=Paper=
{{Paper
|id=Vol-1564/paper14
|storemode=property
|title=Self-disclosure in Social Media: An Opportunity for Self-Adaptive Systems
|pdfUrl=https://ceur-ws.org/Vol-1564/paper14.pdf
|volume=Vol-1564
|authors=Nicolas E. Diaz Ferreyra,Johanna Schäwel
|dblpUrl=https://dblp.org/rec/conf/refsq/FerreyraS16
}}
==Self-disclosure in Social Media: An Opportunity for Self-Adaptive Systems==
https://ceur-ws.org/Vol-1564/paper14.pdf
Self-disclosure in Social Media: An opportunity
for Self-Adaptive Systems
Nicolás Emilio Dı́az Ferreyra and Johanna Schäwel
University of Duisburg Essen, Germany
{nicolas.diaz-ferreyra,johanna.schaewel}@uni-due.de
https://www.ucsm.info/
Abstract. Users of Social Network Sites (SNSs) spend considerable
amounts of hours per day exchanging (consuming or sharing) informa-
tion and using services provided by such platforms. However, nothing
comes for free. SNSs survive at the expense of the information that users’
upload to their profiles, and the knowledge derived from their on-line be-
havior. Discovering hidden knowledge in social networks is a centerpiece
in many personalized on-line services and ad-targeting techniques, and
helps to make a SNS profitable. However, users seem not to be aware
of this common practice and keep sharing content compulsively. Never-
theless, self-disclosure and over-exposition can have severe consequences
and can put users’ integrity into risk. In order to develop better infor-
mation control and awareness systems, we believe that it is important
to take into account the users’ on-line habits and behavior. In this work
we introduce an initial assessment of the different factors that contribute
to self-disclosure in Social Media, and discuss the elements that a self-
adaptive solution should consider to address this issue.
Keywords: social-media, self-disclosure, awareness, self-adaptive sys-
tems
1 Introduction
Social Media has set new standards for our interpersonal relations, and has ac-
celerated the dynamics of our lives. Many users are bridged through SNSs, and
new sub-communities are built everyday based on common interests, likes or
even mottos. The inhabitants of these virtual communities are spending con-
siderable amounts of time exchanging (consuming or sharing) information, and
using services provided by the SNSs. However, none of this is for free. SNSs
survive at the expense of the information that users’ place in their profiles, and
the behavior they exhibit while using the different services provided by these
platforms.
Discovering hidden knowledge in social networks is a centerpiece in many
personalized on-line services and ad-targeting techniques, and is basically what
makes a SNS profitable [18]. However, many of the content that is uploaded to
social platforms (text, image, video, location) contain a high level of private and
Copyright © 2016 for this paper by its authors. Copying permitted for private
and academic purposes.
2 Nicolás Emilio Dı́az Ferreyra and Johanna Schäwel
sensitive information. The reality is that Social Media users compulsively share
content without caring about the consequences. Moreover, their behavior off-
line (in the real world) differs highly from their on-line behavior (inside a SNS)
[3][14]. If we add to this that users are careless when adding new contacts to
their network, there is a high chance of having potentially dangerous individuals
accessing this information.
Although existing privacy-preserving mechanisms have been developed and
improved over the years, they are still not helping users in distinguishing a self-
exposition behavior that might put them into risk. It is very hard for a regular
user to keep track of everything that he or she has shared through its “on-
line life”. Moreover, once the content has been shifted to the Internet, the user
has no control over it anymore. This situation demands new mechanisms for
tracking the sensitive information that a user has already shared, and the degree
of sensitiveness that new information might have. Thus, users of SNSs can make
a wiser decision before sharing content, and have a better vision of what they
have shared (and would like to un-share) in the past.
In this work we present an analysis of the “self-disclosure” problem in Social
Media and provide insights towards a self-adaptive solution. Different dimensions
of the problem like the users’ behavior and information sensitiveness are studied
from an inter-disciplinary perspective. Furthermore, initial guidelines for a self-
adaptive approach based on the MAPE-K model by IBM [9] are here introduced.
In the following section the fundamental bases and concepts involved in our
proposal are initially introduced (Section 2). Section 3 covers the different aspects
of the self-disclosure issue including: the diversity of information in SNSs, the
so-called “privacy paradox”, information sensitiveness, and an adapted version
of the MAPE-K model. Next, Section 4 discusses alternative existing solutions,
and finally Section 5 presents our conclusions and related future work.
2 Theoretical Background
This section introduces the fundamental concepts that form the bases of our
proposal. Here, Autonomic Systems and run-time self-adaptation concepts are
presented and analyzed for further application in a Social Media scenario.
In order to raise awareness of self-disclosure among the users of SNSs we
propose to develop an Autonomic Computing vision of this issue. The goal of
Autonomic Computing is to design and develop distributed and service-oriented
systems that can easily adapt to changes that affect the system administration
and service delivery, while reducing some of the complexities associated with
the management of such systems [10]. Considering the user’s content-sharing
behavior in SNSs as the managed element of our autonomic system, will al-
low us to apply the concepts of Autonomic Computing into a Social Media
domain. MAPE-K (Monitor, Analyze, Plan, Execute, and Knowledge) is a ref-
erence model for control loops used in Autonomic Computing with the objective
of supporting the concepts of self-management, specifically: self-configuration,
self-optimization, self-healing, and self-protection [9][10]. Fig. 1 shows the ele-
Self-disclosure in Social Media: An opportunity for Self-Adaptive Systems 3
Fig. 1. Autonomic Computing and MAPE-K Loop [9]
ments of an Autonomic System: the control loop activities, sensor and effector
interfaces, and the managed system.
The Monitor component provides the mechanisms to observe through Sen-
sors different events or changes that take place in the System (managed element).
It also filters and aggregates the data, and reports details or metrics [9]. The
Analyze component provides the means to correlate and model the reported
attributes or measurements. It is able to interpret the environment, to handle
complex situations, and predict future scenarios. Plan provides the means to
construct the set of actions required to achieve a certain goal or objective in
response to certain events. On the other hand, Execute offers the elements to
release the actions involved in a particular plan (e.g. to control the system by
means of Effectors that modify the managed element)[10]. Additionally, a com-
mon Knowledge Base acts as the central part of the control loop, and is shared
by the activities to store and access collected and analyzed data.
The MAPE-K model is used as an architectural reference in cases where
a feedback loop is a distinctive characteristic of the system being built. Such
is the case of [7], where a MAPE-K loop is used for run-time monitoring of
trustworthiness properties in a socio-technical system in order to achieve trust
goals. In a Social Media context like ours, the users’ accounts are the elements we
want to monitor since they contain the resources and services consumed by them.
In line with this, the actions executed over the accounts (managed elements) are
directed to aware the users about an over-exposition behavior.
3 A self-adaptive approach for addressing Self-disclosure
It is necessary to conduct an analysis of several factors that contribute to the
problem and the solution of self-disclosure in SNSs. In this section we will go
through the different types of information that can be found in a SNS (partic-
ularly on Facebook), and we will provide some insights for further sensitiveness
classification. We will also discuss the influence of users’ on-line behavior and
risk aversion. At the end of this section, an approach for addressing this issue
based on the MAPE-K model will be introduced.
4 Nicolás Emilio Dı́az Ferreyra and Johanna Schäwel
3.1 Diversity of information in SNSs
SNSs are a rich source of the most varied kinds of information. However, users
do not realize the importance that this information they “voluntarily” deposit in
these sites has. From a high level inspection, normally one can find in a Facebook
profile the following information: list of friends, personal information (e.g. first
name, surname and profession), wall posts (public messages from other users),
messages, photos, and notes [11].
However, if one takes a closer look to the now improved “Facebook Security
Centre”, it is now possible for users to download a copy of the information that
Facebook stores about them. Surprisingly, the list is way bigger than the one
mentioned before, and includes (among other information)1 :
– Ads Clicked: Dates, times and titles of ads clicked by the user.
– Ad Topics: A list of topics that the user is targeted against based on its likes,
interests and other data included in its Timeline.
– Check-ins: Places where the user has checked-in to.
– Facial recognition data: A unique number based on a comparison of the
photos the user has been targeted in.
– IP Address.
– Log-ins and Log-outs.
– Deleted friends.
Clearly, users do not submit many of this information voluntarily to Facebook.
For someone familiar within SNSs and their privacy practices, it is not surprising
that Facebook (like many other SNSs) keeps all these records in their servers.
However, for many users (newcomers or advanced) this situation remains unclear,
even when the privacy settings of their Facebook accounts are public by default
[16].
3.2 Self-disclosure and the Privacy Paradox
Exposing personal information to other persons is referred as individuals’ self-
disclosure. Self-disclosure in on-line contexts like Social Media is, at least to a
certain extent, the precondition for a functional social network [12]. In other
words, users’ contributions are necessary for the survival of SNSs. Without the
users’ shared content (such as posted information and tagged photos), SNSs
would lack of diversity and fail on being interesting enough for the users to
engage with.
Self-disclosure is frequent among the users of SNSs. Furthermore, users seem
careless when providing sensitive information through SNSs. However, they con-
sider privacy protection an important issue that must be addressed. This phe-
nomenon of contradiction has been referred as the “privacy paradox” [2][14].
Despite the studies that reveal evidence of this thesis [8], complementary re-
search judges the non-holistic approach of the applied methods in these findings
1
https://www.facebook.com/help/405183566203254/ (last access: 22/01/2016)
Self-disclosure in Social Media: An opportunity for Self-Adaptive Systems 5
[5]. Nevertheless, we believe that, whether the privacy paradox exists or not,
users’ on-line behavior has to be empowered with a recommendation system
that can assist them in the identification of potentially sensitive information in
real time.
3.3 Defining sensitiveness in Social Media
Several gaps and dilemmas have been identified when trying to define what sen-
sitive information is [15]. Moreover, it is a matter of discussion in the legislation
of many countries and politico-economic unions [1][6]. The European Parliament
for instance has defined some “personal data” categories (e.g. racial or ethnic
origin) that are protected against public disclosure. It also makes use of the
term “sensitive information”, however it does not define it [6]. The Canadian
Personal Information Protection and Electronic Documents Act 2000 state that:
“Although some information (e.g. medical records) is almost always considered
to be sensitive, any information can be sensitive depending on the context”
[6]. This last one is an interesting approach towards the definition of “sensitive
information” since it highlights the influence that the context has over it. Nev-
ertheless, this reveals the need for considering and understanding the context
where the information is placed.
A SNS is a complex environment where multiple factors converge and (in
many cases) are the ones that define the rules of interaction and contributions
for the users. For instance, a post that can look trivial on Facebook can be totally
inappropriate in another SNS like LinkedIn (e.g. a photo of you in a party might
not look very professional). In other words, here the context affects the degree
of sensitiveness of the content. In this case the targeted audience of the SNS is
a conditioning dimension of the context.
3.4 Towards a MAPE-K-based approach
An awareness system like the one proposed in this work has a self-adaptive
nature. Its purpose is to perform a constant monitoring over the user sharing
activities and notify when a self-disclosure behavior is detected. This notification
can be seen as an interaction with the user, where he or she will have the last
word and control over the sharing act. In other words, the user should have
the chance to accept or reject the recommendation of not to share potentially
sensitive information. This sequence of detection-notification-acceptance defines
a feedback loop between the user and the awareness system.
As we have discussed in the previous sections, classifying information into
categories of sensitiveness does not have a straightforward solution. However,
as several legislations agree, it is possible to build categories of “personal” or
“sensitive” data. Since the user’s perception is also a determinant on the final
classification, it seems logical to perform a classification of the users based on
their interpretation of particular pieces of information. Then, by combining these
two approaches together with attributes of the SNS (e.g. the targeted audience
6 Nicolás Emilio Dı́az Ferreyra and Johanna Schäwel
Fig. 2. Elements for the analysis of sensitive information
Fig. 3. Adapted MAPE-K Loop
and activity levels of the users), a better classification of the information can be
performed (Fig. 2).
In Fig. 3 an adapted version of the MAPE-K loop is described. In this case,
the Managed element corresponds to the representation of the user in a SNS, this
is, the user’s account. In this approach, the Monitor is sensing the activity of
the user and responses when an information-sharing event takes place. As was
previously mentioned, the goal of this system is to provide recommendations
to the user when it attempts to publish content of sensitive nature. Therefore,
what the Analysis unit should do is to analyze the information collected by
the Monitor’s sensors and classify it into sensitive or not sensitive. Here, the
Knowledge base has a main role because it contains all what has been learned
about sensitiveness and its influencing factors. After this is done, the Plan will
elaborate a recommendation for the user, and then the Execute module will
proceed to deliver it to the user.
A privacy protection recommendation system must be able to adapt on users
individual self-disclosing behavior without destructing the interactive nature of
SNSs. Our approach takes this statement into account by asking the user “do
you really want to share this?” instead of forbidding it to continue. By this, the
autonomy of the user is ensured and its final decision contributes to the feedback
loop of the system.
Nevertheless, self-adaptation brings into account a fundamental reasoning
problem: decide which is the best course of action to follow based on the perceived
Self-disclosure in Social Media: An opportunity for Self-Adaptive Systems 7
stimuli from the environment. In Artificial Intelligence this type of reasoning is
usually called planning, where the condition to achieve is called goal and the
sequence of actions that will make the goal true is called a plan [4]. Because
such Autonomic Element must exhibit an intelligent behavior, planning is a
central discipline in our study. According to [4] Situation Calculus based on
First Order Logic (FOL) is an adequate candidate to support planning due to
its appropriateness for representing dynamically changing worlds. Furthermore,
it provides a framework for defining a set of actions, states and changes in the
environment, and entails a reasoning mechanism to make inferences. Adapting
Situation Calculus to our problem domain is one of the major challenges of our
research.
4 Discussion
Many privacy breaches in SNSs have been identified and addressed through
different types of privacy-preserving software architectures (e.g. P2P). Many
researchers advocate particularly for decentralized architecture schemas unlike
predominant centralized approaches[13]. Some of the benefits of this are end-
to-end encryption, hidden activity from 3rd parties, and hidden social graph
among others. Although decentralized schemas improve privacy protection for
the users, they demand a major development effort and cannot provide the same
functionality as centralized ones [13]. This is one of the major reasons why users
are reluctant to migrate to privacy-preserving SNSs [13].
While these approaches focus mainly on the architectural elements that a
privacy-preserving SNS must have, the solution presented in this work propose
to contribute to privacy on the application level. This is, even with a centralized
and non-privacy-preserving SNS architecture, it should be possible to arise user’s
awareness and hence prevent extensive self-disclosure. In this way, empowered
users will take better control over their on-line acts and in consequence over
their private data. This can be achieved since SNSs like Facebook provide APIs
and extension points for including 3rd party applications, which would allow us
to integrate our solution without forcing users to change into another SNS.
5 Conclusions
In off-line situations people’s communication about sensitive topics take place be-
hind closed doors; whereas in SNSs users do not seem to lock their metaphorical
doors when they address sensitive topics [3]. Moreover, the range of the audience
that can access to personal information is perceived differently in on-line and
off-line contexts. In an off-line context a person usually recognizes his or her
audience, whereas in on-line contexts people are not able to sufficiently estimate
the size of such audiences [17]. Due to the difficulty in estimating the number of
receivers of what in many cases can be sensitive information, it is important to
support the users in analyzing the sensitivities of their contributions.
8 Nicolás Emilio Dı́az Ferreyra and Johanna Schäwel
It is true that some users are not much concerned about the consequences that
self-disclosure in SNSs could bring to them, and are not willing to modify their
behavior. However, this does not neglect the fact that it is necessary to support
and empower them through better control and awareness systems. Instead, this
raises the necessity of developing instruments that take into consideration users’
distinctive characteristics that make them more or less adverse to the risks of
over-exposition.
Self-disclosure and information sensitiveness analysis propose a number of
challenges and opportunities for self-adaptive systems. This work has analyzed
and summarized the requirements that a self-adaptive solution must cover for
addressing self-disclosure in SNSs. Now this vision has to be put into practice
and undoubtedly new challenges and research questions will arise. This is matter
of our future work, together with an analysis of acceptance of such awareness
system among the social network’s community.
Acknowledgments. This work was supported by the Deutsche Forschungsge-
meinschaft (DFG) under grant No. GRK 2167, Research Training Group ”User-
Centred Social Media”.
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