# About Engaging and Governing Strategies: A Thematic Analysis of Dark
Patterns in Social Networking Services

Thomas Mildner  0000-0002-1712-0741 University of BremenBremenGermany
<EMAIL_ADDRESS>,  Gian-Luca Savino  0000-0002-1233-234X University of
St.GallenSt.GallenSwitzerland<EMAIL_ADDRESS>,  Philip R. Doyle
0000-0002-8317-3374 University College DublinDublinIreland
<EMAIL_ADDRESS>,  Benjamin R. Cowan  0000-0002-8595-8132
University College DublinDublinIreland<EMAIL_ADDRESS>and  Rainer
Malaka  0000-0001-6463-4828 University of BremenBremenGermany<EMAIL_ADDRESS>
(2023)

###### Abstract.

Research in HCI has shown a growing interest in unethical design practices
across numerous domains, often referred to as “dark patterns”. There is,
however, a gap in related literature regarding social networking services
(SNSs). In this context, studies emphasise a lack of users’ self-determination
regarding control over personal data and time spent on SNSs. We collected over
16 hours of screen recordings from Facebook’s, Instagram’s, TikTok’s, and
Twitter’s mobile applications to understand how dark patterns manifest in
these SNSs. For this task, we turned towards HCI experts to mitigate possible
difficulties of non-expert participants in recognising dark patterns, as prior
studies have noticed. Supported by the recordings, two authors of this paper
conducted a thematic analysis based on previously described taxonomies,
manually classifying the recorded material while delivering two key findings:
We observed which instances occur in SNSs and identified two strategies —
engaging and governing — with five dark patterns undiscovered before.

SNS, social media, social networking services, interface design, dark
patterns, well-being, ethical interfaces

††journalyear: 2023††copyright: rightsretained††conference: Proceedings of the
2023 CHI Conference on Human Factors in Computing Systems; April 23–28, 2023;
Hamburg, Germany††booktitle: Proceedings of the 2023 CHI Conference on Human
Factors in Computing Systems (CHI ’23), April 23–28, 2023, Hamburg,
Germany††doi: 10.1145/3544548.3580695††isbn: 978-1-4503-9421-5/23/04††ccs:
Human-centered computing Empirical studies in HCI††ccs: Human-centered
computing HCI theory, concepts and models††ccs: Human-centered computing
Empirical studies in interaction design††ccs: Human-centered computing
Interaction design theory, concepts and paradigms††ccs: Security and privacy
Usability in security and privacy

##  1\. Introduction

“Reading this paper makes you a better person!” Emotionally pressuring
language is just one example of a growing body of unethical and malicious
design practices, often referred to as “dark patterns”. Brignull (Brignull,
2010) first introduced the term over a decade ago, describing dark patterns as
interface design strategies that coerce or steer people into actions they
would not necessarily engage in if fully informed (Mathur et al., 2019). Since
then, significant effort has been expended on identifying, capturing, and
describing examples of dark patterns, most notably in the domain of e-commerce
websites where a large corpus of dark patterns has been catalogued (Mathur et
al., 2019). There is, however, a need to extend this work further to capture
dark patterns across other domains, such as social networking services (SNSs).
Although SNSs are used extensively across the globe, a clear view as to the
types of patterns used on these sites and how these vary between major SNSs
platforms has yet to be defined. Existing literature does not focus
exclusively on SNSs (Di Geronimo et al., 2020; Bongard-Blanchy et al., 2021;
Gunawan et al., 2021; Fritsch, 2017) or focuses on specific elements only, for
instance advertising dark patterns only (Habib et al., 2022) and SNSs’
deletion processes (Schaffner et al., 2022). The work does, however, emphasise
an urgent need to deepen our understanding of how SNSs harm their users
through unethical design practices (Di Geronimo et al., 2020; Gunawan et al.,
2021; Habib et al., 2022; Schaffner et al., 2022).

Gaining this understanding is a critical step in informing current efforts to
legislate against such practices. Current legislation often limits regulation
to data and privacy protection, in many cases neglecting potentially harmful
consequences interface designs can have on individuals. For instance, both the
GDPR (Commission, 2016) and CCPA (Legislature, 2018) require website providers
to make their reasons for collecting data transparently whilst also offering
users the option to decline any storage of personal data. However, interface
designs that encourage excessive use of social media or designs that obfuscate
account deletion are currently unregulated. To support future regulatory
endeavours, such as a proposed draft for guidelines by the European Data
Protection Board (EDPB) (Board, 2022), we first need to understand how SNSs
use dark patterns and identify what domain-specific dark patterns might be
used on these platforms.

Our work contributes to this understanding by answering two key research
questions:

  * RQ1

What types of dark patterns are currently used in the four SNSs Facebook,
Instagram, TikTok, and Twitter?

  * RQ2

Do SNSs contain dark patterns currently unique to their domain?

To address these questions, we had six HCI researchers conduct reviews across
four widely used SNS platforms, based on their mobile applications: Facebook,
Instagram, TikTok, and Twitter. Reviewers were assigned to identify instances
of dark patterns whilst completing a range of tasks commonly carried out by
users on these platforms. The decision fell on HCI researchers as the ability
to not only recognise dark patterns but also to reflect and react to them was
deemed a crucial requirement for reviewers to generate best possible results.
Prior studies researching non-expert users’ ability to recognise dark patterns
demonstrate a significant difficulty for such tasks (Bongard-Blanchy et al.,
2021; Di Geronimo et al., 2020) which we aim to mitigate by relying on experts
instead. These sessions were recorded, producing 16 hours of interaction data,
which was then evaluated using thematic analysis. Based on screen recordings
created during the study, instances of 44 out of 80 previously established
dark patterns were identified. Thematic analysis of these identified five
consistent themes the describe SNS-specific dark patterns: (1) interactive
hooks; (2) social brokering; (3) decision uncertainty; (4) labyrinthine
navigation; and (5) redirective conditions. These themes were then further
organised into two overarching strategies that cover more high-level
incentives allowing for broader application: engaging strategies and governing
strategies. These emerge from SNS-specific incentives that differ from how
dark patterns are used in other domains. The strategies include two and three
types of the SNS-specific dark patterns. Falling under the umbrella of
engaging strategies are the interactive hooks and social brokering dark
patterns, which are designed to keep users occupied and entertained with SNS
for as long as possible. Whereas, SNS dark patterns that can be considered
governing strategies include decision uncertainty, labyrinthine navigation,
and redirective conditions, which are designed to navigate users’ decision-
making ability on these platforms. We contribute to the current dark pattern
discourse and literature by considering the impact dark patterns have on four
popular SNSs, and by extending current taxonomies with instances specific to
this domain.

##  2\. Related Work

In this section, we will review the relationship between research on dark
patterns and work on the persuasive design in social media. We highlight the
necessity to carry the dark pattern discourse over to SNSs, bridging the
current gap between these two strands of research. The first two subsections
present a brief overview of current dark pattern taxonomies, which were used
to guide the thematic analysis conducted in this study. The overview also
highlights approaches and methodologies used in previous work aimed at
identifying dark patterns. Some of these are adopted in this study which
builds on previous work to better understand dark patterns, specifically
within the context of SNS interface design. We then continue with work that
dealt with users’ perception of dark patterns and their ability to recognise
them in different environments. Lastly, we establish the importance of
considering dark patterns in social media while following the discourse of SNS
interface strategies that lead to problematic or even harmful usage behaviour.

###  2.1. Early Research On Dark Patterns

The past decade of research into dark patterns has defined and described a
comprehensive taxonomy of different types across several different domains. In
recent work, Mathur et al. (Mathur et al., 2021) offer a summary of the
current dark pattern landscape resulting in a dense taxonomy comprising
relevant works. This taxonomy is the result of their attempt to characterise
dark patterns based on the cognitive biases that they exploit. As this corpus
depicts a thorough overview of past dark pattern collections, we decided to
use this corpus as the basis for our study. However, we decided only to
include work based on empirical research, which means that we excluded
reports, such as the NCC (Council), 2019) and the CNIL (et Libertés, 2019)
that promote dark patterns to the public while recommending guidelines to make
informed decisions.

One of the earliest pieces of research on dark patterns was conducted by
Brignull (Brignull, 2010), who captured and described interface strategies
used to harm people through interface tricks. Producing the first taxonomy of
dark pattern types, Brignull described twelve interface tricks designed to
misguide users. Among these were dark patterns, such as sneak into basket,
hidden costs, and Price Comparison Prevention, which all operate by obscuring
certain information from users of online shopping sites. The intention here is
to inhibit customers’ ability to make informed decisions and potentially
mislead them into buying unwanted products. Alternately, the dark patterns
forced continuity, privacy zuckering, and roach motel use strategies that
limit the options and decisions available to people when using online
services. In the same year as, Brignull, Conti and Sobiesk introduced their
own taxonomy based on findings from a twelve-month-long study aimed at
cataloguing a wide range of malicious interface practices. This second
taxonomy includes eleven types of dark patterns, such as coercion, which
describes interfaces that mandate users’ decisions by restricting alternative
options and enforcing compliance. Other techniques noted by the authors
include interruptions that interfere with a user’s task flow and the
obfuscation of important information, both of which operate by hindering
informed decision-making. Elsewhere, Zagal et al. examined dark patterns in
video games, identifying seven types of dark patterns that specifically focus
on game mechanics (Zagal et al., 2013). The research shows that while certain
patterns exploit a game’s ecosystem of connected users, such as social pyramid
schemes and impersonation, others impact game-play experience like grinding
and playing by appointment.

Elsewhere, Greenberg et al. (Greenberg et al., 2014) consider dark patterns in
conjunction with proxemics theory (Hall, 1966). Identifying nine types of dark
patterns in total, the authors discuss interactions with potentially abusive
systems in spatial environments. For example, the attention grabber and
disguised data collection dark patterns could be used in the design of digital
billboards and involves brands exploiting people’s proximity and personal data
to deliver personalised advertising to specific pedestrians as they pass by.
In a similar vein – inspired by the concept of Privacy by Design project
(Hustinx, 2010) – Bösch et al. introduce nine further types of dark patterns,
which are effectively inverse strategies to the privacy strategies developed
in the Privacy by Design project. The work also highlights the role design
strategies can play in manipulating users both for good and for nefarious
reasons. Collectively, these early studies show that dark patterns can appear
in a variety of contexts and situations, highlighting the importance of
establishing a broad understanding of their origins.

###  2.2. Understanding the Origins of Dark Patterns

Reflecting on Brignull’s original work (Brignull, 2010), Gray et al. looked to
investigate how dark patterns are created in the first place. Here,
researchers adopted a qualitative approach, using established taxonomies to
analyse an image-based corpus of potential types of dark patterns (Gray et
al., 2018). The work defines five high-level strategies that practitioners
engage in when developing manipulative designs. For instance, the obstruction
dark pattern was used to make processes unnecessarily difficult, and
incorporates Brignull’s roach motel, price comparison prevention, and
intermediate currency (Brignull, 2010). In later work that adopts a similar
approach, Gray et al. analysed 4775 user-generated posts of the Reddit sub-
forum r/assholedesign (Gray and Chivukula, 2019). Following multiple
iterations of content analysis, the authors describe a set of six properties
of “asshole designers” that portray malicious motivations of designers. The
two-faced property, for instance, describes designers who offer conflicting
information that limits users’ ability to make an informed decision.
Understanding the origins of dark patterns as constraints under which
practitioners work offers relevant insights into where to look when trying to
recognise dark patterns anywhere.

Focusing more centrally on the frequency with which dark patterns are embedded
in online interfaces, Mathur et al. (Mathur et al., 2019) built a web-crawler
application to collect data from over 11K shopping websites. The work
identifies instances of dark patterns in more than $11\%$ of their samples.
Although this percentile already presents a significant number of occurrences,
the authors limited the breadth of their analysis by not analysing imagery
material and suggest that many more dark patterns could be identified had
other factors been taken into account. Nonetheless, guided by prior works from
Gray et al. (Gray et al., 2018) and Brignull (Brignull, 2010), the authors
were able to compose a taxonomy of fifteen types of dark patterns. Extending
these works further, Mathur et al. (Mathur et al., 2021) more recently looked
to identify clusters or relationships among established dark patterns, setting
the basis for this work’s thematic analysis. Taking the existing five high-
level characteristics from their previous work (Mathur et al., 2019), the
authors added a sixth characteristic that incorporates Zagal et al.’s online
gaming dark pattern disparate treatment (Zagal et al., 2013). Their proposed
model further categorises these six dark pattern characteristics into two
choice architectures that distinctly affect users: (1) modification of
decision space and (2) manipulation of information flow. The authors thus
propose an interesting three-tiered hierarchical framework (choice
architectures ¿ high-level dark pattern characteristics ¿ specific
manifestations of dark pattern designs) under which discovered and yet-to-be-
defined dark patterns can fit into. Overall, the above-mentioned work
collectively describe 81 specific types of dark patterns from various domains,
mostly unique in how they operate. However, we noticed an omission in regard
to SNSs. Filling this gap, we build on previous work by developing a deductive
codebook containing this taxonomy, which was then used during the execution of
the thematic analysis.

###  2.3. Recognising and Identifying Dark Patterns

With a more central focus on end-users’ perspectives of dark patterns, Di
Geronimo et al. (Di Geronimo et al., 2020) inspected popular mobile
applications sampled from the Google Play Store. The authors use a cognitive
walkthrough (Nielsen, 1994) to identify dark patterns across a total of 240
apps, each used for ten minutes. Findings showed that $95\%$ of the tested
applications contained dark patterns. In a second study, the authors evaluate
users’ ability to recognise dark patterns. Using an online survey, the work
suggests most users had problems recognising dark patterns. Adopting a similar
user-centred approach, Bongard-Blanchy et al. study peoples’ awareness of
manipulative interface designs and their recognition of dark patterns
(Bongard-Blanchy et al., 2021). The study, which consisted of an online survey
of 413 participants, also found that 59% of their participants were able to
identify “interface elements that can influence users’ choices” more than half
of the time, suggesting that they were somewhat able to recognise dark
patterns. This aligns with previous results from Di Geronimo et al. (Di
Geronimo et al., 2020) and Maier and Harr (Maier, 2020). However, the work
also showed that while participants understood what dark patterns were and how
they might manifest, they were still deceived by them during interactions.
Elsewhere, Gunawan et al. (Gunawan et al., 2021) use thematic analysis based
on video recordings of online services to study differences between the
various web modalities and resulting dark patterns. A contribution of their
analysis is the addition of a further twelve specific types of dark patterns
to the body of work. Their account deletion roadblocks dark pattern, for
instance, describes the insufficient communication between the service
provider and user when the latter is trying to delete their account. Together,
these works demonstrate processes for assessing and evaluating dark patterns
and how they manifest. In this work, we consider a wider corpus of dark
patterns on a relatively small set of applications. This allows us to offer
profound insights in SNS-specific dark patterns while building on established
work and their methodologies.

###  2.4. Design Strategies on SNSs

Social media plays a major role in the daily lives of millions of people
worldwide. Although prior dark pattern work has considered SNS in their
research (Di Geronimo et al., 2020; Gunawan et al., 2021; Habib et al., 2022),
we still lack a thorough understanding of social media-specific dark patterns
and the different kinds of harm they might present compared to malicious
practices observed in other domains. In this context, early work by Roffarello
and De Russis (Monge Roffarello and De Russis, 2022) proposes five dark
patterns in Facebook and YouTube which aim to capture their users’ attention.
Although offering insights into SNS specific strategies, dark patterns that
exploit alternative strategies than attention-capturing have yet to be
described. Aside from work with an explicit focus on dark patterns, a growing
body of work suggests certain uses and users of SNS may be prone to negative
consequences in terms of mental health and well-being (Wang et al., 2011; Wang
et al., 2014; Beyens et al., 2020; Shakya and Christakis, 2017). A well-
documented reason for this may be found in research comparing users’ self-
reported time spent on SNS to actual times, suggesting a lack of self-control
and self-determination. Indeed, numerous studies have demonstrated a lack in
users’ ability to self-report accurately the time they spent on any SNS
(Junco, 2013; Schoenebeck, 2014; Ernala et al., 2020). By showing that users
generally spend less time on Facebook than they think while opening the
application more often than realised, both Junco’s and Ernala et al.’s works
highlight a problem around self-awareness when it comes to frequency and
length of social media use. A similar disparity was described by Mildner and
Savino (Mildner and Savino, 2021), where a contradiction in people’s perceived
and actual usage behaviour was observed among 116 participants. Most Facebook
users who participated in the study admitted that they spent more time on
Facebook than planned, though most also declared they had no desire to spend
less time on the platform. Another interesting result of their survey is noted
in participants’ perceived feeling of low control over ad-related data. These
findings are in line with prior research noting a general dissatisfaction in
users seems to arise from limited options to protect their privacy combined
with an urge to have more control over their personal data (Wang et al., 2013;
Dey et al., 2012). Investigating advertising controls on Facebook, Habib et
al. (Habib et al., 2022) consider the impacts of dark patterns when conducting
an online survey to support a thematic analysis identifying users’ desired
advertisement controls. The authors described users’ difficulty in finding
Facebook’s Ad Preference section, to begin with. These findings affirm a prior
suggestion by Gunawan et al. (Gunawan et al., 2021), who argue that the
granularity of interfaces may discourage users from making desired changes to
their preferences. Both users’ difficulty in self-reporting the amount of time
spent on SNSs and their lack of agency to control personal settings outline
unethical practices that may fall under the umbrella of dark patterns.
Consequently, we see an urgent need to better understand dark patterns in SNSs
considering domain-specific strategies. This necessity is further highlighted
by Schaffner et al. (Schaffner et al., 2022), who demonstrate how difficult
account deletion is across 20 popular SNSs. Not only does the possibility to
entirely delete an account vary depending on the modality of a particular
service, but the authors further notice a difficulty among users to follow
through with a deletion process. In our research, we further investigate this
problem by recording and analysing reviewers’ usage of four SNSs, a deletion
process, allowing for detailed insights.

##  3\. Expert Review & Data Collection

To collect necessary data of SNS usage for the thematic analysis, we asked six
HCI experts to record their usage of four mobile SNS applications. By
expanding the scope, we gain a general understanding of common practices
across SNSs. The decision fell on Facebook, Instagram, TikTok, and Twitter, as
they present some of the most popular SNSs while satisfying comparable user
needs. To assist their review in targeting potential unethical practices, each
expert reviewer was provided ten tasks that afforded them to use the mobile
applications of each SNS intensively. To get a better understanding of their
actions, we also asked them to narrate their decisions to retrieve data in the
form of a think-aloud protocol (Jaspers et al., 2004). As we estimated that 30
minutes were required to complete all tasks, we asked each reviewer to record
their usage of two of the four SNS. Thus, three independent recordings per SNS
were collected. As the experiment was conducted during the COVID-19 pandemic,
reviewers completed the study without supervision.

###  3.1. Reviewers

For reviewers, we reached out to HCI researchers, choosing six to investigate
the four SNSs (3 female, 3 male, mean age = 28.33 years, $SD=1.63$). All have
multiple years of experience in HCI research, with backgrounds in cognitive
science, computer science, and media science. At the time of the study, their
years of experience varied from two to six years, with a mean of 3.83 years
($SD=1.47$). At the time of conducting this research, all reviewers were
employed as researchers at academic research faculties based in Germany,
focusing on HCI related topics. Five reviewers are German citizens, while one
is of Russian nationality. Except for one reviewer, participants did not have
prior experience conducting dark pattern related research although all shared
a general conceptualisation of the field. Participation in this study was
voluntary and without compensation. The decision fell on HCI researchers to
conduct this study as regular users have been repeatedly shown to have
difficulties in detecting dark patterns (Di Geronimo et al., 2020; Bongard-
Blanchy et al., 2021). In contrast, the researcher’s strong expertise of
usability best practices and design heuristics makes them more sensitive
towards interface strategies, enabling them to uncover and discuss persuasive
techniques better. Hence, their expertise allows them to identify a wider
variety of dark patterns than regular users would be able to. Nonetheless, we
acknowledge that this qualitative research was conducted entirely by people
with strong HCI backgrounds, possibly influencing our findings and
interpretations.

###  3.2. Preparation

To counter any systematic issues caused by a particular operating system, each
reviewer was provided with two smartphone devices: an iPhone X with iOS and an
Android running Google Pixel 2 device. Reviewers were asked to use both
devices simultaneously and to pay attention to device-specific differences
when solving the tasks. Before giving them the devices, each phone was factory
reset and only contained fresh installations of the SNS111Installed versions
consistent throughout the study: Facebook (iOS: 321.0.0.53.119; Android:
321.0.0.37.119); Instagram: (iOS: 191.0.0.25.122; Android: 191.1.0.41.124);
TikTok (iOS:19.3.0; Android: 19.3.4); Twitter (iOS: 8.69.2; Android:
8.95.0-release.00). and some media content containing photos with creative-
commons licenses. Each participant was also provided with a new phone number
and email address, allowing them to create a new social media account while
protecting their privacy. For the same reason, the media content was provided,
as some of the tasks required the participants to create and post content.

###  3.3. Tasks

Each evaluator was asked to execute ten tasks during their recording sessions.
We decided to implement five tasks similar to Di Geronimo et al.’s (Di
Geronimo et al., 2020) tasks as they were shown to be effective for this kind
of study. To ensure coverage of SNS-specific interface problems, we also added
five additional tasks tailored to this domain. Before employing the tasks to
the reviewers, they were tested to ensure their accuracy and ability to cover
a wide range of SNS-specific functionalities. After minor revisions following
piloting, the ten tasks comprised the following exercises (whereas tasks that
were taken from or worded closely to Di Geronimo et al.’s experiment are
highlighted by an asterisk):

  1. 1.

Turn on screen recording on each device.

  2. *2.

Open the app and create an account to log in and then out.

  3. *3.

Close and reopen the app.

  4. 4.

Create any kind of content, post it, and delete it.

  5. 5.

Follow and unfollow other accounts.

  6. *6.

Visit the personal settings.

  7. *7.

Visit the ad-related settings.

  8. *8.

Use the application for its intended use (minimum of five minutes):

    1. I

Describe the natural flow of the app – what did you use it for?

    2. II

Could you use the app as you wanted, or did some features ’guide’ your
interactions?

    3. III

how easy was it to get distracted, and if so, what distracted you?

  9. 9.

Delete your account.

  10. 10.

Turn off screen recording and save the recording.

###  3.4. Procedure

Figure 1. This flowchart describes the four steps in which our data was
collected. First, reviewers received an in-depth introduction into the dark
pattern literature. Each reviewer was then randomly assigned two of the four
SNSs Facebook, Instagram, TikTok, and Twitter. Per SNS, the reviewers
conducted a walkthrough based on 10 tasks on two devices (iOS and Android).

[Flowchart of the data collection] The figure visualises the process in which
the six reviewers reviewed each SNS in four stages. Each stage is shown in a
box with an arrow linking it to the next. The first stage reads: ”Introduction
into dark pattern literature including 81 types of dark patterns.” The second
stage reads: ”Random assignment of 2 SNSs per reviewer (N=6). The order was
counterbalanced.” The third stage reads: ”First SNS walkthrough including 10
tasks and think-aloud protocol simultaneously on iPhone X and Google Pixel 2.”
The fourth and final stage reads: ”Second SNS walkthrough including 10 tasks
and think-aloud protocol. Simultaneously on iPhone X and Google Pixel 2.”

Before completing their walkthrough of the SNSs, reviewers were prepared for
the study via a one-hour introduction session, outlining recent research on
dark patterns and established taxonomies. This took the form of an online
meeting. The introduction involved instructions around the 81 dark pattern
types provided in the related work section, which was followed by an open
discussion aimed at answering any unresolved questions reviewers may have.
Afterwards, each reviewer was provided with a copy of the information,
including a detailed list of dark patterns described in the introduction
session. This material is included in the supplementary material of this work.
Even though all participants have backgrounds in the field, this preparation
ensured a common understanding of current conceptualizations of dark patterns.
Information provided to reviewers also included a sheet explaining the
cognitive biases particular dark patterns were designed to exploit (Mathur et
al., 2019). This was included to help them identify dark patterns that were
not already described in taxonomies. Each reviewer was then randomly assigned
two of the four SNSs, ensuring that each SNS was examined independently three
times by three different HCI researchers. The SNS applications that reviewers
used were randomly assigned while ensuring that collectively reviewers did
every possible sequential permutation.

Reviewers then conducted their reviews without supervision, following a
detailed step-by-step guide they were provided with. The steps can be seen in
Figure 1, and explain each task along with when to start and end the screen
and audio recording. The manual further reminded reviewers to openly discuss
their actions allowing us to retrieve data in the form of a think-aloud
protocol, which we later evaluated to understand their decisions. The quality
of the reviews are twofold: (1) The tasks were designed to get detailed
insights into SNSs, affording reviewers to engage with the available features
of each platform. (2) By recording the reviewers’ commentary, we also gained
information about their perception and judgement of potentially malicious
artifacts. All recordings were automatically stored on each device and
retrieved when reviewers returned both smartphones.

##  4\. Findings

As a result of this data collection, the recordings provided over 16 hours of
video and audio material, including the think-aloud commentary from reviewers.
On average, each session lasted 41 minutes ($SD=19$) for each device. Between
the six participants, each SNS was thus used for an average of 247 minutes.
Looking at different approaches to identifying dark patterns, we noticed
varying methodologies in prior work. Of the eight taxonomies considered here,
two relied on crowd-sourced or user-generated imagery material to apply
qualitative methodologies, such as the constant comparative method (Gray et
al., 2018; Gray and Chivukula, 2019), whereas another used a web crawler to
generate a large enough corpus to apply hierarchical clustering (Mathur et
al., 2019). Although highlighting important interface issues that need to be
addressed, the remaining five works provide limited information to recreate
their results. We decided to follow prior works and conducted a thematic
analysis based on the 81 dark pattern types contained in this literature while
further relying on screen recordings and audio commentary of participants.
Furthermore, by limiting the scope of this research to four applications only,
we were able to study each service more extensively.

###  4.1. Analysis of the Data

To answer our research questions, two researchers conducted a thematic
analysis supported by the resulting data from the screen and audio recordings
(Braun et al., 2019). While the screen recordings contained the visual
captures of dark patterns and interactions thereof, the audio served as
complementary material, which allowed the coders to get a detailed impression
of reviewers’ perception and judgement of a scene. This aided the coding
process in a supportive role. However, all labelling was conducted by the two
coders. The thematic analysis was carried out through a combination of
deductive and inductive coding (Mayring, 2020) using the software ATLAS.ti
(GmbH, 2021). In line with works from Di Geronimo et al. (Di Geronimo et al.,
2020) and Gunawan et al. (Gunawan et al., 2021), we applied codes whenever an
interface was perceived problematic, rather than being driven by a concern
with designer intent. This allows for the identification of dark patterns that
emerge from both intentionally manipulative and unintended yet potentially
harmful design choices. The thematic analysis was conducted by two researchers
who designed and administered the experiment and aimed to identify indications
of dark patterns present in the four SNSs. Although the recorded sessions were
coded independently, both coders met for discussions after each session to
align their interpretations of dark pattern definitions, rendering testing for
inter-rater reliability unnecessary (Blandford et al., 2016). Additionally,
all instances where coders diverged from reviewers’ comments were specifically
marked and later discussed among both coders. Once all sessions were analysed,
they met for a final thorough discussion to establish full agreement over all
12 sessions.

####  4.1.1. Creating The Deductive and Inductive Codebook

The deductive codebook was derived from the descriptions of existing dark
patterns as seen in Table 1. Each of the 81 codes included in the codebook
denotes a specific type of dark pattern. That is 81 specific interface design
elements that - by accident or by design - impinge on the users’ autonomy. As
the 12 dark patterns by Gunawan et al. were not described by the time we
conducted the thematic analysis, we were not able to include them in this
study. In an attempt to reduce the number of codes, we collapsed dark pattern
types that shared the same name. This resulted in combining Brignull and
Mathur et al.’s confirmshaming dark patterns, which share a name and very
similar descriptions, which reduced the codebook to 80 codes from an initial
81. Other candidates were also considered (privacy zuckering (Brignull, 2010;
Bösch et al., 2016) and bait and switch (Brignull, 2010; Greenberg et al.,
2014)), but were not collapsed as their descriptions were deemed too distinct.
We thus concluded the deductive codebook with 80 codes.

Once the deductive codebook had been generated, a random session was chosen
and then independently coded by both coders. Deductive codes were applied
wherever a type of dark pattern from the taxonomy sufficed to describe a
recognised problem within the interface. In those cases where an aspect of the
interface was deemed a potential dark pattern but was not present within the
deductive codebook, a new code was generated and added to a separate inductive
codebook. Descriptions for inductive codes focused on describing the design or
interaction extracted from the recordings. Once the first session was
completed, the two annotating researchers met to discuss the adequacy of
established inductive codes and to form a common agreement. Afterwards,
overlapping codes were eventually merged to compile a single inductive
codebook comprising 22 codings by using the affinity diagramming technique
(Blandford et al., 2016).

####  4.1.2. Coding Remaining Data

Both coders then proceeded to code the remaining sessions independently,
following the same procedures, only interrupted to discuss and resolve
inconsistencies after each session, based on Blandford et al. (Blandford et
al., 2016). By relying on screen recordings of the interfaces, instead of
sampled screenshots, we gained an important advantage that allowed us to
thoroughly investigate not only dark patterns on particular frames but observe
sequenced interactions that would be invisible in still images. This was
further affirmed by the recordings of the think-aloud protocol. If necessary,
we could stop, repeat, and compare interface situations to get a deep
understanding of the interactions the reviewers performed. Because of this
decision, we also noticed dark patterns that only surfaced after sequential
interactions were taken. This allowed us to observe dark patterns that were
not previously described and were potentially unique to SNSs.

###  4.2. Deductive Codebook Analysis: Findings

Author |  Dark Pattern | F | I | Ti | Tw |  | Author |  Dark Pattern | F | I | Ti | Tw
---|---|---|---|---|---|---|---|---|---|---|---|---
Brignull (Brignull, 2010) |  Bait And Switch | $\bullet$ | $\circ$ | $\circ$ | $\bullet$ |  |  Bösch et al. (Bösch et al., 2016) |  Address Book Leeching | $\bullet$ | $\bullet$ | $\bullet$ | $\bullet$
Confirmshaming | $\bullet$ | $\bullet$ | $\bullet$ | $\bullet$ |  |  Bad Defaults | $\bullet$ | $\bullet$ | $\bullet$ | $\bullet$
Disguised Ads | $\circ$ | $\bullet$ | $\circ$ | $\bullet$ |  |  Forced Registration | $\circ$ | $\circ$ | $\circ$ | $\circ$
Forced Continuity | $\circ$ | $\circ$ | $\circ$ | $\circ$ |  |  Hidden Legalese Stipulations | $\bullet$ | $\bullet$ | $\bullet$ | $\bullet$
Friend Spam | $\circ$ | $\circ$ | $\circ$ | $\circ$ |  |  Immortal Accounts | $\circ$ | $\circ$ | $\circ$ | $\circ$
Hidden Costs | $\circ$ | $\circ$ | $\circ$ | $\circ$ |  |  Information Milking | $\bullet$ | $\circ$ | $\circ$ | $\circ$
Misdirection | $\bullet$ | $\bullet$ | $\bullet$ | $\circ$ |  |  Privacy Zuckering | $\bullet$ | $\circ$ | $\bullet$ | $\circ$
Price Comparison Prevention | $\circ$ | $\circ$ | $\circ$ | $\circ$ |  |  Shadow User Profiles | $\circ$ | $\circ$ | $\circ$ | $\circ$
Privacy Zuckering | $\bullet$ | $\bullet$ | $\bullet$ | $\bullet$ |  |  Gray et al. (Gray et al., 2018) |  Forced Action | $\bullet$ | $\bullet$ | $\bullet$ | $\bullet$
Roach Motel | $\bullet$ | $\bullet$ | $\bullet$ | $\bullet$ |  |  Gamification | $\bullet$ | $\bullet$ | $\bullet$ | $\bullet$
Sneak Into Basket | $\circ$ | $\circ$ | $\circ$ | $\circ$ |  |  Social Pyramid | $\bullet$ | $\bullet$ | $\bullet$ | $\bullet$
Trick Question | $\circ$ | $\circ$ | $\circ$ | $\circ$ |  |  Interface Interference | $\bullet$ | $\bullet$ | $\bullet$ | $\bullet$
Conti & Sobiesk (Conti and Sobiesk, 2010) |  Coercion | $\circ$ | $\circ$ | $\circ$ | $\circ$ |  |  Aesthetic Manipulation | $\bullet$ | $\bullet$ | $\bullet$ | $\bullet$
Confusion | $\bullet$ | $\circ$ | $\circ$ | $\bullet$ |  |  False Hierarchy | $\bullet$ | $\bullet$ | $\bullet$ | $\bullet$
Distraction | $\bullet$ | $\bullet$ | $\bullet$ | $\bullet$ |  |  Hidden Information | $\bullet$ | $\bullet$ | $\bullet$ | $\bullet$
Exploiting Errors | $\circ$ | $\circ$ | $\circ$ | $\circ$ |  |  Preselection | $\bullet$ | $\bullet$ | $\bullet$ | $\bullet$
Forced Work | $\bullet$ | $\bullet$ | $\bullet$ | $\bullet$ |  |  Toying With Emotions | $\bullet$ | $\bullet$ | $\bullet$ | $\bullet$
Interruption | $\bullet$ | $\bullet$ | $\bullet$ | $\bullet$ |  |  Nagging | $\bullet$ | $\bullet$ | $\bullet$ | $\bullet$
Manipulating Navigation | $\bullet$ | $\bullet$ | $\bullet$ | $\bullet$ |  |  Obstruction | $\bullet$ | $\bullet$ | $\bullet$ | $\bullet$
Obfuscation | $\bullet$ | $\bullet$ | $\bullet$ | $\bullet$ |  |  Intermediate Currency | $\bullet$ | $\bullet$ | $\bullet$ | $\bullet$
Restricting Functionalities | $\bullet$ | $\bullet$ | $\circ$ | $\circ$ |  |  Sneaking | $\bullet$ | $\bullet$ | $\circ$ | $\circ$
Shock | $\circ$ | $\bullet$ | $\circ$ | $\circ$ |  |  Gray et al. (Gray et al., 2020) |  Automating The User | $\bullet$ | $\bullet$ | $\circ$ | $\bullet$
Trick | $\circ$ | $\circ$ | $\circ$ | $\circ$ |  |  Controlling | $\bullet$ | $\bullet$ | $\bullet$ | $\bullet$
Zagal et al. (Zagal et al., 2013) |  Grinding | $\circ$ | $\circ$ | $\circ$ | $\circ$ |  |  Entrapping | $\circ$ | $\circ$ | $\circ$ | $\circ$
Impersonation | $\circ$ | $\circ$ | $\circ$ | $\circ$ |  |  Misrepresenting | $\bullet$ | $\bullet$ | $\bullet$ | $\bullet$
Monetized Rivalries | $\circ$ | $\circ$ | $\circ$ | $\circ$ |  |  Nickling-And-Diming | $\circ$ | $\circ$ | $\circ$ | $\circ$
Pay To Skip | $\circ$ | $\circ$ | $\circ$ | $\circ$ |  |  Two Faced | $\circ$ | $\circ$ | $\circ$ | $\circ$
Playing By Appointment | $\circ$ | $\circ$ | $\circ$ | $\circ$ |  |  Mathur et al. (Mathur et al., 2019) | Forced Acrtion (see Gray et al. (Gray et al., 2018))
Pre-Defined Content | $\circ$ | $\circ$ | $\circ$ | $\circ$ |  |  Forced Enrollment | $\circ$ | $\circ$ | $\circ$ | $\circ$
Social Pyramid Schemes | $\circ$ | $\circ$ | $\circ$ | $\circ$ |  |  Misdirection | $\bullet$ | $\bullet$ | $\bullet$ | $\bullet$
Greenberg et al. (Greenberg et al., 2014) |  Attention Grabber | $\bullet$ | $\bullet$ | $\bullet$ | $\circ$ |  |  Pressured Selling | $\bullet$ | $\bullet$ | $\bullet$ | $\bullet$
Bait And Switch | $\circ$ | $\circ$ | $\circ$ | $\circ$ |  |  Visual Interference | $\bullet$ | $\bullet$ | $\bullet$ | $\bullet$
Captive Audience | $\circ$ | $\circ$ | $\circ$ | $\circ$ |  | Obstruction (see Gray et al. (Gray et al., 2018))
Disguised Data Collection | $\circ$ | $\circ$ | $\circ$ | $\circ$ |  |  Hard To Cancel | $\bullet$ | $\bullet$ | $\bullet$ | $\bullet$
Making Personal Info. Public | $\circ$ | $\circ$ | $\circ$ | $\circ$ |  |  Scarcity | $\bullet$ | $\bullet$ | $\bullet$ | $\bullet$
The Milk Factor | $\circ$ | $\circ$ | $\circ$ | $\bullet$ |  |  High-Demand Messages | $\circ$ | $\circ$ | $\circ$ | $\circ$
Unintended Relationships | $\circ$ | $\circ$ | $\circ$ | $\circ$ |  |  Low-Stock Messages | $\circ$ | $\circ$ | $\circ$ | $\circ$
We Never Forget | $\circ$ | $\circ$ | $\circ$ | $\circ$ |  | Sneaking (see Gray et al. (Gray et al., 2018))
Legend: |  F \- Facebook |  |  |  |  |  |  Hidden Subscriptions | $\circ$ | $\circ$ | $\circ$ | $\circ$
|  I \- Instagram |  |  |  |  |  |  Social Proof | $\bullet$ | $\bullet$ | $\bullet$ | $\bullet$
|  Ti \- TikTok |  |  |  |  |  |  Activity Notifications | $\circ$ | $\circ$ | $\circ$ | $\circ$
|  Tw \- Twitter |  |  |  |  |  |  Testimonials | $\circ$ | $\circ$ | $\circ$ | $\circ$
|  |  |  |  |  |  |  Urgency | $\bullet$ | $\bullet$ | $\bullet$ | $\bullet$
|  |  |  |  |  |  |  Countdown Timer | $\circ$ | $\circ$ | $\circ$ | $\circ$
|  |  |  |  |  |  |  Limited-Time Messages | $\circ$ | $\circ$ | $\circ$ | $\circ$

Table 1. This table offers an overview of all 80 deductive codes used in the
thematic analysis, sorted by authors. We only considered original sources for
dark patterns to rely on unique codes and avoiding redundancy. As both Gray et
al. (Gray et al., 2018) and Mathur et al. (Mathur et al., 2019) include both
high-level and low-level definitions, we used indentation to highlight
categorical differences for these types of dark patterns. For transparency, we
added three of Mathur et al.’s (Mathur et al., 2019) high-level dark pattern
categories, which were carried over from Gray et al. (Gray et al., 2018) and
labeled accordingly. The codes were applied in four SNSs (F \- Facebook, I \-
Instagram, Ti \- TikTok, and Tw \- Twitter). Codes that were identified by
either of the two coders conducting the thematic analysis are indicated with
“$\bullet$” whereas “$\circ$” indicates that a code for a dark pattern was not
found in the SNSs.

Answering our first research question, we applied 44 out of the 80 deductive
codes across our dataset, highlighting how many distinct dark patterns
occurred across the four different SNSs. Table 1 displays all dark patterns
and shows for which SNS they were applied during the analysis. Of these 44
that were applied, we observed 32 instances on each of the four SNSs.
Regarding the original scope of individual dark pattern taxonomies, we noticed
that the dark patterns described by Conti and Sobiesk (Conti and Sobiesk,
2010) and Gray et al. (Gray et al., 2018) were noticed most commonly in the
context of SNS, suggesting easier applicability of these taxonomies.
Interestingly, these sets of dark patterns were both originally created by
analysing a wide range of interfaces rather than focusing on a specific
domain. Further, those described by Gray et al. (Gray et al., 2018) even
subsumed dark pattern types formerly described by Brignull (Brignull, 2010),
generalising them even further. We also investigated which SNSs feature the
largest variety of dark pattern types. Facebook, which contained 41 different
types of dark patterns, exhibited the most variety, followed by Instagram,
featuring 39, Twitter, where 35 different types of dark patterns were
observed, and finally Tiktok, with 37 different types identified.

While this result highlights that SNS seem to make use of a wide variety of
dark pattern types, 36 dark pattern codes were still left unused, implying
that these were not appropriate to describe dark patterns in SNSs. Three
groups of dark patterns from the initial 80 were not or only rarely applied:
the proxemic dark patterns by Greenberg et al. (Greenberg et al., 2014), the
gaming dark patterns by Zagal et al. (Zagal et al., 2013), and the e-commerce
dark patterns by Mathur et al. (Mathur et al., 2019). Many of these dark
patterns were generated by describing design decisions in a specific domain or
context, making it almost impossible to apply them elsewhere. One example
would be Greenberg’s captive audience dark pattern, which requires a ”person
[to] [enter] a particular area” (Greenberg et al., 2014) relying heavily on
the actual proximity, hence why they are less applicable to a domain as
ubiquitous as SNSs.

Beyond overly-specific dark patterns, others share a name, albeit with varying
definitions. As a result, two dark patterns with the same name do not
necessarily apply identically to SNS. For example, privacy zuckering was
described by Brignull (Brignull, 2010) and Bösch et al. (Bösch et al., 2016).
While Bösch et al. focuses specifically on sharing more data than a user
intends to give based on privacy settings, Brignull does not make this
specification and thus leaves more room for interpretation and application.
Hence, Brignull’s version of the dark pattern was applied across all four SNS
during our analysis, while the version by Bösch et al. was only applied
explicitly on Facebook and TikTok. In other cases, some names of dark pattern
promise applicability in SNSs but were actually never applied as their
definition hindered accurate coding. An example is illustrated by the dark
pattern immortal accounts by Bösch et al. (Bösch et al., 2016). While the name
of the dark pattern suggests that user accounts are (almost) impossible to
delete, its definition refers to service providers requiring new users to sign
up for accounts to use their service.

###  4.3. Inductive Codebook Analysis: Findings

Although 44 codes from the deductive codebook could be applied in the SNSs,
often, new codes were required to describe interface issues not yet covered.
We decided early on to stick rigidly to established definitions of the types
of dark patterns when applying deductive codes to avoid ambiguity among
coders. During the coding of a first sample, we noticed issues were close in
nature to certain deductive codes, but narrow wording hindered precise usage
in different contexts. For instance, this was the case with the confirmshaming
code. The underlying dark pattern, first coined by Brignull (Brignull, 2010),
describes texts that guilt users into certain actions based on shameful
language. Applying an inverse strategy, we noticed various cases in which
language was used in the form of positive encouragement to steer users towards
a certain direction. We, therefore, created an additional code, persuasive
langugage, that we defined bidirectionally describing any instance where
language is used to push decisions. In total, we defined 22 unique codes
describing problematic interactions or otherwise questionable interfaces that
could not be coded using deductive codes (the entire inductive codebook,
including code descriptions, are provided in Appendix A). As seen in Table 2,
the 22 inductive codes did not occur across each of the four SNSs equally.
Yet, 16 codes were applied to recording sessions on Facebook, 15 to TikTok and
Twitter, while 14 were applied on Instagram sessions, showing a similar
propagation of dark patterns across SNSs. Once all twelve sessions were
analysed, we applied axial coding to the 22 codes. This resulted in the
identification of five themes that describe the various types of dark patterns
that are specific to SNSs. Answering our second research question, these
themes encompass: (1) interactive hooks; (2) social brokering; (3) decision
uncertainty; (4) labyrinthine navigation; and (5) redirective conditions.
Moreover, we were able to assign these five themes to two broader strategies,
to describe practitioners’ and SNSs’ intentions to navigate users’ decision-
making: (1) Engaging strategies and (2) Governing strategies. Engaging
strategies envelope the themes interactive hoooks and social-broking, whilst
governing strategies incorporate decision uncertainty, labyrinthine
navigation, and redirective conditions. Table 2 provides a complete summary of
these findings, including the 22 inductive codes, resulting in five themes and
overarching strategies.

Strategy | Theme | No. | Inductive Code | F | I | Ti | Tw
---|---|---|---|---|---|---|---
|  | 1. | Addictive Design | $\circ$ | $\bullet$ | $\bullet$ | $\bullet$
|  | 2. | Autoplay Content | $\bullet$ | $\bullet$ | $\bullet$ | $\bullet$
|  | 3. | Fear Of Missing Out | $\circ$ | $\circ$ | $\circ$ | $\bullet$
|  | 4. | Gamification | $\bullet$ | $\bullet$ | $\bullet$ | $\bullet$
|  | 5. | Infinite Scrolling | $\bullet$ | $\bullet$ | $\bullet$ | $\bullet$
|  | 6. | Pull To Refresh | $\bullet$ | $\bullet$ | $\bullet$ | $\bullet$
| Interactive Hook | 7. | Reduced Friction | $\bullet$ | $\bullet$ | $\bullet$ | $\bullet$
|  | 8. | False Content Customisation | $\bullet$ | $\circ$ | $\circ$ | $\circ$
|  | 9. | Regression Toward The Mean | $\bullet$ | $\circ$ | $\bullet$ | $\bullet$
Engaging Strategies | Social Brokering | 10. | Social Connector | $\bullet$ | $\bullet$ | $\bullet$ | $\circ$
|  | 11. | Decision Uncertainty | $\bullet$ | $\circ$ | $\circ$ | $\bullet$
|  | 12. | Clinging To Accounts | $\bullet$ | $\bullet$ | $\bullet$ | $\bullet$
| Decision Uncertainty | 13. | Persuasive Language | $\bullet$ | $\bullet$ | $\bullet$ | $\bullet$
|  | 14. | External Solution Search | $\circ$ | $\bullet$ | $\circ$ | $\circ$
|  | 15. | Labyrinth | $\bullet$ | $\bullet$ | $\circ$ | $\bullet$
| Labyrinthine Navigation | 16. | Hidden In Plain Sight | $\bullet$ | $\bullet$ | $\bullet$ | $\bullet$
|  | 17. | Auto Accept Third Party Terms | $\circ$ | $\circ$ | $\circ$ | $\bullet$
|  | 18. | Decision Governing | $\bullet$ | $\bullet$ | $\circ$ | $\circ$
|  | 19. | Forced Access Granting | $\circ$ | $\circ$ | $\bullet$ | $\circ$
|  | 20. | Forced Dialogue Interaction | $\bullet$ | $\circ$ | $\bullet$ | $\circ$
Governing Strategies | Redirective Condition | 21. | Forced Grace Period | $\bullet$ | $\bullet$ | $\bullet$ | $\bullet$
|  | 22. | Plain Evil | $\circ$ | $\circ$ | $\bullet$ | $\circ$
|  |  | Total | 16 | 14 | 15 | 15
Table 2. This table lists inductive codes and their presence within the four
SNSs Facebook (F), Instagram (I), TikTok (Ti), and Twitter (Tw). Inductive
codes observed by either of the two coders in a particular SNS session are
visualised with “$\bullet$” while “$\circ$” implies that a code was not found.
This table further shows the themes each inductive code was assigned to based
on axial coding of the data and the high-level strategies they subscribe to -
engaging strategies and governing strategies. To align with prior research of
this field, the developed themes are later referred to as types of dark
patterns.

###  4.4. Engaging Strategies

In the context of SNSs, engaging strategies cover dark patterns where the goal
is to keep users occupied and entertained for as long as possible. Prior dark
patterns described by Roffarello and Russis (Monge Roffarello and De Russis,
2022) fall under this strategy alongside Lukoff et al.’s (Lukoff et al.,
2018a) discussion of potentially existing attention-capture dark patterns,
which the OECD recently integrated (OECD, 2022). As Table 2 indicates, the
overall count of engaging strategies and subordinated codes is higher compared
to those assigned to governing strategies, which is in line with research on
people’s motivation to use SNSs (Wong and Burkell, 2017). Emerging from the
five themes, we identified two SNS-specific dark patterns - interactive hooks
and social brokering \- that subscribe to the engaging strategies (see Table
2).

####  4.4.1. Interactive Hooks

We define interactive hooks as design mechanisms that use rewarding schemes to
keep users entertained and spend more time on a service. Throughout our
recordings, we found multiple cases where such mechanisms were implemented.
For example, some form of gamification elements (Simões et al., 2013) were
coded within each SNS, galvanising users to share more information about
themselves or connect to new people (see Figure 2d). In another example, we
found that many artefacts utilise addictive mechanisms (Montag et al., 2019)
that often provide seemingly infinite content, further coded with pull-to-
refresh, infinite scrolling, and auto playing media. With this finding, we
confirm previous results of Lukoff et al. (Lukoff et al., 2018b) who looked at
auto-playing mechanisms on YouTube. We extend their work by also considering
four alternative SNSs. By relying on a sequence of interactions, these
addictive mechanisms can only be described with difficulty from still images,
demonstrating the complexity in which dark patterns manifest in SNSs.

(a) Screenshot from Facebook

(b) Screenshot from Instagram

(c) Screenshot from TikTok

(d) Screenshot from Twitter

Figure 2. Four examples of interactive hooks. Each sub-figure contains
gamification elements that galvanise users to widen their social networks or
publish more information about themselves.

[Screenshots of the four mobile apps with interactive hook dark patterns] This
figure contains four sub-figures of four examples of the interactive hooks
dark pattern. Figure 2a) shows a screenshot from Facebook asking a user to add
15 friends to see more posts. A big blue button below states find friends
nudging users to connect to more people. Figure 2b) shows an interface from
Instagram asking users to complete their profiles. A gamification-like element
indicates progress currently at 1 of 4 complete. Below are options with icons
to add a photo or add biographic data. Figure 2c) shows a picture from TikTok.
Similar to the Instagram interface, it asks users to complete their profile
with a similar progress bar showing 0 of 3. Below are options to add a profile
photo or include a name as an addition to the user name. Buttons for both
options are bright red. Figure 2d) stems from Twitter. The interface
announces: let’s get you set up. Below the statement are boxes with game-like
tasks. The first requires the user to follow three topics. The second asks
them to complete their profile. A green ’Done’ text over the second suggests
that this task has been accomplished.

####  4.4.2. Social Brokering

We define social brokering as design mechanisms that nudge users to create
multiple connections with people (e.g. based on similar characteristics) while
suggesting new people to connect to, leading users to share more than they may
want to a wider public. The name is inspired by agents whose aim is to
facilitate connections between potential (transaction-) partners - here in the
context of social networks. Although gamification strategies of the
interactive hooks pattern already encourage users to increase their social
connections, we found a range of artefacts specifically designed for this
purpose codes as social connector. Moreover, we noticed that each SNS
customised content presented based on the reviewers’ usage behaviour. While in
some instances the news feed content appeared a poor fit for the reviewers’
preferences (coded as false content customisation), we also found very popular
content reappearing across the recordings, captured in the code regression
toward the mean. Visualised in Figure 3, Brignull’s bad defaults pattern
(Brignull, 2010) is exploited to promote one’s account to other users also
outside the platform itself. Figure 4 shows how Instagram nudges its users to
upload their device’s local contacts to connect to more accounts quickly.
However, by giving away details from private contacts, SNS providers can also
store information from people who do not use their services without getting
their consent first. In a similar case, Facebook uses persuasive language by
telling users to add friends in order to see more content (see Figure 5).

Figure 3. Example of social brokering from TikTok where settings are pre-set
to suggest an account within and outside the SNS. The interface includes bad
defaults and privacy zuckering dark patterns

[First example of a social brokering dark pattern from TikTok]The image shows
an interface from TikTok. At the top, it reads: ”Suggest your account to
others”. Four options are seen: First contacts: meaning that one’s account is
suggested to others if a phone number is provided. Second are ”Facebook
friends”, allowing TikTok to connect to a Facebook account. The third option
reads users with mutual connections, and the fourth is users who open or send
shared links. Further deploying the bad defaults and privacy zuckering dark
patterns, all options are active.

Figure 4. Example of social brokering from TikTok where settings are pre-set
to suggest an account within and outside the SNS.

[Second example of a social brokering dark pattern from TikTok]This interface
from TikTok asks users to find friends to follow and message them. Two options
are provided: a connect to contacts option and a search for friends option.
Both have a big blue button to follow the suggestion, whereas light grey
X-buttons would allow dismissal.

Figure 5. Example of social brokering from Facebook. Once users reach the
bottom of their timeline content, this message appears, nudging them to add
friends to see new posts.

[Third example for social brokering dark pattern from Facebook]This Facebook
screenshot asks users to add friends to see more posts. In a light grey text,
it uses persuasive language to nudge them by saying that more friends mean
more photos and videos to be seen. A big blue button to add friends is
provided.

###  4.5. Governing Strategies

Governing strategies describe interface designs that navigate users’ decision-
making towards the designers’ and/or platform providers’ goals. Essentially,
these are designed to control or govern user behaviour. While existing dark
patterns, such as interface interference (Gray and Chivukula, 2019), fit this
strategy’s scope, we shine a light on not yet discussed dark pattern types:
(1) decision uncertainty; (2) labyrinthine navigation; (3) and redirective
conditions. All these strategies share a limitation of users’ agency as SNS
providers override users’ goals with their own incentives.

####  4.5.1. Decision Uncertainty

We define decision uncertainty as strategies that are confusing to users by
diminishing their ability to assess situations, leaving the user clueless as
to what is expected of them or what options are available. Most similar to
this dark pattern is the confusing dark pattern by Conti and Sobiesk (Conti
and Sobiesk, 2010). However, in SNS, the strategy is not necessarily limited
to incomprehensible questions or information but includes other elements, like
distraction (Conti and Sobiesk, 2010), that overwhelm users. Interface
elements of the decision uncertainty code were so striking that we decided to
promote it to a theme. Other elements obscured decision-making further. Some
interfaces obfuscated the account deletion process (coded with clinging to
accounts) while others used persuasive language to confuse users, similar to
the confirmshaming dark pattern (Brignull, 2010). We found a quite unique
design choice TikTok users faced when first logging on to the platform: When
first opening the app after logging in, users are prompted with an interface
asking them to choose preferred ad-related settings. While making their
decision, both video and audio media is running in the background,
contributing to cognitive overload. During the recordings, we noticed that
reviewers of the data collection quickly complied with the platform’s
preference that utilises interface interference (Gray and Chivukula, 2019) and
visual interference (Mathur et al., 2019) dark patterns (see Figure 6). In a
second example, Twitter users trying to delete their accounts will only find
an option to deactivate it. Although it is possible to fully delete their
account by following this path, the wording obfuscates this possibility.

Figure 6. Example of decision uncertainty from TikTok. After first logging in,
users of the SNS are asked to choose preferences for their ad-related
settings. Meanwhile, video and audio media are playing in the background,
complicating the interaction. The interface further contains interface
interference, visual interference and hidden-legalese stipulation dark
patterns.

[Example of a decision uncertainty dark pattern from TikTok]This TikTok
interface shows an interface prompt blocking further interaction. It asks
users to allow personalised ads. Three paragraphs of light grey text yield
links to legal information whereas a big red button reads: ”Accept and
continue” while a ”review settings” button is obfuscated by a light grey font
colour. Not visible in this screenshot is the situation where this prompt
appeared during the first initialisation of the application while loud audio
and video played in the background, overwhelming users.

####  4.5.2. Labyrinthine Navigation

We define labyrinthine navigation as nested interfaces that are easy to get
lost in, disabling users from choosing preferred settings. This pattern is
often seen in SNS settings menus. Related to manipulating navigation (Conti
and Sobiesk, 2010), but not necessarily steering users towards a designer’s
objective. Instead, this dark pattern describes interface architectures, such
as menus, that users will easily get lost in, leaving them unsuccessful in
achieving their goals. While recording the data, especially tasks six (visit
the personal settings) and seven (visit the ad-related settings), surfaced
difficulties for reviewers to find specific settings, some of them coded
hidden in plain sight camouflaged between a wide collection of other options.
We noticed this issue across all four SNSs. In a particular case, we noticed
one participant using Instagram to switch applications to an online search
engine to look up the solution for how to find a specific setting.

####  4.5.3. Redirective Conditions

We define redirective condition as choice limitations that force users to
overcome unnecessary obstacles before being able to achieve their goals.
Redirective conditions usually favour the objectives of the SNS. The forced
action dark pattern (Gray and Chivukula, 2019) plays an important role here,
as users are required to first comply with certain demands before being able
to do what they want. In the context of SNS, this dark pattern includes
passive functionalities, like the restriction of services that are only lifted
after users give permissions unrelated to the functionality (coded as forced
access granting). In related situations, we noticed forced dialogue
interactions that required users to engage with text elements otherwise
occupying desired functionalities. More specific yet similar to the roach
motel (Brignull, 2010), hard to cancel (Mathur et al., 2019), or account
deletion roadblocks (Gunawan et al., 2021) dark patterns, a grace period of up
to 30 days was announced by all SNSs before certain deletion processes were
accepted. The Forced Grace Period code was used in such instances. When
deleting content, Facebook keeps the targeted item, as seen in Figure 7.
Trying to bypass this rule, participants found it difficult to find the
particular settings that would delete their content immediately.
Interestingly, all four SNS denied users an immediate account deletion, as
demonstrated by TikTok in Figure 8. Each had a 30 days return option that
would automatically reactivate accounts, even if users accidentally logged in
to the SNS.

Figure 7. Example of redirective conditions from Facebook. When trying to
delete a post, Facebook will instead keep the item stored for another 30 days.

[First example of a redirective conditions dark pattern from Facebook]This
interface from Facebook wants a user to reaffirm whether they want to move an
item to the trash. In grey text it reads that items will be stored for 30 days
before they will be deleted, denying the user immediate deletion.

Figure 8. Example of redirective conditions from TikTok. Accounts will be kept
and able to be restored for 30 days without giving users the choice to bypass
this decision.

[Second example of a redirective conditions dark pattern from TikTok.]This
TikTok interface stems from an account deletion process. It shows a list of
bullet points of reasons to stay, persuading the user to cancel this action.
At the bottom, a big red continue button is placed.

##  5\. Discussion & Implications

Our examination of four SNSs — Facebook, Instagram, TikTok, and Twitter —
identified instances of 44 dark patterns from a taxonomy of 80 codified in
prior dark pattern literature. The work also identified instances of 22
inductive codes capturing malicious design artefacts that were not outlined in
earlier research. Findings stem from expert reviews of the aforementioned SNSs
carried out by six trained HCI researchers. Each researcher reviewed two SNSs
and executed ten tasks designed to thoroughly understand the applications. The
22 inductive codes were then analysed, supported by axial coding. This
resulted in the generation of five common themes describing SNS-specific dark
patterns and two high-level strategies: Engaging strategies and governing
strategies. Figure 9 summarises these findings while further allocating Mathur
et al.’s (Mathur et al., 2021) dark pattern characteristics.

###  5.1. Dark Patterns In SNSs

To explore the potential existence of dark patterns in SNSs, we began the
thematic analysis by initialising a deductive dark pattern codebook comprising
80 dark pattern types from prior works (Brignull, 2010; Conti and Sobiesk,
2010; Zagal et al., 2013; Greenberg et al., 2014; Bösch et al., 2016; Gray et
al., 2018, 2020; Mathur et al., 2019). Answering our first research question
regarding the types of dark patterns that are used across the four SNSs, we
identified instances of 44 types. This might suggest that the other 36 types
of dark patterns are, as our experience conducting thematic analysis attests,
highly domain-specific, hindering their potential to be applied elsewhere.

Indeed, despite the relative success of our attempts to apply these dark
patterns to SNSs, different levels of generalisability and specification
caused ambiguity decreasing confidence of coders when applying certain dark
patterns. Some works provide alternating abstractions for their taxonomies.
Conti and Sobiesk (Conti and Sobiesk, 2010), for instance, call their patterns
malicious interface design techniques, as the term dark pattern was not widely
established at the time of their publication. In another work, Gray et al.
(Gray et al., 2018) speak of dark pattern strategies, placing their findings
on a more abstract level as, for example, Zagal et al.’ (Zagal et al., 2013)
dark patterns, who speak of game dark patterns without further labels.

Because we agree with the necessity to understand dark patterns in different
hierarchical contexts, we chose to include all taxonomies in our study to
learn about differences when they are applied in situations outside their
original field of application. Further guided by our decision to remain close
to provided definitions, we learned that dark patterns with abstract
definitions were applied more often throughout the SNSs. On the contrary, 36
dark patterns were left unused during our study. Certain dark patterns shared
a name with varying definitions leading to ambiguity when used. Some dark
patterns were derived from highly specific contexts, such as e-commerce
(Brignull, 2010; Mathur et al., 2019) or games (Zagal et al., 2013), which
hindered their usage elsewhere. Others contained overly precise descriptions
descriptions (Bösch et al., 2016) but could find usage elsewhere if phrased
more generically. This implies that general and unspecific dark patterns gain
the utility to describe unethical practices and, thus, better help to identify
interface problems.

This implication is in line with prior research by Mathur et al. (Mathur et
al., 2019). Aside from the identification of 12 dark patterns, the authors
found an alternative approach to abstract the basic operations of dark
patterns by mapping them onto cognitive biases that they exploit (Mathur et
al., 2019; Mathur et al., 2021). The authors developed six characteristics
distinguishing overall approaches to dark patterns: (1) asymmetric; (2)
covert; (3) deceptive; (4) hides information; (5) restrictive; and (6)
disparate treatment. Through these characteristics, the authors were able to
characterise a comprehensive dark pattern taxonomy in which we notice an
accessible and extendable framework for future works.

Figure 9. Summary of engaging and governing strategies with five SNS dark
patterns in two strategies - engaging and governing. For future work, each
dark pattern was assigned corresponding attributes following Mathur et al.
(Mathur et al., 2019) six dark pattern characteristics.

[Figure summarising the two strategies and five SNS-specific dark
patterns.]This figure shows contains five pictograms visually describing each
of the SNS-specific dark patterns. Two, interactive hooks and social
brokering, are placed under the engaging strategies. Three, decision
uncertainty, labyrinthine navigation, and redirective conditions, are placed
under the governing strategies. The interactive hooks pictogram shows a red
fishing hook surrounding a blue play button and a toggle that is set to a red
auto-play option. The social brokering pictogram shows three people forming a
triangle. Two are blue (one as fill-colour the other outlined to distinguish
them), the third one is red and the upward peak of the triangle. From the red
person, two arrows go towards each person establishing a connection. A speech
bubble is over the third person’s head, containing a ringing bell symbolising
a notification. The decision uncertainty pictogram shows a sequence of choices
in boxes at the top. Two boxes are blue containing an X, and the last box is
red with a check symbol. Below is a blue person with a red thinking bubble
above their head. Inside the bubble, confusion is symbolised by wavy lines and
button options. These options are coloured in contrary colours to the ones on-
top to indicate a mismatch in the person’s mental model. The pictogram for
labyrinthine navigation shows a circular labyrinth with a checkered flag in
its centre. A blue person stands at the entry on the left-hand side. A red
arrow indicates the direction into the labyrinth and is continued with a red
line finding its way to the checkered flag. The redirective conditions
pictogram shows a blue puppet. Each joint is connected with a red string that
is connected to a larger red hand, indicating control over the puppet’s
movement. Each SNS-specific dark pattern is assigned Mathur et al.’s dark
pattern characteristics (2021) by a full moon icon, if the characteristic
applies, a half moon if it applies in certain situations but not always, and
an empty circle if it does not apply. Characteristics for interactive hooks
are: asymmetry = half moon; covert = circle; deceptive = half moon; hides
information = half moon; restrictive = half moon; and disparate treatment =
circle. Characteristics for social brokering are: asymmetry = full moon;
covert = half moon; deceptive = half moon; hides information = half moon;
restrictive = circle; and disparate treatment = circle. Characteristics for
decision uncertainty are: asymmetry = full moon; covert = full moon; deceptive
= half moon; hides information = full moon; restrictive = half moon; and
disparate treatment = circle. Characteristics for labyrinthine navigation are:
asymmetry = full moon; covert = circle; deceptive = circle; hides information
= full moon; restrictive = half moon; and disparate treatment = circle.
Characteristics for redirective condition are: asymmetry = full moon; covert =
half moon; deceptive = half moon; hides information = full moon; restrictive =
full moon; and disparate treatment = circle.

###  5.2. SNS-Specific Dark Patterns & Strategies

Answering the second research question, asking about dark patterns unique to
SNSs, our study revealed five types of dark patterns not contained in previous
work. When defining the dark patterns, one goal was to provide enough
abstraction to enable applicability outside SNSs. For the same reason, we
placed them in Mathur et al.’s (Mathur et al., 2019; Mathur et al., 2021) six
characteristics to allow future works to consider these dark patterns under
their lens as well. As can be seen in Figure 9, not all of Mathur et al.’s
characteristics could be applied with the same effectiveness or explicitness.
Interestingly, we were not able to apply the disparate treatment
characteristic at all because it solely contains Zagal et al.’s (Zagal et al.,
2013) game dark patterns which describe mechanics where games benefit players
who, for example, buy advantages. We found none of the four considered SNSs to
offer distinct benefits to certain users while posing disadvantages to others.

Another reason why Mathur et al.’s characteristics show lower effectiveness in
SNSs may be described in how the two strategies operate, distinguishing them
from prior dark pattern settings. As an incentive to increase time spent on
their platforms, SNS need satisfied users. Since the implementation of harmful
designs could act as an antagonist to this goal, jeopardising potential
advertisement revenue, we describe engaging and governing strategies that
navigate users’ decision-making while possibly keeping their satisfaction with
the SNS high (Mildner and Savino, 2021). Although we cannot draw an easy
causal connection between how dark patterns affect users and users’ well-
being, assessing identified SNS dark patterns further resulted in the
description of two design strategies, engaging strategies and governing
strategies (see Figure 9). While one group aims to increase the time users
spend on SNS, the other governs their decision-making by nudging users into
desired directions or keeping them from other options by, for example,
designing for increased friction.

With the intent to enable future work to better build on our findings and
inspired by the types of dark patterns that showed more pertinence in our
study, our aim was to describe our dark patterns to allow application outside
SNS-specific contexts. As a different environment, many games feature
achievement tracks requiring players to regularly play the game to progress.
Similar to Zagal et al.’s playing by appointment, it is imaginable that these
games feature interactive hooks to keep players engaged or contain distracting
elements obfuscating users’ decisions and, thus, deploying decision
uncertainty.

Although this work mainly contributes to the academic community, this strand
of research has gained traction outside, including regulation and guidelines.
In a first line of defence against violations of GDPR (Commission, 2016)
requirements in SNS contexts, the EDPB (Board, 2022) developed a guideline for
designers and users to recognise and avoid dark patterns. However, the
included dark pattern categories (overloading, skipping, stirring, hindering,
fickle, and left in the dark) lack alignment with taxonomies developed in the
academic community that relies on empirical evidence. In this regard, we
acknowledge a potential for better cooperation between law and HCI research to
work on the protection of users in a joint effort. To offer some aid in these
efforts, our goal was to create definitions that are broadly applicable. The
five dark patterns expand current taxonomies by the scope of SNSs, whereas the
two strategies provide high-level categories to describe alternate burdens
placed on users not yet covered.

##  6\. Limitations & Future Work

As previously stated in Section 3, we faced certain limitations in the data
collection phase of this work. Firstly, we decided to focus on only four SNSs
that we deemed comparable after prior considerations looking at a wider range
of applications for the user count and similar functionalities. This
limitation extends to the fact that we limited the review to only looking at
their mobile applications. Although this limitation allowed us to study each
SNS in a single modality on a deeper level, our findings do not necessarily
represent all SNSs. None of the considered SNSs indicates any usage of the
disparate treatment characteristic, which describes discrepancies in the
treatment of paid and free customers, respectively. This might be more
relevant in a study of LinkedIn, for example, as it offers both free and paid
subscriptions, the latter of which affords certain advantages like hiding
one’s identity when looking at another user’s profile. Future work could
extend our findings by considering other SNSs. Secondly, we decided early on
to only focus on mobile applications of considered SNSs, following users’
preferences. However, users may be faced with alternative dark patterns based
on SNSs’ modalities, as the research by Guanawa et al. (Gunawan et al., 2021)
or Schaffner et al. (Schaffner et al., 2022) suggest. Future studies could
consider our results when expanding on other SNSs and modalities. Thirdly,
this research was conducted during the COVID-19 pandemic. The data collection
was thus conducted without supervision after providing necessary information,
including a comprehensive manual. While neither screen nor voice recordings
indicated confusion about the tasks, room for misunderstanding remained.
Thirdly, we relied on HCI researchers as expert reviewers. This decision is
justified by their competence to understand and recognise state-of-the-art
design practices, unlike regular users would be able to. However, by limiting
reviewers’ expertise to a single domain, we may have missed alternative
expertise that may surface additional findings. Future work could consider
recruiting experts with backgrounds in cognitive science and psychology,
including a thorough understanding of cognitive biases. Their expertise could
be particularly interesting in establishing connections between our current
understanding of dark patterns and of cognitive biases.

During the thematic analysis, we also faced some limitations. Firstly, we
limited the deductive codebook to eight works comprising 81 dark patterns. We
relied on Mathur (Mathur et al., 2021) dark pattern review to get a
comprehensive taxonomy. However, we decided to neglect dark patterns that were
described outside the academic community, including guidelines such as those
published by the NCC (Council), 2019), CNIL (et Libertés, 2019), or the EDPB
(Board, 2022). Moreover, dark patterns by Gunawan (Gunawan et al., 2021) were
not included, as they were not mapped onto Mathur et al. (Mathur et al., 2019;
Mathur et al., 2021) dark pattern characteristics, making comparisons more
difficult. Future work could generate a complete mapping of a comprehensive
dark pattern taxonomy onto Mathur et al.’s characteristics. Connecting dark
patterns to cognitive biases, as Mathur et al. propose, seems to be a natural
next step for the dark pattern discourse. To prepare this work for this
direction, we assigned our findings to appropriate characteristics. In this
process, we noticed that the current scope of characteristics does not cover
all problematic design strategies contained in SNS. However, a thorough
analysis of potential cognitive biases active in SNS would have been outside
the scope of this research. Future work could address this gap and extend this
work by bringing together the studies surrounding cognitive biases and dark
patterns to generate a resourceful and sustainable framework.

##  7\. Conclusion

In recent years, the dark pattern landscape has expanded into various
different domains. In this paper, we contribute to this body of research
through the application and expansion of a comprehensive taxonomy of dark
patterns in the context of SNSs. Supported by thematic analysis, we
investigate Facebook, Instagram, TikTok, and Twitter and confirm that the
platforms all deployed a variety of dark patterns and design strategies aimed
at limiting users’ agency, steering their decision-making. Findings suggest
that dark patterns with higher grades of abstraction are easier to be applied
in multiple contexts compared to those given narrower definitions. Lastly, our
results yield evidence for two high-level strategies - engaging and governing
- containing five types of dark patterns previously not described.

###### Acknowledgements.

The research of this work was partially supported by the Klaus Tschira
Stiftung gGmbH.

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##  Appendix A Inductive Codebook Including Descriptions

|  Code |  Description
---|---|---
1 |  Addictive Design |  Features or elements that keep users hooked to content.
2 |  Auto Accept Third Party Terms |  Unknowingly giving consent to share data with third parties per default settings.
3 |  Autoplay Content |  Auto-playing content without further actions by the user.
4 |  Clinging To Accounts |  Making the process of deleting an account unnecessarily difficult or reactivating accounts after deletion has already been initialised.
5 |  Decision Governing |  Interface instances that navigate or steer users’ decision-making.
6 |  Decision Uncertainty |  Users do not know what they are left in confusion and what consequences their decision will have.
7 |  External Solution Search |  Not able to find specific features or settings, users fall back to use search engines to find what they are looking for.
8 |  False Content Customisation |  Shown content does not fit the users’ preferences or followed accounts.
9 |  Fear Of Missing Out |  Feeling pressured to (re)visit specific SNS features out of fear to miss something.
10 |  Forced Access Granting |  Features requiring unnecessary access to special device hardware or local data.
11 |  Forced Dialogue Interaction |  Prompting Text Boxes that require immediate attention with no option to dismiss them without interaction.
12 |  Forced Grace Period |  When deleting content or accounts, users are forced to wait a minimum amount of days before changes become active (often 30 days).
13 |  Gamification |  Playful elements that motivate users to do something by presenting artificial progress and/or rewards to get more data.
14 |  Hidden In Plain Sight |  Crucial information is often obscured by attention-grabbing interface elements.
15 |  Infinite Scrolling |  Users can infinitely scroll through content (often, more and more suggested content is shown the more they progress.
16 |  Labyrinth |  Nested interface structures users get easily lost in.
17 |  Persuasive Language |  Emotional pressuring language to push towards a specific direction that may not be in the users best interest.
18 |  Plain Evil |  Interface elements suddenly change functionalities or are being exchanged for alternative ones.
19 |  Pull To Refresh |  Pulling downward on a content-displaying interfaces will load new content. Sometimes, suggested content is shuffled into the feed to give users more to look at.
20 |  Reduced Friction |  Purposefully making certain elements easier accessible and thus pushing alternatives into the back.
21 |  Regression Toward The Mean |  Users are suggested topics which most people like and thus are likely to add to the already existing “popular main topics”.
22 |  Social Connector |  Requesting additional information to connect to friends/family and other social circles.
Table 3. Table containing all 22 codes from the inductive codebook from study
1, later used to create five themes deriving into the SNS-specific dark
patterns.