# Facebook Data Shield: Increasing Awareness and Control over Data used by
Newsfeed-Generating Algorithms

Jules Sinsel<EMAIL_ADDRESS>Department of Industrial Design,
Eindhoven University of TechnologyEindhovenThe Netherlands ,  Anniek Jansen
<EMAIL_ADDRESS>Department of Industrial Design, Eindhoven University of
TechnologyEindhovenThe Netherlands and  Sara Colombo<EMAIL_ADDRESS>Department of Industrial Design, Eindhoven University of
TechnologyEindhovenThe Netherlands

(2023)

###### Abstract.

Social media platforms newsfeeds are generated by AI algorithms, which select
and order posts based on user data. However, users are often unaware of what
data is collected and employed for this aim, neither can they control it. To
open up discussions on what data users are willing to feed the newsfeed
algorithm with, we created the Facebook Data Shield, a human-size interactive
installation where users can see and control what type of data is collected.
By pressing buttons, data categories and/or data variables can be
(de)activated. An outer rim with lights gives feedback to users about the
level of personalization of the resulting newsfeed. We performed a preliminary
study to get insights into what data users are willing to share, their
preferred level of control, and the effect of such an installation on users’
awareness. Based on our findings, we discuss implications for design and
future work.

tangibility, Facebook, social media, user study, data control

††copyright: acmcopyright††journalyear: 2023††copyright:
rightsretained††conference: TEI ’23: Proceedings of the Seventeenth
International Conference on Tangible, Embedded, and Embodied Interaction;
February 26-March 1, 2023; Warsaw, Poland††booktitle: TEI ’23: Proceedings of
the Seventeenth International Conference on Tangible, Embedded, and Embodied
Interaction (TEI ’23), February 26-March 1, 2023, Warsaw, Poland††doi:
10.1145/3569009.3573116††isbn: 978-1-4503-9977-7/23/02††ccs: Human-centered
computing Interactive systems and tools

##  1\. Introduction

Facebook plays an important role in the life of many people, who use it, among
others, for social interactions, entertainment, or news consumption. With 2.9
billion users in 2022 (Dixon, 2022), it is the social media platform with the
largest user base. While Facebook’s services are offered for free to users,
revenue is generated by leveraging the data they collect in different ways,
including online behavioral advertising (OBA) (Fiesler and Hallinan, 2018). To
do this effectively, Facebook collects and uses data to profile users and
serve targeted ads based on such profiles (Habib et al., 2022). The data they
collect usually consist of both “voluntarily” offered information when signing
up, a.o. demographic information, and data gathered by monitoring users’
online behavior (Joler et al., 2016b). This includes monitoring the behavior
on Facebook, e.g., how long one interacts with a post or if they “like” it,
but also on other platforms through the use of cookies (Joler et al., 2016b).
This process of data collection and processing is usually a black box for
users. Even for researchers in related domains, it is not possible to
completely map the practices Facebook adopts (Joler et al., 2016a).

(a) The FDS in its totality, annotated with the different elements

Overview photo with the whole FDS visible. Blue lights are visible in the
outer rim and between the data variables in the detailed layer and core. There
are footprints at the base of the FDS to indicate where people need to stand.
There are lines indicating what the outer rim, detailed layer and core is.

(b) The detailed layer rotating around the core.

A photo where the detailed layer is partly rotated. The core is fully visible
but all lights are off and you see a faint glow of blue in the outer rim.

(c) The core of the control panel with the data categories and upload data
buttons

Photo of the five data categories in the core and the upload data button in
the middle. Outside the core, you see the black pyramids, which reflect the
light from the outer rim.

Figure 1. The Facebook Data Shield

To make OBA even more profitable, major social media platforms, including
Facebook, tend to maximize the time users spend on their applications. This is
achieved in different ways, including customizing the newsfeed (also called
’feed’) to the interests and preferences of users. AI algorithms are employed
to predict users’ interests and behavior and to filter and order the posts
displayed on a newsfeed so that the user feels engaged and compelled to keep
interacting with the platform (Meta, 2021).

The practices of OBA and newsfeeds personalization raise concerns about data
privacy and their effect on society. It has been proven that they can lead to
discriminating users (Ali et al., 2019), spreading fake news (Rocha et al.,
2021; Ali and Zain-ul abdin, 2021) and creating filter bubbles (Flaxman et
al., 2016). Previous studies have shown that people are not always aware of
these practices and when they discover them, they often feel uncomfortable
with the data that is being collected (Center, 2019; Habib et al., 2022;
Calbalhin, 2018). However, a segment of users also considers these targeted
ads useful (Habib et al., 2022; Ur et al., 2012).

Users can exert some control by changing their privacy settings or how their
newsfeed is generated. However, these controls are hidden inside nested menus,
which makes them not only hard to find but also less known to users (Habib et
al., 2022, 2020; Hsu et al., 2020). For example, Facebook currently allows
users to choose how their newsfeed is generated, but this is a hidden setting
that needs to be activated each time the platform is accessed.

While prior work has identified and analyzed the issues around data control
through empirical studies, we adopt a critical design approach (Bardzell et
al., 2012) where we aim to disrupt the current practices of data sharing and
give full control to users. To achieve this, we designed the Facebook Data
Shield (FDS), a human-size physical installation resembling a control panel,
which embeds a selection of data points that Facebook currently collects.
Users can control what data they want to share with the algorithm that adjusts
their newsfeed by interacting with the panel. The FDS consists of three parts
(see Figure 1): (i) an inner circle, the _core_ , showing five general data
categories that are being collected (e.g., demographic or behavior
information); (ii) a rotating disk around it, the _detailed layer_ , which
offers the user a more granular control by giving access to specific data
variables within each category, and (iii) a lighted outer rim, which
visualizes how much control the algorithm has over the user’s newsfeed — and
the consequent level of personalization of the newsfeed, by varying the light
intensity and speed.

To explore what data regular Facebook users want to share and how they use the
control panel to select the data they want to share, we ran preliminary
studies with 10 industrial design students. Our results show that the layered
control panel makes it possible for users to interact with the installation
without being overwhelmed with information, while still being provided with
enough detail to feel informed and in control. The main contribution of this
work in progress is the Facebook Data Shield as a design exemplar of the use
of critical design and tangible interactions in the context of online data
sharing.

##  2\. Tangibility and privacy

Making data or privacy settings more tangible to enhance users’ understanding
and control of privacy has been used as an approach in previous studies,
although the majority of these works focus on privacy in IoT devices (Delgado
Rodriguez et al., 2022; Ahmad et al., 2020; Mehta, 2019; Al Muhander et al.,
2022). For example, PriKey is a small controller in the shape of a key with
sliders for control, which offers house inhabitants or visitors control over
which sensors are being used in the home, by either disabling them
individually or all at once (Delgado Rodriguez et al., 2022). Another example
is the PrivacyCube, which gives users insight into the type of data being
collected by active IoT resources, where it is stored, for what purpose, and
who has access to the data (Al Muhander et al., 2022). However, to the best of
our knowledge, no work has focused on making the data in social media
applications tangible. This is the focus of this work.

##  3\. Design: Facebook Data Shield

In this work, we take a different approach from prior research. Instead of
performing empirical studies based on the design and testing of real
interfaces for privacy control, we adopt a critical design approach (Bardzell
et al., 2012) by creating an installation that invites people to engage in a
discussion about data control. The installation can be seen as a research
probe aimed at both increasing users’ awareness and generating knowledge on
users’ preferences for data control.

Table 1. All data categories and variables included in the Facebook Data Shield Data categories in core |  Data variables in detailed layer
---|---
Your interactions with your social network |  Your interactions with Facebook groups
Your interactions with Facebook events
Your interactions with Facebook pages
Friends’ interactions with Facebook pages
Friends’ interactions with you
Your interactions with friends
Your behavior |  Contents you shared
Your likes
Your comments
Contents you created
How long you interacted with a content
Your behavior on the web
Your behavior on Facebook-owned platforms
Your technological set-up |  Type of device
Internet quality
Your demographic information |  Your residential location
Your native language
Your education status
Your career information
Your relationship status
Your gender
Your age
Post-related information |  Public engagement
Moment of publication
Subject of post
Type of post

###  3.1. Design considerations

Prior work showed that it can be challenging for users to find where they can
change their privacy settings (Habib et al., 2020, 2022). In our design, we
eliminated this step, and we only focused on the design of the control itself.
By doing so, we intended to explore _’what’_ data users are willing to share
and at what level of detail they want to control what data is being shared.
The FDS is designed with the vision that it is an object that can be placed in
public spaces, where it can attract people to interact with it.

###  3.2. Data variables

To determine which data variables should be included in the FDS, we turned to
the work of Joler et al. (Joler et al., 2016b), who created a mapping of the
Facebook algorithm, including the data it collects. They identified two
clusters of data. The first set of data is the data collected _within
Facebook_. This data consists of _Your activities and behavior_ on the
platform, e.g., _likes, comments, uploaded posts, page visits_ , etc., and
_your profile information_ , this is data you have entered in your account
such as your _age, gender, relationship, work_ , etc. The second set of data
is _Your digital footprint_ , i.e., data collected through your devices and
other platforms. Facebook collects this data through cookies and Facebook
pixels (small pieces of code that can be embedded in other websites to measure
the effectivity of ads and the behavior of visitors) (Meta, nd), but also
mobile permissions, i.e., data stored on your phone and information such as
your precise location or type of device, and other companies owned by Meta
such as WhatsApp and Instagram (Joler et al., 2016b).

The overview made by Joler et al. (Joler et al., 2016b) includes too many data
variables (¿100) to include in the FDS. Showing users a high number of
variables would prevent them from exploring all data points and select the
ones they truly want to share. Moreover, it would slow down the interaction,
and it may potentially overwhelm the user, decreasing their attention and
interest in interacting with the installation. Overall, we decided to favor
enabling the user to fully explore and assess a smaller number of data
variables, versus giving them a full overview of all data used by the newsfeed
algorithm. As identified by Waldschütz and Hornecker (Waldschütz and
Hornecker, 2020), this process of data curation is an important aspect when
representing data physically. To reduce the number of variables, we clustered
them (e.g., number of likes and contents of likes were clustered into a
“likes” variable) and selected the ones that would give users a sense of the
type of data Facebook collects and that could be categorized into overarching
data categories.

Since we were interested in the granularity of the control users wish to
exercise on their data, we divided the variables into two layers, showing
different levels of detail. The first layer includes five general data
categories, such as _demographic information_ and _technological setup_. The
second layer includes more specific data variables belonging to the five
categories in the first layer. Examples in this layer for the _Your behavior_
category are _Contents you share_ and _Your behavior on Facebook-owned
platforms_. In total, 26 data variables are included in the detailed layer. A
full overview of the selected data variables and to which layer they belong
can be found in Table 1. On the FDS, the first and second layers are connected
by a dynamic light path. When deactivating a variable in the general category
layer, the light path and all the corresponding detailed variables are also
switched off.

###  3.3. Facebook Data Shield

The FDS consists of three parts (see Figure 1): (i) an inner circle, i.e.,
_the core_ , showing the five general categories (see Figure 1(c)), (ii) a
rotating disk around it, i.e., _the detailed layer_ , displaying the variables
from the second layer, and (iii) an outer rim, which gives a light feedback.

The _core_ includes the five data categories as buttons (see Figure 1(c)).
Each button is surrounded by a light ring that shows if it is activated (light
on) or deactivated (light off). The user can activate or deactivate each
variable by pressing the button. In the middle of the core is the _upload
data_ button, which is pressed by the user to mark the end of the interaction
and to finalize the variable selection.

The _detailed layer_ contains the 26 detailed data variables, represented by
26 buttons on a disk (see Figures 1(b) and 1(a)). The buttons are connected to
the corresponding data category in the core through light strips. Again, each
data variable can be (de)activated by pressing the button. As feedback, the
light strip will turn on or off. The disk can be rotated 180 degrees, to hide
the detailed data variables. The other side shows a landscape of black
pyramids designed to reflect light and create a perception of impenetrable
obscurity, like the black box algorithm.

Around the _core_ and _detailed layer_ is the _outer rim_ , which represents
how much control the Facebook algorithm has over the user’s newsfeed, i.e. the
level of personalization (see Figures 1(b) and 1(a)). In the outer rim, a
light moves through the circular profile with different speed and intensity.
The more data the algorithm has at its disposal, the brighter and faster the
light is.

##  4\. Method

To validate our design and get an initial understanding of how it could be
used to trigger discussions about the Facebook algorithm, we conducted
preliminary studies.

###  4.1. Participants

Our goal is to deploy the FDS in a public space where a variety of
participants can interact. However, for this preliminary evaluation, we
recruited participants through convenience sampling among industrial design
students from the same university, with the inclusion criteria of being weekly
users of Facebook. This ensured that participants would have an understanding
of the Facebook newsfeed and could reflect on their own practices regarding
data sharing. In total 10 participants (male (n=5), female(n=5), age: M=23.6,
SD=1.07) took part in the study.

###  4.2. Materials and setup

The FDS was used during all sessions, and the final settings were recorded
manually. A poster was placed beside the FDS with short descriptions for each
variable. The FDS was set up in a large open space. The interviews were
conducted next to the installation so that participants could look at it.

###  4.3. Procedure

The study consisted of three parts: an introduction, the interaction with the
installation, and a semi-structured interview. The study protocol complied
with the University Ethical Review Board procedures and all data were managed
in accordance with GDPR regulations.

The sessions started with the participants giving consent. They were asked
their age, gender, educational background and for what purpose they used
Facebook. Next, they received a brief introduction. Participants were
explained that the Facebook newsfeed is generated by an algorithm that uses
their data to determine what posts will be most relevant to them. The
introduction concluded with describing the installation scope and interaction
modalities.

After the introduction, participants were invited to change the settings in
the FDS according to their data sharing preferences. At the start, the
_detailed layer_ was hidden so that only the five data categories in the
_core_ were visible. Participants could rotate the _detailed layer_ whenever
they wished, or could decide not to do so. The session ended when the
participant pressed the _upload data_ button. During the interaction, they
were asked to think aloud, and a researcher observed the interactions and took
notes.

The session ended with a semi-structured interview. Three questions were
asked, to investigate: i) how participants felt when they were given the power
to determine what data Facebook collects/uses, ii) which variable triggered
them the most to deactivate and why, iii) whether and how often they would use
a similar feature if it were available on Facebook.

###  4.4. Data analysis

All sessions were audio recorded and transcribed. The interactions with the
buttons were recorded as active-passive describing whether the participant
interacted with the variable and activated-deactivated to notate the final
setting of each variable. The quantitative interaction data were aggregated
and analyzed. The qualitative data of the recordings and observation notes was
analyzed using a thematic analysis (Clarke et al., 2015).

##  5\. Findings

Table 2. An overview of which data categories were kept activated (1) and were deactivated (0) for each participant  | P1 | P2 | P3 | P4 | P5 | P6 | P7 | P8 | P9 | P10 | Total active
---|---|---|---|---|---|---|---|---|---|---|---
Your interactions with your social network | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 1 | 0 | 1 | 7/10
Your behavior | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2/10
Your technological set-up | 1 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 1 | 5/10
Your demographic information | 0 | 0 | 1 | 1 | 0 | 1 | 1 | 1 | 0 | 1 | 6/10
Post-related information | 0 | 1 | 1 | 0 | 1 | 1 | 1 | 0 | 0 | 1 | 6/10

###  5.1. Control level

During the study, participants decided themselves if and when to turn the
outer circle to access the _detailed layer_. The majority of the participants
(8/10) looked at the _detailed layer_ , but only seven participants interacted
with it (in total 24 changes were made to the detailed layer, versus 24 to the
core). P1, P3 and P10 only used the core although P3 did look at the detailed
layer. However, they explained during the interaction that the basic layer
provided them with enough control. The rest of the participants did use the
detailed layer to control their data in more detail and to receive more
information about what each data category entailed. P6 felt that the _detailed
layer_ was needed because the core could hide too much information.

The number of changes made in the detailed layer differed between participants
and data categories. For the data category _Your interactions with your social
network_ most changes were made in the detailed layer (13 out of the 60
possible changes for that data category in the detailed layer were changed)
while for _Your behavior_ only three changes were made (out of 70 possible
changes). Overall, when interacting with the detailed layer, participants
tended to stick to the settings resulting from their choices in the main data
category (i.e., all variables either activated or deactivated). However,
seeing the specific data variables triggered new reflections and resulted
occasionally in (de)activating some of them. For example, P2 deactivated _Your
interactions with Facebook events_ and _Friend’s interactions with a third
party_ since they found them _“annoying”_.

While most participants accessed the detailed layer, they also expressed that
they would expect to be overloaded with information if there was no separation
between the two layers or if the detailed layer would be visible from the
start. Even by splitting the two layers, several participants had an overload
of information when presented with the second detailed layer.

###  5.2. Motivators: privacy, newsfeed personalization, and goals

When making decisions about which variables to turn on or off, different
motivations were used.

Firstly, participants claimed that the reason for deactivating certain data
categories and/or variables was to protect their privacy. _Your behavior_ was
considered by most participants (6/10) the most personal data, and therefore
they wished to deactivate them (see Table 2). They felt that this type of data
was an invasion of their privacy. As P5 and P7 expressed, _“Facebook has no
right to know this information”_ (P5) and _“Creepy, you don’t know how they
use it”_ (P7). Demographic information was also seen as private, but it was
not deactivated as often (40%).

A second motivation for (not) changing settings was the influence of data
sharing on the level of personalization and consequent the quality of the
newsfeed. This reflection mainly resulted in not deactivating some data. While
one participant deactivated all data, the others tried to find a balance
between deactivating data (for privacy reasons) but still having a relevant
and engaging newsfeed. For example, P3 left _Your behavior_ and _Your
demographic information_ active since they expected them to be necessary for a
personalized newsfeed showing relevant posts.

These choices were often linked to the participant’s goal in using Facebook.
Five participants used it for entertainment, four for social interactions and
one for business. Those using it for entertainment were less likely to
deactivate variables (they deactivated between 2-17 detailed variables each).
Social users deactivated between 15-19 detailed variables each, and the
business user deactivated all variables.

###  5.3. Control and engagement

The FDS was designed to both offer users a sense of control over their data
and engage them in a critical reflection. Participants were unanimous in that
the FDS provided them with control, and they all indicated they would use this
option if it were to be implemented on Facebook. Moreover, they also indicated
that it was _“eyeopening”_ (P5) and encouraged self-reflection (P7).

##  6\. Discussion

Based on our preliminary findings, we reflect upon the design of the FDS and
discuss implications for design.

With the FDS, we aimed to explore how the hidden intangible data settings of
Facebook could be made visible and tangible. We opted for a design where data
variables are represented as buttons that can be (de)activated and are divided
into two layers of detail. Our findings showed that using different levels of
details has potential, since it first introduces users to the general types of
data being collected without overwhelming them, and then provides a more
granular control over their settings through the _detailed layer_. At the same
time, our results also show that only a few changes were made to the variables
in the detailed layer. A potential explanation for this is that after setting
the data group, the control in the complex layer becomes superfluous, since
their feeling to each of these variables is the same as their feeling over the
entire data group. However, having made a preliminary choice might also affect
users’ willingness to revise that decision later on, or to reflect on the
differences in the detailed variables. This might suggest that data categories
with more variability in the detailed layer, e.g., _your interactions with
your social network_ , should be split into additional main data categories
that represent them better, if the control happens at the core level. However,
reducing the control to only the data categories might leave participants
feeling uninformed about the specific contents of each category, as
participants appreciated the detailed variables as providing additional
explanation. An alternative would be to give users the option to uncover one
data category at the time, instead of fully rotating the _detailed layer_. To
facilitate this, the rotating mechanism of the FDS should be redesigned.

Presenting the data as tangible and actionable resulted in participants
feeling in control, which was highly appreciated. This confirms earlier
findings about people wanting to be in control of their data (Hsu et al.,
2020; Habib et al., 2022).

While it seems unfeasible to offer social media users tangible control over
their data, this approach proved to be useful to discuss certain aspects of
data collection and control, and to understand users’ needs, without focusing
on where such a control can be found. Findings of this study can help to
better understand users’ wishes around privacy and the desired level of data
awareness and control. For instance, the use of basic and detailed data layers
to provide nuanced control and to inform users seems to be appreciated, and it
could be easily translated into the digital world. While Facebook currently
offers limited control, much more can be done to empower the user and give
them agency in the use of their data.

Future studies might adopt a similar approach to investigate users’ data
control on other social media platforms, as the general design of the FDS
supports multiple platforms. Only the data categories and variables are
Facebook specific.

##  7\. Limitations and Future Work

One of the main limitations of this work is the small and homogenous study
sample. A larger and more diverse sample is needed to find relevant patterns
in the interaction data. A future study is already planned where the
installation will be deployed at a large public event. This will result in a
larger and more diverse set of participants and a greater amount of data being
collected.

Finally, the FDS represents the level of personalization of the resulting
newsfeed - and the consequent expected level of user’s engagement, in an
abstract manner through light, since it is not possible to adjust the actual
Facebook newsfeed of users. This resulted in users having to imagine the
quality of their newsfeed, instead of experiencing it. Depending on the user’s
familiarity with and knowledge of Facebook, their understanding of the effects
of their data selection can be different. Nevertheless, we argue that our
design offers insight in what data users are willing to share and the desired
level of control. Moreover, the FDS increases users’ awareness, and it fosters
reflection on the delicate balance between privacy and personalization or
engagement.

##  8\. Conclusion

Facebook collects a wide range of data from users. However, what data exactly
is collected and how this can be controlled is often not clear. In this work,
we present the Facebook Data Shield, a human-size tangible installation aimed
at inviting users to start a discussion and reflection about data sharing and
data control. We ran a preliminary study with 10 Industrial Design students
who are frequent Facebook users. Our findings show that such a tangible
interactive installation can increase users’ awareness, stimulate reflection,
and provide insight into what participants consider too personal to share and
how they prefer to control their data. Results also hints to a possible
correlation between users’ goal and their choices in data sharing. We will
further investigate the potential of this installation in exploring these
aspects in a future study with a larger and more diverse group of
participants, to build on these preliminary findings.

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