# Ethical User Interfaces: Exploring the Effects of Dark Patterns on Facebook

Thomas Mildner  University of BremenGermany and  Gian-Luca Savino  University
of BremenGermany

(2021)

###### Abstract.

Many researchers have been concerned with whether social media has a negative
impact on the well-being of their audience. With the popularity of social
networking sites (SNS) steadily increasing, psychological and social sciences
have shown great interest in their effects and consequences on humans. In this
work, we investigate Facebook using the tools of HCI to find connections
between interface features and the concerns raised by these domains. Using an
empirical design analysis, we identify interface interferences impacting
users’ online privacy. Through a subsequent survey ($n=116$), we find usage
behaviour changes due to increased privacy concerns and report individual
cases of addiction and mental health issues. These observations are the
results of a rapidly changing SNS creating a gap of understanding between
users’ interactions with the platform and future consequences. We explore how
HCI can help close this gap and work towards more ethical user interfaces in
the future.

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

††journalyear: 2021††copyright: rightsretained††conference: CHI Conference on
Human Factors in Computing Systems Extended Abstracts; May 8–13, 2021;
Yokohama, Japan††booktitle: CHI Conference on Human Factors in Computing
Systems Extended Abstracts (CHI ’21 Extended Abstracts), May 8–13, 2021,
Yokohama, Japan††doi: 10.1145/3411763.3451659††isbn:
978-1-4503-8095-9/21/05††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 & Motivation

During the rapid rise of social networking sites (SNS) and the number of
people using them in a little more than a decade, an increase of mental health
issues among their audiences has been recorded and published (Selfhout et al.,
2009; O’Keeffe et al., 2011; Sagioglou and Greitemeyer, 2014; Van den Eijnden
et al., 2008), while further studies looked at their impacts on social capital
and connectedness (Allen et al., 2014; Ryan et al., 2017; Ahn and Shin, 2013).
Although participants of longitude studies self-reported a negative impact on
physical and mental health when using Facebook (Shakya and Christakis, 2017),
they still kept using the application regularly. A possible explanation can be
derived from existing research on internet addiction (Kuss and Griffiths,
2011; Ryan et al., 2014; Biolcati et al., 2018). Christakis et al. has further
linked an experienced decrease of future well-being in Facebook users to the
disease in earlier works. (Christakis and Moreno, 2009; Christakis, 2010).
Other researchers, however, did not find a reason to believe that SNS cause
their audience to experience higher risks of mental health problems, such as
depression (Jelenchick et al., 2013). Moreover, significant findings show that
audiences of SNS may perceive higher levels of social connectedness (Ahn and
Shin, 2013; Grieve et al., 2013; Sinclair and Grieve, 2017) and improved
overall well-being with regards to reduced stress and social support (Nabi et
al., 2013).

As people perceive and interact with SNS on the level of user interfaces, we
believe that the perspective of the HCI community can add important insights
to describe the relationship between a person and an interface with a focus on
well-being. Recent research in HCI has already shown some interest in social
media and their features. The ‘liking’ behaviour of people on SNS, for
example, has been investigated, noticing specific elements that influence the
count of likes received (Jang et al., 2015). With a focus on the social
network Facebook, Wang et al. found that people often feel regret about
certain content that they have shared which ultimately caused them
disadvantages in social and professional areas (Wang et al., 2011). In a
follow-up study, Wang et al. implemented additional interface features that
gave people the chance to review and correct content while displaying all
recipients before publishing (Wang et al., 2013). They found that most
participants approved their features and thus demonstrated possibilities in
which interface design can respond to people’s concerns. A recent study from
Andalibi et al. describes anxiety among interviewees concerning emotion
recognition technologies that is expected to become a big part in SNS perhaps
giving new possibilities to show fitting content (Andalibi and Buss, 2020).

As a result of the fast changes and growth in technology, including SNS,
society and research have fallen behind to provide ethical guidelines creating
a ‘cultural lag’ (Gray et al., 2018; Ogburn, 1957). This lag is especially
problematic when people are being misguided into doing something that they
either did not expect or even tricked into actions with harmful results. With
regards to Brignull’s et al. work defining Dark Patterns (Brignull et al.,
2015), Grey et al. have showcased such occurrences in various digital
interfaces. Often, Dark Patterns utilise knowledge about human psychology in
combination with usable design to create deceiving design practices which do
not have the user’s interests in mind (Gray et al., 2018). Because SNS want
their audience to be recurring and promoting their service, it is unlikely
that they want them to be associated with any form of harm. Thus, traditional
Dark Patterns as defined by Brignull et al. (Brignull et al., 2015) mostly
seem unfit for SNS.

Prominent SNS, such as Facebook, Twitter, and Instagram, offer their services
for free and generate their revenue through advertisement (de Vries et al.,
2012; Kim et al., 2016). The more time people spend on them, the more income
can thus be generated. This motivates our research to take a look at design
and interface strategies of SNS that may be linked to the decreased well-being
through Dark Patterns or features similar to them. We, therefore, investigate
Facebook, the currently largest social networking site, using common HCI
methods like interface and user behaviour analysis. In this work, we present
two studies: (1) In an empirical design analysis we identify and describe
Facebook’s key interface features and their changes in the past 16 years. (2)
Through a survey with 116 participants, we analyse people’s usage behaviour of
Facebook with a particular focus on motivation, satisfaction, and privacy
concerns. Based on these studies, we highlight scopes in which Facebook
implements Dark Patterns, especially interface interferences. We further
noticed a change in people’s motivation to use Facebook due to privacy
concerns and that those increase with age. Thus, especially younger
generations might be exposed to harm through loose privacy settings and
harmful features like ‘endless scrolling’. Eventually, we noticed a few
general behavioural patterns but found worrying behaviour especially in
individual cases, which we urge to further investigate in future research.
With this paper we hope to spark the interest of the scientific community and
contribute (1) an analysis of Dark Patterns in Facebook’s desktop interface
and (2) a discussion on how HCI can help to identify potential impacts of user
interfaces to peoples’ well-being based on the responses of 116 survey
participants.

##  2\. Methodology

Since their launch date in 2004, Facebook’s interface received multiple
updates. Meanwhile, additional features brought new options for people to
engage with others and the application itself. In our research, we take a
close look at Facebook’s interface, its changes, and their effects on
Facebook’s audience while considering previous research in HCI, psychology,
and social sciences. The presented study is split into two parts: (1) An
empirical design analysis of Facebook’s user interface and (2) a survey asking
about people’s Facebook usage.

###  2.1. Empirical Design Study

Two HCI researchers analysed Facebook’s desktop user interface on an empirical
level. To collect the necessary images of past UI iterations we used manual
web crawling and collected screenshots of Facebook’s desktop interface from a
wide range of years and were crosschecking screenshots from different sources
to ensure their accuracy. Further analysis focused mainly on Dark Patterns and
privacy concerns. It included tracking certain UI-elements like the logout
button and its positioning in the navigation bar as well as several iterations
of the ‘account’ menu. The understandings gained through this empirical
approach led to the design of the Facebook user survey.

###  2.2. Facebook Usage Survey

To understand people’s motivation, satisfaction, and privacy concerns when
using Facebook, we created a questionnaire comprising of 30 questions
including attention checks. The questionnaire consisted of ‘yes/no/maybe’,
5-point Likert scale, and open-ended questions. We structured it in four
sections: (1) demographics, (2) current/past motivation to use Facebook and
most used features, (3) satisfaction of the time spent on Facebook and
specific features, and (4) the participants’ attitudes towards privacy
concerns. We recruited participants through SurveySwap and Reddit, and hosted
the surveys on Qualtrics (Qualtrics, 2021). We received 126 responses from
which we excluded 10 due to failed attention checks or double entries. In the
following results section, we analyse the data of these 116 survey
respondents.

##  3\. Results

###  3.1. Empirical Design Analysis

Developed as a student network at Harvard University in 2004, Facebook
received their most recent visual update in 2019. In the following paragraphs,
we recorded notable changes to Facebook’s desktop interface made within these
years. For readability, the most prominent findings are visualised in Figure
1. The following paragraphs present an overview of relevant changes to the
logout button, the privacy settings, and the Privacy Checkup.

####  3.1.1. Interface Interference

Although being moved around the applications’ interface, most of Facebook’s
features remained to be constant elements of the application. The logout
button, for example, was part of the top-navigation until 2010. With an
update, the button was moved into the ‘Account’ menu, limiting
discoverability. The privacy settings have experienced similar changes.
Shortly after Facebook was opened to a wider audience, the privacy settings
were moved from the navigation bar into the site’s settings drop-down menu in
2008. In 2012, Facebook offered users an alternative on the first-level view,
the Privacy Shortcuts. This feature allowed them to quickly access selected
privacy settings and tools and was removed from the first-level interface with
Facebook’s latest redesign in 2019. Even though these changes could be natural
consequences of responsive interface design, it is important to note that
Facebook does benefit from users not logging out or sharing more information
due to lighter privacy settings. This way Facebook is able to track their
users across the web and use the information to create content and targeted
advertisement. Intentionally moving these buttons to limit their
discoverability and prevent certain user actions (i.e. logouts) would be
considered interface interference in the Dark Pattern terminology (Gray et
al., 2018).

####  3.1.2. Novel Dark Patterns

During their latest redesign, Facebook introduced a new feature called Privacy
Checkup. It allows users to edit privacy settings for pre-selected categories
during a guided step-by-step process. Changes made in this feature directly
affect the privacy settings, albeit with incomplete coverage. In addition to
the privacy settings, users are further able to control ad-related settings.
These also do not include the full spectrum of settings that are available in
the general settings. Especially ad-related settings can interfere with
Facebook’s advertisement strategies. By offering a guided settings feature
Facebook is able to curate which settings users will manage. If this is used
intentionally to keep users from certain settings, it could be seen as a novel
way of interface interference. While placing all privacy settings behind
several interface layers, Facebook actively offers a well designed but
incomplete alternative to handle them.

[Figure 1 shows a diagram of Facebook’s interface changes between 2004 and
2020 with supplement data to highlight the increase of Facebook’s user count
per year and descriptive texts for certain occasions.] This diagram follows a
time-axis from 2004 to 2020. Throughout the years, Facebook updated their user
interface various times. The diagram describes interface elements as they were
added to the application while following their position throughout multiple
updates. Facebook’s first level views are mainly split into a side-bar and a
top-navigation. In 2004, the side-bar included Account-Settings and Privacy-
Settings. In 2008, these elements moved to the Top-Navigation into the second
level-view of the settings menu. A descriptive text for 2006 reads: “Facebook
opened to everyone who has email and is over 13 years old.” As a result of
this change, an invite-button, located in the top-navigation since 2004, was
soon removed in 2007. Also in the top-navigation, the logout button, which was
also present since 2004, was moved into the second level view of the settings
menu, now called ‘account’ menu. Another descriptive text for 2010 reads: “The
former text-based buttons [for] ‘inbox’, ‘friend-requests’, and
‘notifications’ are replaced with icons”. A descriptive text for 2012 states:
“Facebook introduces the Timeline next to a redesign of the website” In this
year, Facebook introduced another element – the Privacy Shortcuts. This
feature was on the first-level view of the top-navigation until Facebook’s
latest UI update in 2019. It was then moved into the ‘Account’ menu, now
visualised by an icon of a downwards pointing arrow. This icon replaced a gear
icon which was present in 2012. Also in 2019, Facebook introduced their
Privacy Checkup next to the Privacy Shortcuts, a feature that allows users to
make changes to pre-selected privacy settings. The diagram shows three further
texts: The first, a descriptive text for 2016 reads: “‘Reactions, as
additional options to the ‘Like’ button, are implemented allowing for more
interactions with posts.” The second text for 2017 reads: “Stories are
implemented as a new Feature.” A last descriptive text for 2019 states:
“Facebook announces its new interface including an almost full redesign of
their website. Facebook’s new dating feature roll’s out.”

For each year the respective user count is presented in a bar plot as
supplement data. Facebook counted 1 Million users in their last quarter of
2004. In what looks like linear growth, the count increased to 2,740 Million
users in the 3rd quarter of 2020. The user count for each year are as follows:
2004: 1; 2005: 6; 2006: 12; 2007: 58; 2008: 100; 2009: 360; 2010: 608; 2011:
845; 2012: 1056; 2013: 1228; 2014: 1393; 2015: 1591; 2016: 1860; 2017: 2129;
2018: 2320; 2019: 2498; 2020: 2740.

Figure 1. This diagram visualises Facebook’s interface changes between
2004-2020. With increasing popularity the interface was extended in multiple
iterations. Because no official and complete data-set was present at the time
of writing this work, the data of monthly Facebook users was collected from
two sources. Firstly, the data for the years between 2004-2008 were retrieved
from Facebook Newsroom (Facebook, Inc, 2021; Sedghi, 2014). As Facebook
Newsroom no longer features their original data, an article from The Guardian
that relies on the same source is cross-referenced for support. However,
comparisons need to be tentative and taken with caution. Secondly, the data
representing the years from 2008-2020 was gathered from Statista.com (Clement,
2020).

####  3.1.3. Summary

The goal of the empirical study was to find indications for Dark Patterns
within Facebook’s interface. Moving the logout button and privacy settings
into drop-down menus can be classified as interface interference (Gray et al.,
2018). Given that Facebook directly benefits from users not logging out (by
being able to track them across the web as long as they are logged in), this
can be a conscious choice to limit discoverability and thus prevent certain
user actions (i.e. logouts). Traditionally, Dark Patterns cause some degree of
harm to their audience. As SNS aim to keep theirs entertained and satisfied,
this creates a contradiction to what is typically described by Dark Patterns.
We did, however, find a novel way in which Facebook guides their users’
actions in their interest. Past research has shown that managing privacy
settings on Facebook is not an easy task for their users and most often does
not result in the level of security that they expect (Liu et al., 2011).
Facebook now offers a feature which makes this process easier, but potentially
still leaves the user with wrong expectations. This design choice leaves the
impression of traditional Dark Patterns but is not yet covered by their
terminology.

###  3.2. Facebook Survey

The following results were extracted from the survey and present people’s
usage of Facebook, as well as our three main focal points on their motivation,
satisfaction, and privacy concerns when using Facebook. Since our survey is
exploratory, we primarily used descriptive statistics.

####  3.2.1. Demographics

Of the 116 respondents 46 ($40\%$) identify as male, 67 ($58\%$) as female,
one identified as other, and two did not prefer to say. Their age ranged from
16 to 52 with a mean age of 26 ($SD=7.4$). Respondents were situated in 29
different countries with roughly half coming from the UK ($n=19$), the US
($n=23$) and the Netherlands ($n=27$). Of all participants, 81% ($n=94$)
actually use Facebook currently and 19% ($n=22$) do not. Of those who use
Facebook, 74% joined the network between 2005 and 2012. The majority (65%) of
respondents use Facebook on mobile more than on desktop, for 17% it is the
other way round. 18% use it the same amount on both.

####  3.2.2. Motivation

To get an understanding of people’s general motivation to use Facebook, we
asked participants about their current and original motivation (why they
initially signed up) as well as their most-used and most-regrettable features.
The 94 active Facebook users were allowed to give multiple replies and named a
total of 133 current motivations. We identified six main categories: (1)
’Keeping in Touch with Family and Friends’ (31%); (2) ’Groups of Interest’
(14%); (3) ’News/Information’ (13%); (4) ’Messenger/Chatting Feature’ (12%);
(5) ’Events’ (8%); and (6) ’Entertainment’ (7%). 21 replies (15%) did not fit
those categories and comprise of individual motivations. Regarding the
original motivation (when first joining Facebook) respondents named a total of
88 which were sorted in the same six main categories: (1) ’Keeping in Touch
with Family and Friends’ (41%); (2) ’Groups of Interest’ (3%); (3)
’News/Information’ (0%); (4) ’Messenger/Chatting Feature’ (6%); (5) ’Events’
(2%); and (6) ’Entertainment’ (3%). Within the remaining 39 replies we
identified two other large categories: (1) ’Like/Share’ (20%); (2) ’Peer
Pressure’ (11%). ’Keeping in Touch with Family and Friends’ remains an
important feature of Facebook from back when people original started using it
to today. While Facebook’s ’Groups of Interest’ feature is mentioned more
often as a current motivation compared to the original one, ’Like/Share’ seems
to have declined being mentioned only once as a current motivation. The
participants were further asked to state their most used and most regrettable
Facebook features. The two most used features of a total of 181 features
mentioned, were (1) ’Messenger’ (29%) and (2) ’Groups of Interest’ (21%).
Other notable features comprised of: (3) ’Like/Share’ (8%); (4) ’Newsfeed’
(8%); and (5) ’Entertainment’ (7%). Additionally, participants mentioned 29
features they regret spending too much time on. People mostly regret spending
time on ’Entertainment’ (34%) features on Facebook, followed by the ’News
Feed’ feature (24%). Together ’Entertainment’ and ’News Feed’ makeup 15% of
people’s most used features. Thus, they are among the lesser-used features
compared to others. Still, some people use them while, at the same time,
regret spending too much time on them.

####  3.2.3. Satisfaction

We further asked participants about their satisfaction with the time they
spent on Facebook and whether they would like to change something about it.
Most users were extremely (29%) and somewhat (39%) satisfied with the time
they spent on Facebook. 20% answered that they were neither satisfied nor
dissatisfied and the remaining 12% were somewhat dissatisfied. No participant
was extremely dissatisfied which was mostly due to them wanting to spend less
time on Facebook rather than more time. 49% of respondents reported to
sometimes find themselves longer on Facebook than they had planned to and 13%
about half the time or more often. Only 37% never find themselves using it
longer than they planned to. In contrast, 80% of respondents never want to
spend more time on Facebook than they already have. When asked directly, 14%
think they spend too much time on Facebook, 18% think they maybe do, and 68%
do not think they spend too much time on Facebook.

The 32% ($n=30$) who think that they do or maybe do spend too much time on
Facebook listed the following reasons for why they think this is the case: 7
participants answered that they use Facebook to procrastinate and distract
themselves from tasks they should be doing; 4 answered that it is addictive or
has addictive features and that they would like to stop using it but still use
it every day; 4 mentioned that they often mindlessly scroll and that the
infinite scrolling behaviour of the timeline supports this habit. Other
responses included that it is just entertaining, boring, or a waste of time.
Finally, we asked all 94 participants if they would like to actually spend
less time on Facebook. To this, 50% answered that they do not want to spend
less time, 21% replied with maybe, and 29% do would like to spend less time.

####  3.2.4. Privacy Concerns

In order to learn more about people’s privacy concerns, our questionnaire
comprised four directed question items. We distinguished between social
interaction and business related incentives from Facebook by asking targeted
questions in a 5-point Likert-Scheme. A majority of the participants generally
want to be in control over the information that other people can see about
them. For 50% of them, this is an extremely important desire while 1% do not
find this control important at all. When asked about whether they actually
feel in control over such information, 63% agree while 23% rather do not. When
instead asking about the control over the information that Facebook uses for
advertisement, 29% find it extremely important whereas 6% find it not
important at all. We again asked about whether they feel in control over
Facebook’s use of their data for advertisement. While 13% actually feel in
control over the information that Facebook uses for advertisement, (definitely
yes - 4%), 70% of our participants do not feel in control (definitely no -
34%). This is further visualised in Figure 2. Eventually, we asked
participants to state their logout behaviour as well as their general concern
about internet security. Most of the participants (79%) never log out of
Facebook, 7% log out regularly, and 5% do so always. When it comes to internet
security, 73% are generally concerned whereas 11% are not.

##  4\. Discussion

Research is divided on what effects social media and SNS have on people’s
mental health. While some results show small positive effects when spending
time on social media, others yield small negative effects, or did not identify
any effect at all. On one hand, various studies have found connections between
increased depressive symptoms, that in occasions tragically resulted in
suicides, and increased screen-time, new media, and SNS (Twenge et al., 2018).
On the other hand, researchers did not identify any such links (Coyne et al.,
2020) or instead observed overall positive effects on people’s well-being (Ahn
and Shin, 2013; Grieve et al., 2013; Sinclair and Grieve, 2017).

[Figure 2 compares four horizontal bar plots that show their results in a
Likert-Scheme from 1-5. Questions asked about people’s privacy concerns when
using Facebook while comparing their importance to control specific data and
their feeling to be in control.] This graphic shows four horizontal bar plots
for the items: a) ’How important is it for you to be in control over the
information other people can see about you on Facebook?’; b) ’How important is
it for you to be in control over the information about you which Facebook uses
for targeted advertising?’; c) ’Do you feel in control over the information
other people can see about you on Facebook?’; d) ’Do you feel in control over
the information about you which Facebook uses for targeted advertising?’ The
bar plots are arranged so a comparison between them can be done with respect
to people’s importance to be in control of their data as well as their feeling
to be in control. It can be seen, that while 50% of the participants find it
extremely important to be in control over the information that others can see
about them, only 29% expressed the same importance regarding information
Facebook uses for advertisement. In general, people found it less important to
be in control over data used for advertisement than what others can see about
them. A similar shift can be observed for the items asking about peoples’
feelings to be in control. While 63% of our participants feel to be in control
over the information that others can see only 13% feel in control over the
information that Facebook users for advertisement. Meanwhile, 23% say that
they don’t feel in control over information that others can see while 70% say
that they don’t feel in control over the information used for advertisement.

Figure 2. This graphic shows the results of the questions: a) ’How important
is it for you to be in control over the information other people can see about
you on Facebook?’; b) ’How important is it for you to be in control over the
information about you which Facebook uses for targeted advertising?’; c) ’Do
you feel in control over the information other people can see about you on
Facebook?’; d) ’Do you feel in control over the information about you which
Facebook uses for targeted advertising?’ of the survey.

As so often in technology, SNS change quickly and thus require research to
regularly re-evaluate their impact. HCI has the utilities to continuously
analyse SNS and their effects on users with a particular focus on human-
centred design. It can describe the relationship a person has with an
interface and actively propose design guidelines to prevent possibly harmful
design choices such as Dark Patterns.

In this work, we focused on this relationship between people and interfaces.
We highlighted Facebook’s continuous interface changes and noticed that design
choices have led to potential Dark Patterns. In the presented empirical design
study, we have identified two interface interferences. As can be seen in
Figure 1, Facebook has changed the location of the logout button as well as
the link to the privacy settings multiple times while offering alternative but
incomplete features. Although the cause for these changes may be reasoned
through aesthetics, they result in unnecessary obstacles that users have to
overcome. In the following discussion, we aim to create possible links between
the presented studies. Our interface analysis only focuses on Facebook’s
desktop application while most participants of our survey preferred the mobile
application. Thus, the conclusions drawn from these comparison should not be
generalised as they reflect only on a smaller portion of our sample.

Even though they might not be intentionally developed, Dark Patterns can be
used to evaluate the ethical principles of interfaces by assessing the
difficulty of, for example, basic user actions. In the case of Facebook’s
logout button, it could explain why a majority of the participants never log
out of Facebook, while also showing concern about internet security. Since
Facebook uses cookies to track personal data within and outside their website
(Facebook, Inc, 2020), our findings demonstrate an incongruity between
people’s privacy concerns and their actual activities when using SNS. As can
be seen in Figure 2, our results show that participants want control over the
data that they share with others. This preference also exists for the data
which Facebook uses for advertisement, although to a lesser extent. During
their updates, Facebook chose to implement their Privacy Shortcuts and Privacy
Checkup as alternatives to regular settings. With both, they provide their
audience with controls over the general privacy settings. However, by limiting
the number of options Facebook governs over the settings that their audience
will adjust. Although such alternative interfaces are not covered by
traditional Dark Patterns, they create interferences that allow Facebook to
navigate their audience’s decision-making without causing immediate harm.

Governing, but never harming, users makes it possible to keep their general
satisfaction with Facebook high. Even though the majority of participants are
generally satisfied with their time spent on Facebook, most of them spend more
time than they actually plan to. Those who actually think they do spend too
much time mention reasons like procrastination, distraction, mindless
scrolling and even addiction. This shows a worrying development in which
individual participants even realise their bad habits: ”No time at all would
be the ideal time, but the fact that I still open the app once a day shows a
bad habit.” P(102). Facebook seems to keep general satisfaction up, but it is
the individual person who develops problematic usage behaviours through
features like endless scrolling (_”infinite scroll makes me stay longer”_
(P113)). Investigation of such features offer insights into how user
interfaces may affect people’s well-being.

An important part of this process is to understand people’s incentives and
motivations when using a service. By understanding what features people use or
do not use or how their usage behaviour has changed over time, we are able to
identify user groups and can connect them to behaviours and habits. Individual
comments of the participants describe a perceptional change in their
motivation to use Facebook. Sharing content, for example, made up 20% of
people’s original motivation to create a Facebook account: _”To […] post about
my social life”_ (P44). Interestingly, it was only mentioned by one
participant as their current motivation. A potential reason for this change
could be the increased privacy concern as people grew up: _”[I] used to be
more active to share my life with others, but I don’t anymore due to the
privacy concern”_ (P52). Our results show that with age participants’ privacy
concern also increased ($r=0.23$, p-value = $0.029$). Unfortunately, we were
not able to show statistically significant relationships between features or
motivations and users’ satisfaction. Still, this analysis helped us to
understand what people do on Facebook and why they might quit the service or
never even use it in the first place. We asked the 22 participants (19% of our
participants), who do not use Facebook actively, why they either stopped using
the SNS or never even registered. Although their replies are highly
individual, they foreshadow a link to the concerns of psychological and social
science scholars, namely to well-being. P11 stated, that _”it was bad for
[their] mental health”_ while P13 felt _”judged by likes and comments”_.
Although still a Facebook user, P24 wrote: _”I felt bad about myself and my
life”_ , as a reason for why their motivation has changed.

In this work, we explored how standard HCI methods like interface and user
behaviour analysis can be used to identify potentially harmful aspects of
Facebook’s user interface and discuss how those could relate to effects on
people’s well-being. We do not find large numbers indicating general problems
with SNS, but individual users show worrying usage behaviour and even
specifically state mental health issues. As Beyens et al. (Beyens et al.,
2020) show, there can be vast individual differences, especially between
adolescent users, in the way SNS affect their well-being. We, therefore, argue
that researching the individual is just as important as the large scale
studies which are already available. Through our approaches, we propose a
first guideline for ethical interface design, namely considering users’
motivation and expectations while testing user interfaces for Dark Patterns.
Practitioners should further understand that high user satisfaction and
usability is not necessary a result of ethical user interfaces.

##  5\. Limitations & Future Work

The empirical design analysis focuses only on Facebook’s desktop interface, as
it proved to be difficult to find enough authentic screenshots for all mobile
versions due to various screen layouts based on different device sizes and
operating systems (iOS and Android). An in-depth analysis of the mobile
interface was thus out of scope for our work. Yet, the results of the survey
show that most participants prefer Facebook’s mobile application. While
Facebook’s latest interface update makes comparison between both interfaces
easier, since both layouts show little to no differences, caution should be
excersised when drawing conclusions. Future research should distinguish
between all interface variations.

As with most studies utilising qualitative surveys, participants assessed
their experience with Facebook. Comparing users’ current usage behaviour with
Facebook to their original motivation is necessary to understand how design
changes may alter people’s handling of a system. Nevertheless, the presented
study asked participants to recall their experiences making these replies
susceptible to recall bias. Moreover, chosen terminology, especially regarding
one’s well-being, are subjective. This subjective assessment allowed us to
conduct in-depth analyses. We do, however, acknowledge that our sample was too
small to find significant results in other parts of the evaluation. Especially
for the results on participants’ motivation larger sample sizes are necessary
to fully understand their relevance.

The presented research only focuses on Facebook since it is still the most
widely used SNS. While more thorough research should continue to study
Facebook, similar studies should investigate other SNS as the range of
available SNS has grown in recent years. The research on the protection of
human’s well-being on SNS is an interdisciplinary effort. HCI adds a range of
utilities that help to understand the interaction between people and SNS on
the level of interface and technology. This community can thus support
psychological and social sciences by actively impacting future design
development and providing ethical guidelines while advancing its
interdisciplinary efforts.

##  6\. Conclusion

This paper presents two studies investigating the social medium Facebook. The
first describes an empirical study analysing Facebook’s continuous interface
changes throughout recent years. We find cases of interface interference that
Facebook has implemented with respect to the logout button and the privacy
settings. In contrast to traditional Dark Patterns, these do not cause direct
consequences but indirectly govern how they interact with the interface.
Through a survey with 116 participants, the second study finds that users’
motivation changed over time, making them share less personal data than they
used to, due to increased privacy concerns. While being concerned about their
private and ad-related data, users only partly feel in control about the
amount they share. This feeling could be a result of the interface
interference through which Facebook actively limits users’ choices and guides
their behaviour. This negatively impacts users (e.g. sharing information by
not logging out), demonstrating an example for Dark Patterns on SNS. With this
contribution, we explore different methods to show how HCI can contribute to
investigating the well-being of social media users and hope to spark the
interest of the scientific community to engage in this research.

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# Comment on arXiv:2312.04495 by M. I. Eremets and coauthors

J. E. Hirsch  Department of Physics, University of California, San Diego, La
Jolla, CA 92093-0319

###### Abstract

In the recently posted arXiv:2312.04495v3 talv3 , its authors used a computer
code written by me supplied to them by the authors of Ref. hmtrapped , and
claimed that the results that they obtained invalidate the results and
conclusions presented in the paper J. Supercond. Nov. Mag. 35, 3141-3145
(2022) by F. Marsiglio and myself hmtrapped . Here I point out that the
authors’ claim (i) resulted from improper unauthorized use of my computer
code, (ii) is wrong, and (iii) is misleading to the scientific community.

##  I introduction

In Ref. hmtrapped , F. Marsiglio and I analyzed experimental results on
trapped magnetic flux in hydrides under pressure published in Ref. etrappedp
by Minkov, Ksenofontov, Bud’ko, Talantsev and Eremets, and we concluded from
our analysis that the experimental results indicated that the residual
magnetic moment measured after the external magnetic field was turned off
under zero field cooling (ZFC) conditions did not originate in superconducting
currents hmtrapped , hence did not provide proof that the materials were
superconducting, contradicting the claims of Ref. etrappedp .

At a scientific meeting in the Summer of 2023, one of the authors of Ref.
etrappedp  expressed their disagreement with our conclusions in Ref. hmtrapped
, and their interest to study the computer codes that we had used for our
analysis presented in Ref. hmtrapped . On September 18, 2023, that author
requested by email to F. Marsiglio and myself that we provide the codes used
to generate the curves presented in the figures in our paper Ref. hmtrapped ,
stating “I am confident that together we will reach a consensus.”

The figures shown in Ref. hmtrapped  were generated using computer codes
written independently by the two authors of Ref. hmtrapped , that were based
on the model defined in Ref. hmtrapped , that yielded identical results. On
September 19, 2023, we sent this author one of my computer codes, named
$cylinder.f$. We did not include specific instructions on how to use the code,
expecting that the author would contact us if questions arose. At no point did
we give that author nor anybody else permission to publish neither the
computer code nor results derived from the computer code.

Figure 1: Panels (a) and (b) of Fig. 2 of Refs. talv1 ; talv2 ; talv3 . The
figure caption of Refs. talv1 ; talv2 ; talv3 , as it refers to these panels,
reads “Experimental ZFC $m(B_{appl},T=30K)$ dataset measured in highly
compressed $H_{3}S$, together with reproduced simulations and standard data
fitting. (a) The original Figure 5 from Ref.1; (b) the reproduced full
simulation curve utilizing the original simulation code, the reported fixed
$\mu_{0}H_{p},\mu_{0}H^{*},m_{sat}$, and $\mu_{0}H_{max}$ parameters and the
aforementioned authors´ manipulations demonstrating the unphysical anomaly
within the Meissner state…Open rhombuses represent experimental data, black
and red curves represent simulation and fitting curves.” Here, Ref. 1 is Ref.
etrappedp  and “the aforementioned authors” are the authors of Ref. hmtrapped
.

##  II the physics and the computer code

In Ref. hmtrapped , we modeled what the trapped magnetic field in a hard
superconductor would be when a magnetic field $H_{M}$ is applied at low
temperatures and subsequently removed, i.e. zero field cooling (ZFC) protocol.
We used the well-known Bean model, that assumes a critical current independent
of magnetic field.

The model has two important parameters: (1) The threshold magnetic field
$H_{p}$, below which the applied magnetic field does not penetrate and hence
cannot be trapped, and the field $H^{*}$: for applied magnetic field
$H_{M}=H^{*}$, the applied field reaches the center of the sample. $H^{*}$ is
proportional to the critical current density. The threshold magnetic field
$H_{p}$ is the lower critical field $H_{c1}$ if the sample is a long cylinder,
and for other sample shapes it is the lower critical field $H_{c1}$ corrected
for demagnetization. In any case, for applied magnetic field $H_{M}<H_{p}$ the
applied magnetic field does not penetrate the sample, hence cannot be trapped,
hence when the external magnetic field is removed the resulting magnetic
moment is exactly zero.

All the figures shown in our paper Ref. hmtrapped  that show results of our
model, namely Fig. 3, Fig 4 and Fig. 5, and their insets, show that the
trapped magnetic moment predicted by our model for $H_{M}<H_{p}$ is exactly
$zero$.

In their paper arXiv:2312.04495v1,v2,v3 talv1 ; talv2 ; talv3  the authors
show in their Fig. 2 the two panels shown in Fig. 1 here. The left panel of
Fig. 1 shows the inset of Fig. 5 of our paper Ref. hmtrapped , the red dashed
curve on the left panel of Fig. 1 shows the prediction of our model for zero
field cooling (ZFC), as published in our paper Ref. hmtrapped . The red curve
reaches zero at magnetic field $H_{M}=H_{p}=0.042T$ and does not continue for
$H_{M}<H_{p}$ because in that range there cannot be a trapped moment under ZFC
since the applied field never penetrated the sample, as explained above.

The right panel of Fig. 1 show what the authors of Refs. talv1 ; talv2 ; talv3
obtained when they ran the computer code $cylinder.f$ provided by us to them
and plotted the numbers that the computer code printed in unit 12. Those
numbers joined by a smooth line result in the dashed red line on the right
panel of Fig. 1.

It can be seen that the dashed red curves are identical on the left and right
panels of Fig. 1 in the field range $H_{M}\geq H_{p}$, with $H_{p}=0.042T$.
Instead, for $H_{M}<H_{p}$ there is no dashed red curve on the left panel,
while on the right panel there is a dashed red curve that rises as $H_{M}$
becomes lower.

What is the reason for this discrepancy? On the left panel there is no dashed
red curve for $H_{M}<H_{p}$ because there can be no trapped magnetic moment if
the magnetic field didn’t penetrate, as explained above. On the right panel
there is a dashed red curve for $H_{M}<H_{p}$ because the authors of Refs.
talv1 ; talv2 ; talv3  took the numbers from the output of the computer code
$cylinder.f$ in the region $H_{p}<H_{M}$ and plotted them and connected them
with a dashed red line.

However, those numbers don’t have any physical meaning and should not be
interpreted as having any physical meaning. Anybody that understands the
computer code $cylinder.f$ and how to use it, and understands the physics of
the model used in our Ref. hmtrapped  to describe trapped flux in hard
superconductors, knows this. The authors of Refs. talv1 ; talv2 ; talv3 ,
because they ran a computer code that did not belong to them, and used it
without checking with the authors of the code how the code should be used and
how the numbers printed out by the code should be interpreted, apparently
don’t know this.

From the upturn in the dashed red curve seen on the right panel of Fig. 1 for
field $H_{M}<H_{p}$, and similar upturns shown in Figs. 1(a), (1b) of Refs.
talv1 ; talv2 ; talv3  also for $H_{M}<H_{p}$, the authors of Refs. talv1 ;
talv2 ; talv3  concluded that:

  * •

“Significantly, the Hirsch-Marsiglio model does not describe the Meissner
regime”,

  * •

“the model predicts a significant positive magnetic moment for ZFC”,

  * •

“This feature of the proposed model was not discussed by the authors and
obviously contradicts the physics for the trapped magnetic flux in
superconductors”,

  * •

“they simulated the $m(B_{appl})$ curves by implementing the following step-
by-step procedure… manually hide/delete all simulated points within the
Meissner state, i.e. for $B_{appl}\leq\mu_{0}H_{p}$

  * •

“we conclude that the authors in their Figures 3-5 hid/deleted (without
reporting this) parts of their simulated datasets that disagree with the
Meissner state. This potentially allowed them to conceal the issue that their
model and computer code do not adequately describe the Meissner state.”

  * •

“The simulations conducted by the authors involve … and unjustifiably deleting
parts of the simulation dataset.”

  * •

Caption of Fig. 2: “…the aforementioned authors´ manipulations demonstrating
the unphysical anomaly within the Meissner state”

  * •

Abstract: “Hirsch and Marsiglio, in their recent publication (J. Supercond.
Nov. Mag. 35, 3141–3145, 2022), assert that experimental data on the trapping
of magnetic flux by hydrogen-rich compounds clearly demonstrate the absence of
superconductivity in hydrides at high pressures. We argue that this assertion
is incorrect, as it relies on the wrong model coupled with selective
manipulations (hide/delete) of calculated datasets…”.

These conclusions of Refs. talv1 ; talv2 ; talv3  are (i) wrong, (ii)
misleading to the scientific community, and (iii) were obtained using a
computer code that did not belong to the authors of Refs. talv1 ; talv2 ;
talv3 , in a way that was incorrect and inconsistent with the way the computer
code was designed to be used, without authorization from the code’s authors,
and without checking with the code authors whether the code was used properly.

##  III summary

In summary, Refs. talv1 ; talv2 ; talv3  used in an incorrect way a computer
code written by me supplied by F. Marsiglio and me to one of the authors of
Ref. etrappedp  responding to their request with the stated goal “I am
confident that together we will reach a consensus.”, drew an incorrect
conclusion from it, and posted Refs. talv1 ; talv2 ; talv3 , that contain
several explicit statements misrepresenting our work Ref. hmtrapped , as
discussed in this Comment. Namely, the incorrect conclusion that the model
used in Ref. hmtrapped  yields the unphysical conclusion that a trapped
magnetic moment appears when a magnetic field smaller than the lower critical
field is applied to the sample and then removed, in direct contradiction with
the analysis, discussion and results presented in our paper Ref. hmtrapped .
The authors of Refs. talv1 ; talv2 ; talv3  did not request nor receive
permission from us to publish results derived from the code that we sent them,
nor did they send us their paper in advance of posting it (nor thereafter) to
request our comments.

In a separate paper, F. Marsiglio and I will address other issues about our
paper Ref. hmtrapped  that were raised in arXiv:2312.04495.

Note: the original version of this Comment, submitted to arXiv on 12/19/2023,
can be found in Ref. original .

## References

  * (1) E.F. Talantsev, V.S. Minkov, V. Ksenofontov, S.L. Bud’ko and M.I. Eremets, “Is MgB2 a superconductor? Comment on “Evidence Against Superconductivity in Flux Trapping Experiments on Hydrides Under High Pressure” [J. E. Hirsch and F. Marsiglio in J. Supercond. Nov. Mag. 35, 3141–3145 (2022)]”, arXiv:2312.04495v3, December 13, 2023.
  * (2) J. E. Hirsch and F. Marsiglio, “Evidence Against Superconductivity in Flux Trapping Experiments on Hydrides Under High Pressure”, J Supercond Nov Magn 2022; 35: 3141–3145.
  * (3) V. S. Minkov, V. Ksenofontov, S. L. Bud’ko, E. F. Talantsev and M. I. Eremets, “Magnetic flux trapping in hydrogen-rich high-temperature superconductors”, Nat. Phys. (2023).
  * (4) E.F. Talantsev, V.S. Minkov, V. Ksenofontov, S.L. Bud’ko and M.I. Eremets, “Is MgB2 a superconductor? Comment on “Evidence Against Superconductivity in Flux Trapping Experiments on Hydrides Under High Pressure” [J. E. Hirsch and F. Marsiglio in J. Supercond. Nov. Mag. 35, 3141–3145 (2022)]”, arXiv:2312.04495v1, December 7, 2023.
  * (5) E.F. Talantsev, V.S. Minkov, V. Ksenofontov, S.L. Bud’ko and M.I. Eremets, “Is MgB2 a superconductor? Comment on “Evidence Against Superconductivity in Flux Trapping Experiments on Hydrides Under High Pressure” [J. E. Hirsch and F. Marsiglio in J. Supercond. Nov. Mag. 35, 3141–3145 (2022)]”, arXiv:2312.04495v2, December 10, 2023.
  * (6) J. E. Hirsch, “Comment on arXiv:2312.04495 by M. I. Eremets and coauthors”, https://doi.org/10.31219/osf.io/p29ht (2023).