalize the experiencesand effects of phenomena across populations, even if we tend totreat WEIRD research results that way. As we described above, inDP research, population samples and national contexts are almost
exclusively WEIRD or ambiguous, at best. Given that previous
studies of DPs have been biased towards Western contexts, we
can say that DP research is not an exception to the general rule.
Nevertheless, we need to consider how facets of culture may affectwhat we know about DPs. Our work is not unprecedented, even
within HCI. For example, we know from previous research in asimilar context, namely web design, that design aesthetics can be

CHI ’23, April 23–28, 2023, Hamburg, Germany

Shun Hidaka et al.

Figure 5: Differences in the distribution of the DPs in our Japanese app corpus compared with Di Geronimo’s [9] study.
characterized differently depending on culture or country [7]. Our
study therefore highlights the importance of cross-cultural studies
and the limits of relying on research produced within and for certain
nations and cultural contexts. Japan is likely not an outlier; other
non-Western and non-English-speaking nations may also be at riskof DPs such as these. This can be confirmed by conducting researchin other non-WEIRD countries.
The prevalence of Linguistic Dead-Ends in the Japanese app context also deserves interrogation. One reason could be that foreignmade apps often become a trend in Japan. Considering the apps inwhich neither Untranslation nor Alphabet Soup were found was
also illuminating. For instance, apps developed by Japanese companies dominated the News & Magazines category. Also, in the
Shopping category, many fashion apps from Japan that focus on
Japanese brands and target, perhaps exclusively, Japanese clientele,i.e., by requiring a Japanese postal address, alongside commercial
apps by Japanese companies were at the top of the list. Conversely,
the Communication category—the most common category for both
Untranslation and Alphabet Soup—included browsers and online
communication platforms from competitors all over the world, albeit with English-speaking companies accounting for most of the
top spots.
Alternatively, Linguistic Dead-Ends may only exist in certain
contexts where the app creators and the app users have, or are
expected to have, some knowledge of different languages. In Japan,
for instance, the English language has long been a feature of public
education, and learning English became mandatory for elementary
school students in 2020 [13]. App designers may also be taking
advantage of “English as the global standard” even while knowingthat the intended users may not know English, thus providing mostof the app in the language of the users. Yet, this does not explain theoverreliance on Untranslation over Alphabet Soup. Perhaps blocks
of text are simply easier to manipulate, i.e., translate, especiallywith machine translation, which improves in naturalness as timegoes on. Notably, Alphabet Soup requires a more sophisticatedunderstanding of the local language. Indeed, the pattern we foundof outside nations with close cultural and/or linguistic ties, e.g.,
Korea and China, or inside knowledge of the local tongue, e.g., thesatellite office of a Japanese company, deploying both subclasses in
one app raises this as a stark possibility.
We must also consider the intersection of legal standards andsocial expectations. Untranslation, the more prevalent of the two,often (79% of the time) appeared in relation to the app’s ToS. A ToS
is a contract between the user and the creator/s and stakeholder/sof the app, i.e., a legal construct. Yet, ToS are well-known to beburdensome and confuse lay people by relying on legalese, jargon,
and vague terms, even though Japanese law regulates how they are
written. Specifically, Article 3 of Japan’s Consumer Contract Law
states that there is a need to ensure that the rights and obligationsof consumers are clear and plain for consumers9 . Even so, a study in
the U.S. found that 99.6% of ToS are often as long as academic papers[2]. Similarly, in Japan, a study on factors that make it difficult toread the ToS showed that 36% of 400 respondents agreed to the ToS
without reading them, with 123 citing length as the reason [39].
A “darker” reason could be what language the terms are writtenin; in other words, Untranslation may make the information in
the ToS more opaque, further reducing the probability of it being
read. Future work, ideally with the creators and purveyors of theJapanese and other non-WEIRD apps that house these Linguistic
9 https://www.shugiin.go.jp/internet/itdb_housei.nsf/html/housei/h147061.htm

Linguistic Dead-Ends and Alphabet Soup: Finding Dark Patterns in Japanese Apps

Dead-Ends will be needed to shed light on the reasons behind theiruse and the current reliance on Untranslations over Alphabet Soup.

5.2

Understanding the Distribution of Dark
Patterns across Classes and App Categories

A fairly even variety of DP classes and subclasses were found across
app categories. A critical look at how the categories may link to theDPs we found may be useful for making sense of these results. The
most frequently found DP was Preselection. Notably, the apps thatmost frequently included this DP did not involve user-generated
content, including those found in the Shopping, Entertainment,
Social, and News & Magazines app categories. In contrast, apps
without this DP either required or encouraged user-created content, such as Photography and Family apps. In other words, inthe former category, users consume material that the creators of
the app provide, while in the latter category, users themselves use
the app to create something. In the former case, it is possible toattract user interest by presenting content through newslettersand notifications. These are contexts in which Preselection canbe easily applied. However, in the case of apps that rely on usergenerated content, engagement relies on new content that the users
themselves create. Defaults and Preselection DPs for encouraging
or forcing user-generated content are difficult to imagine for this.
These characteristics may have led to the difference in the DP
distribution among app categories.
False Hierarchy emerged as the second most commonly found
DP. This DP often took the form of a combination of buttons that
had prominent colors, such as blue, red, or pink, and buttons thatwere less prominent, such as those with no outline or with grayedout text. Buttons colored blue are thought to evoke a sense of calm
and are often used by companies that have trust as a core value[27], while red is thought to evoke feelings of excitement [23].
Moreover, in the absence of white, blue, red, and vivid colors ingeneral tend to be preferred by Japanese populations [34], and these
preferences seem to map onto how False Hierarchy was designed
into the Japanese apps in our corpus. We may thus not be surprisedthat these colors were used in creating visual hierarchies, even
within the Japanese context. Given the relative importance of color
as a core element of visual UIs, we may also not be surprised to findso many instances of a DP that relies on how color is perceived by
the user.
The third most common DP was Nagging, which is a very common
DP the world over. As Mathur, Kshirsagar, and Meyer [29] explain,
Nagging imposes a cognitive burden on the user, with the goal ofwearing them down until they accept whatever option alleviates
the burden. In some sense, Nagging may be considered a neutral DP,
applicable to any app regardless of category or purpose, because
its value lies in pressuring the user to engage with the app in any
way that the creators define. In other words, this DP is not tied
to a specific goal, task, activity, context, theme, technical requirement, and so on. Given the relatively fewer frequency of DPs ingeneral compared to Western rates, it may be easier for app creators
working in the Japanese context to create and widely deploy suchgenerally applicable DPs.

CHI ’23, April 23–28, 2023, Hamburg, Germany

5.3

Building on the Findings of Di Geronimo etal. [9] Cross-Culturally

The work of Di Geronimo et al. [9] formed the basis of this research.
Indeed, we attempted to replicate their protocol to discover how
DPs operate within the Japanese context and outside of the Google
suite of devices and apps. Unexpectedly, we found far fewer DPs
overall. Differing app store guidelines and laws could help explain
this. We were also inspired to undertake this research because DPs
seem not to be widely known or discussed in Japanese circles yet;this could also indicate that app creators in the Japanese market are
only just starting to recognize and employ DPs. In other words, DPs
may be novel in Japan, and only time will show if this difference isa feature of that novelty or not.
At present, can focus on the known issue of app store choice[33], which was also one of the main differences between our study
and that of Di Geronimo et al. [9]. We found that Aesthetic Manipulation, Privacy Zuckering, and Roach Motel, in that order, showed
the largest difference in occurrence rates. Aesthetic Manipulation,
which was rare in our Japanese corpus, was found in 33% of theapps in the US corpus [9]. This may be due to differences between
Google and Apple app store publication guidelines. According toApple’s App Store Review Guidelines:
Interstitial ads or ads that interrupt or block the user
experience must clearly indicate that they are an ad,
must not manipulate or trick users into tapping into
them, and must provide easily accessible and visible
close/skip buttons large enough for people to easily
dismiss the ad. [1]In other words, use of Aesthetic Manipulation DPs, such as moving ads and the use of small buttons for closing ads, will not pass the
App Store review process, even if they may be allowed by Google.
Privacy Zuckering was found in 31% of the apps used in theprevious study [9], while it was found in 7% of the apps in thisstudy. For apps found to have Privacy Zuckering in this study,
the ToS required permission to obtain user information. In otherwords, the app was designed in such a way that the provision ofuser information was essential to use the app, and thus it wasrecognized as a DP. Other apps that attempted to acquire users’
app usage data had their permission requested by Apple’s uniform
pop-up. The reason why this study found a very small number ofthis dark pattern compared to the study by Di Geronimo et al. [9]may be Apple’s criteria for app applications:Apps that collect user or usage data must secure user
consent for the collection, even if such data is considered to be anonymous at the time of or immediately
following collection. [1]5.4

Implications

Our findings confirm and extend the scholarship on DPs in key waysthat we expect will have implications for researchers, practitioners,
and the general app-using public.
5.4.1 Cross-cultural research on DPs is needed. As the first known
study on DPs in Japan, this work represents a first step towards a
global understanding of DPs. We drew from existing research, using
a qualitative expert evaluation protocol to explore the distribution of

CHI ’23, April 23–28, 2023, Hamburg, Germany

DPs for the case of Japan. This allowed us to make comparisons withresults from other countries. We found that DPs exist in the Japanese
app context, and in no small numbers or variety. User perceptionsof and reactions to design in general and DPs specifically may differ
between the West and the East, broadly speaking, or by culturalcontext, more pointedly. There is much room for verification in
Japan and other WEIRD and non-WEIRD nations for the greater
goal of knowledge production as well as the ethical goal of reducing
any harms, potential or actual, caused by DPs or arising from theirpresence with the larger app context. In addition, considering the
ethical murkiness of DPs, it will be important to collaborate withprofessionals and companies that work on UX with integrity.
5.4.2 Cross-cultural research on DPs should address language
broadly. Mobile apps may be used by a variety of people who speak
different languages and speak a given language to different degrees
across a range of countries. When translating or localizing apps,
it is necessary to consider possible cultural differences from a linguistic angle: sentences, vocabulary, syllables, numbers, currency,
and so on. User-centred design has a role to play here. For example,
Japanese and Korean have the concept of "honorifics," which areword endings, prefixes, or phrases that change depending on who
you are speaking to. These can indicate social hierarchies, respect
or a lack thereof, formality or informality, and even psychologicalcloseness [38]. If an app developed in a country without the conceptof honorifics is used in a country with the concept of honorifics,
the translation will not take this into account and could result inunintentionally influential designs, such as strong tone of voiceanchored to particular user prompts, or even DPs, such as Toying
with Emotion. As the new DP and its subclasses reveal, language
can be used to persuade and manipulate end-users. Japanese (andKorean) are just the beginning: future work will need to explore
other languages and syllabaries for the same or new DPs embeddedor reliant on how interactions are linguistically expressed.
5.4.3 Cross-cultural research on DPs should go beyond language.
While our main results related to replicating patterns found outside of the Japanese context and patterns related to the Japanese
language, we also found indications that other features of culturalexpressed in design and user interactions have a role to play. A key
example is colour and how it was used to create False Hierarchies:
the same DP, but with a Japanese flavour. We expect there to be
similar “flavours” for other DPs that may only be found throughdirect comparison of UIs and interactions, which we could not doin this work. Future work may take on such direction comparisons,especially of “the same” app, e.g., Uber, which is available in several
countries and languages. Future work can also involve controlleduser studies involving a diversity of participants, including consumers and design experts, who may be able to identify lower-level
differences in how DPs are visually or interactively expressed. This
would add a new, cross-cultural dimension to the work alreadybeing done with user studies of DPs [3, 4, 9, 14].

5.5

Limitations and Future Work

This work has several limitations. The new DPs that we found were
only assessed within the Japanese context; future work will need to
examine other language contexts to determine whether and to what

Shun Hidaka et al.

extent these DPs are found elsewhere. We also only considered apps
for smartphones and mobile devices; future work will need to consider other technology setups, including websites of various kinds,
software, and so on. Although we had planned to, were unable toobtain ethics approval to disclose our list of apps or conduct humansubjects research, due to concerns over published criticism of alarge corpus of commercial and non-commercial property. Future
work should seek to understand the effects on real or potential endusers through participant-based research methods. Future work
can also involve working with companies and designers directly,
carrying out interviews with the people creating and deploying
DPs about their ethical stances, whether they agree theses design
patterns are DPs, and what benefits they receive or expect to fromtheir deployment. Academic-industrial collaborations could also
work together to investigate the nature of DPs on end-users andconsumers through nonbranded but ecologically valid versions ofDP-containing apps as research instruments. Finally, developers
and companies could provide a list or means of deriving and accessing a record of app versions so as to chart out a history and timelineof DP integration for a single app and how each DP within theapp changed over time. This could provide fine-grained contextual
information of the spread of DPs and trends in DPs, which could becompared cross-culturally. Rather than take on this laborious andtime-consuming task manually, industrial collaboration or othermeans, such as automated detection or crowdsourcing, could enable
this line of research.

6

CONCLUSION

Dark patterns have become a hot topic in critical computing andinteraction design at large. In this work, we have provided evidence
that they exist at a broader scale in the case study of Japanese apps.
We have also shown the value in approaching research on DPsfrom a cross-cultural perspective, identifying a new form of DP that
may only be found with a culturally- and linguistically-sensitivelens. Future work will need to explore whether and how this DP
exists in other contexts beyond the West and in languages otherthan English. Moreover, the Japanese-specific properties of thisDP and its subclasses suggest the possibility of other, still hidden
DPs within other cultural and linguistic contexts that may only beunearthed by targeted, critical work. Critical computing research,
ethical design praxis, and the global e-commerce sector may benefit
from cross-cultural and intersectional initiatives within and beyond
apps.

ACKNOWLEDGMENTS
We thank the members and staff in the Aspirational Computing
Lab for supporting this work and providing early feedback. This
work was funded by departmental funds.

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