=Paper=
{{Paper
|id=Vol-1283/paper25
|storemode=property
|title=
Social Interaction Based Audience Segregation
for Online Social Networks
|pdfUrl=https://ceur-ws.org/Vol-1283/paper_25.pdf
|volume=Vol-1283
|dblpUrl=https://dblp.org/rec/conf/ecsi/AhmedGTV14
}}
==
Social Interaction Based Audience Segregation
for Online Social Networks ==
https://ceur-ws.org/Vol-1283/paper_25.pdf
Social Interaction Based Audience Segregation
for Online Social Networks
Javed Ahmed1 , Guido Governatori2 , Leendart van der Torre3 and Serena
Villata4
1
CIRSFID, University of Bologna, Italy
2
NICTA QRL Brisbane, Australia
3
University of Luxembourg, Luxembourg
4
INRIA Sophia Antipolis, France
Abstract. Online social networking is the latest craze that has captured
the attention of masses, people use these sites to communicate with their
friends and family. These sites offer attractive means of social interac-
tions and communications, but also raise privacy concerns. This paper
examines user’s abilities to control access to their personal information
posted in online social networks. Online social networks lack common
mechanism used by individuals in their real life to manage their privacy.
The lack of such mechanism significantly affects the level of user control
over their self presentation in online social networks. In this paper, we
present social interaction based audience segregation model for online so-
cial networks. This model mimics real life interaction patterns and makes
online social networks more privacy friendly. Our model uses type, fre-
quency, and initiation factor of social interactions to calculate friendship
strength. The main contribution of the model is that it considers set of
all possible interactions among friends and assigns a numerical weight
to each type of interaction in order to increase or decrease its contribu-
tion in calculation of friendship strength based on its importance in the
development of relationship ties.
1 Introduction
Online social networks (OSNs) have experienced exponential growth in recent
years. OSNs are the top most visited sites on the Internet.5 According to Nielsen,6
OSNs are the fourth most popular activity on the Internet nowadays. The key
breakthrough brought by OSNs is that these sites promote the vision of a human-
centric web, where network of people and their interests become the primary
source of information, which resides entirely on social networking services [1].
Online social networks are one of the most popular fora for self representation
and user interactions. Individuals join social networks to present themselves. In
OSNs users can present themselves by constructing a profile. A profile is a digital
5
Alexa http:/www.alexa.com/topsites
6
Nielsen http://www.nielsen.com/
representation of an OSN user. A Profile contains huge amount of personal in-
formation about the user. Additionally, these users are engaged in various social
interactions with other users. All these activities are recorded on these platforms
which can be easily analyzed, manipulated, systematized, formalized, classified,
and aggregated [2]. This poses a serious privacy threat to OSN users, and that
is the main reason privacy is hotly debated topic in research literature [3] [4] [5]
[6] [7] [8]. There are several dimensions of privacy threats in online social net-
works such as privacy threats related to OSNs users[9], third party applications
[10], and OSN service providers [5]. In this paper, we are addressing the issue of
privacy threats related to OSN users.
Tremendous growth of online social networks resulted in fundamental shift
in status of end users. Individual end users become content managers instead
of just being content consumers. Today, for every single piece of data shared on
OSNs, the uploader must decide which of his friends should be able to access
the data. In OSNs, term ”friend” has become all-encompassing, it has become
increasingly difficult for users to control which friends get to see what personal
information. Several studies on Facebook usage have shown that the average
number of friends per user is approximately 150. Anyone can make a request
to join a user’s friend circle–family members, colleagues, classmates, acquain-
tances, strangers etc. Current literature support the claim that users are willing
to add strangers to their friend circle [9]. However, allowing strangers to join
user’s friend circle can lead to a number of privacy risks [11]. Most of the OSNs
provide users with binary relational ties (e.g., friends or stranger) [12]. This bi-
nary indicator provides only a coarse indication of the nature of the relationship.
In reality human relationships are much more complicated than a single binary
relational tie. There is need for segregation of friends according to the strength of
relational ties. Some of the social networking sites have begun providing friend-
lists feature, in order to help users in organizing a large friend network into
groups. Grouping several hundred friends into different lists, however, can be
a laborious process; on what basis should users construct the friend-lists? And
even if the user were to group friends into lists, are these lists meaningful for
setting privacy policies? To alleviate the burden of constructing meaningful lists
manually, we propose interaction based audience segregation model for online
social networks. The estimation of friendship interaction intensity among OSN
users and its classification based on different level of intensity can be quite use-
ful for identifying privacy threat from individuals added as friends. The social
web is kind of virtual society that exhibits many of the characteristics of real
societies in term of forming relationships and how those relationships are uti-
lized. In real societies, the relationship strength is a crucial factor for individuals
while deciding the boundaries of their privacy. Moreover, this subjective feeling
is quite efficiently utilized by humans to decide various other privacy related
aspects such as what to reveal and whom to reveal.
The main question for this research is how interactions of users determine
tie strength and implement privacy in online social networks. More specifically,
we want to explore whether a users interaction with his friends can be used as
a basis for making data access decision for that users. To answer this question,
we need to understand nature of privacy in online social networks and dynamics
of interactions intensity for OSN users. We break main research question into
three sub questions:
– How to measure privacy risk associated with social graph of OSN users?
– How to construct interaction graph by quantifying users interactions in OSN?
– How to segregate audience on the basis of interaction graph in OSN?
First research question help to quantify the privacy risk attributed to friend
relationship in online social networks. We show that risky friends can reveal
user personal information unintentionally in online social networks. For exam-
ple, Javed and Serena are friends in online social network. Serena is very careful
about the privacy. She adopts a policy that conceals all her friends from public.
On the other hand, Javed, adopts a weaker policy that allows any users to view
his friends. In this case, Serena’s relationship with Javed can still be learned
through Javed. We say that privacy conflict occurs as Serena’s restrictive pol-
icy is violated by Javed’s weaker privacy policy. This shows that the user can
only control one direction of a an inherently bidirectional relationship. Second
research question deals with user’s interaction patterns in online social networks.
We show that users tend to interact mostly with small subset of friends, often
having no interactions with majority of their friends in online social networks.
This cast doubts on the practice of extracting meaningful relationships from so-
cial graphs. We suggest interaction based model for validating user relationships
in online social networks. Third research question deals with audience segrega-
tion. We consider social interactions as currency to estimate friendship strength
and perform audience segregation. Providing users with audience segregation
mechanism would improve the quality of interactions and self presentations. Rest
of the paper is organized as follows. In section 2, we present characterization of
privacy in OSNs. Impact analysis of various social interactions is presented in
section 3. We discuss interaction based audience segregation and present model
to compute the interaction intensity in section 4. Section 5 discuss the related
literature. Finally, we conclude the paper with future research direction.
2 Privacy in Online Social Networks
With emergence of the Web 2.0, a new debate started about the meaning and
value of privacy. According to some researchers privacy has been undermined by
online social networks, even some of them claim that it no longer exist.7 The
concept of privacy is so intricate that there is no universal definition of it. One
of the oldest definitions of privacy is ”the right to be let alone”. Warren and
Brandeis were one of the first authors who recognize the multidimensionality
of privacy concept [13]. Privacy on the web in general revolves mostly around
7
Do Social Networks Brings the End of Privacy?
http://www.scientificamerican.com/article/do-social-networks-bring/
information privacy. The information privacy is an individual’s claim to control
the terms under which personal information is acquired, disclosed or used [14].
With emergence of the social web, where users collaborate and share personal
information, we need to define privacy in fine grained manner to address existing
issues from multiparty perspectives. The definition of privacy should be able to
address the privacy concerns related to OSN users, third party applications, and
OSN service providers.
Palen et al. [15] defined privacy in very precise manner. This definition gives
some idea of various realms in which privacy issue may occur. The authors
classify three boundaries of privacy with which OSNs users are struggling.
Disclosure Boundary It deals with managing private and public scope of up-
loaded information.
Identity Boundary It deals with managing self representation with specific
audience.
Temporal Boundary It deals with managing past actions with future expec-
tations; user behavior may change over time.
The users have a scope in mind when they upload personal information in on-
line social networks. This scope is defined by disclosure, identity, and temporal
boundaries. The privacy is breached when information is moved beyond its in-
tended scope either accidentally or maliciously. Simply a breach can occur when
information is shared with a party for whom it was not intended, it can also
happen when information is abused for different purpose than was intended, or
when information is accessed after its intended lifetime.
Another very comprehensive concept of privacy is given by Patil et al. [16].
The authors present legal, social and technical perspectives from which the no-
tion of privacy is commonly described and analyzed.
Normative from this perspective, privacy is an ethical concept. Privacy is
viewed as right of individual and thus as a matter of freedom.
Social from this perspective, privacy has psychological and culture roots. Pri-
vacy is socially constructed based on the behavior and interactions of indi-
viduals as they conduct their day-to-day affairs.
Technical the technical perspective views privacy in terms of the functional
characteristics of digital systems. Privacy is thus treated as the desire for
selective and adequate control over data and information.
Note that three perspectives of privacy are not mutually exclusive but interde-
pendent. Normative focuses on laws and policies aiming to protect the individual
from cooperations, governments and other individuals. European data protection
framework is an example which promotes informational self determination em-
phasizing an individual’s right to control the collection and use of personal data.
Technical dimension of privacy aims at translating norms and regulations into
technical specifications. The Platform for Privacy Preferences Project (P3P) is
one of the examples for enhancing the individual’s ability to control information
disclosure by technical means. Social dimension of privacy focuses on managing
social relationships and boundaries between public and private life. Privacy is
breached if personal information is available outside its intended context.
Netter et al. [17] breaks the concept of privacy into a set of characteristics
that aim at analyzing the OSN privacy from multiparty perspectives. The pri-
vacy risks associated with online social networks are mainly from OSNs users,
third party applications, or OSNs service providers. Each characteristic address
privacy risk related to one of these stakeholders.
Audience Segregation This characteristic describes that each individual per-
forms multiple and possibly conflicting roles in everyday life and it needs to
segregate the audience for each role, in way that people from one audience
cannot witness a role performance that is intended for another audience. In
current online social networks almost all friends are treated equally, As a
result, privacy is threatened because a large audience might have access to
personal information. This characteristic deals mainly with social web users.
Data Sovereignty It describes to what extent an individual is able to control
the processing of its personal data. In case of online social networks personal
data is available in structured manner. It can easily be copied, linked, aggre-
gated, and transferred. This characteristic deals mainly with OSNs service
providers and third party applications.
Data Transience This characteristic revolves around the loss of personal in-
formation over time. In computer mediated communication permanency of
personal information poses great challenge to privacy, whereas data tran-
sience can be considered as typical characteristic of real world communica-
tion. This characteristic deals mainly with OSNs service providers and third
party applications.
Protection against profiling It describes an individual’s ability to prevent
an adversary from collecting, aggregating and link personal data in order
to create a digital dossier. The current landscape of online social networks
poses this threat at large scale. This characteristic deals with OSNs service
providers and third party applications.
Privacy Awareness It describes that an individual’s awareness for privacy
risks is a prerequisite for privacy preserving behavior. The characteristic
deals with all stakeholders at social level.
Transparency It describes transparency of processing and dissemination prac-
tices. This characteristic deals mainly with OSNs service providers and third
party applications.
Enforcement It describes an individual’s means to bring privacy preference
into force. This characteristic deals with OSNs service providers at legal
level.
This paper focuses only on audience segregation characteristic to preserve
three boundaries of privacy defined by Palen et al. The audience segregation is
main characteristic that deals with OSNs users. A comprehensive solution to ad-
dress the privacy risks associated with OSNs users can not be developed without
taking into consideration importance of audience segregation. We consider social
interactions as currency to estimate friendship strength and perform audience
segregation. In this paper, we develop a mathematical model for this purpose.
The issues related to third party applications and OSNs service providers are
not in the scope of this paper.
3 Social Interactions in Online Social Networks
Online social networks are popular for interaction, communication and collabo-
ration between friends. The properties of social interactions have been studied
by many researchers. Facebook data team recently showed that a typical Face-
book user communicates with a small subset of their entire friends network, but
maintains relationships with a group that is two times larger.8 Wilson et al.[18]
propose the interaction graph, a model for representing user relationships based
on user interactions. The authors show that interaction activity on Facebook
is significantly skewed towards a small portion of each user’s social links. This
finding casts doubt on the assumption that all social links imply equally mean-
ingful friend relationships. Burke et al.[19] study the role of user interactions on
Facebook. The authors quantify usage of visible actions (such as wall posts and
comments) and also silent actions (such as profile visits). They show that, differ-
ent from high levels of content consumption, high levels of direct communication
among users is usually associated with greater feelings of emotional support from
close friends. Jiang et al. [20] show that latent (or silent) interactions are much
more prevalent and frequent than visible interactions. Gao et al. [21] attempt to
characterize and detect malicious forms of interactions in online social networks.
In this section our main goal is to identify impact of various types of interac-
tions provided by online social networks. OSNs provide a variety of social inter-
actions. These interactions can play important role to estimate friend strength.
There are several interaction factors that can be considered to identify their
impact in developing relationship ties. These factors include type of interaction,
frequency of interaction, and initiation of interaction. The type of interaction
is quite important factor in order to estimate friendship strength. Online social
networks provide numerous types of interactions such as messages, wall posting,
comments, tagging, chatting etc, some of these interactions are real time and
the others are non-real time. An individual chooses an interaction type on ba-
sis of relationship with target audience. Hence, the interaction type defines the
intimacy, openness, sensitivity as well as strength of relationship between com-
municating parties. The interaction frequency refers to total occurrences of each
type of interaction between an individual and his friends within certain period
of time. This factor helps to understand that users are willing to interact with
each other over a period of time. The interaction initiation factor is very impor-
tant to understand strength of relationship. We further categorize this aspect in
following manner.
User Initiated Interactions When the user initiate interaction with his friend
it is termed as user initiated interactions. These interactions have more
8
Maintained Relationships on Facebook http://overstated.net/2009/03/09/maintained-
relationships-on-facebook
weight in developing relationship strength because the user is willing to com-
municate and collaborate with his friend.
Friend Initiated Interactions When an individual friend initiate interaction
with the user it is termed as friend initiated interactions. These interactions
have less weight in developing relationship strength because willingness of
communication and collaboration is coming from the friend.
As discussed earlier, the selection of interaction type gives an indication of na-
ture of relationship among users. Some the interaction types are preferred to
communicate with close friends, whereas the others to interact with ordinary
friends. Hence, all interaction types cannot be given similar weight in estimation
of relationship strength. Each interaction type is given a numerical weight in or-
der to increase or decrease its contribution in development relationship strength.
We consider social interaction as a very strong indicator for friend segregation.
Our model uses type, frequency, and interaction initiation factor to calculate in-
teraction intensity that can be useful in audience segregation. The term audience
segregation is explained in following section before presentation of our audience
segregation model.
4 Audience Segregation in Online Social Networks
Social web users privacy issues can be addressed by providing users with tools
that help them manage their personal content in more privacy friendly man-
ner. In everyday life individuals have control over what kind of information is
presented to different audiences. Mirroring or mimicking this real life strategy,
we propose interaction based audience segregation model for online social net-
works. The term ”audience segregation” was coined by Goffman [22] as part of
a perspective on the ways in which identities are constructed and expressed in
interactions between human beings in everyday context. According to Goffman,
whenever individuals engage in interactions with others they perform roles, with
which they hope to present a favorable image of themselves. One of the key ele-
ments of Goffman’s perspective on identity is the fact that individuals attempt
to present self-images that both are consistent and coherent. To accomplish this,
performers engage in audience segregation. While Goffman’s idea of audience
segregation didn’t originally relate directly to privacy, it is easy to see that au-
dience segregation and privacy are, in fact, closely linked. Another quite similar
conclusion is drawn by Nissenbaum [23] that is privacy revolves around contex-
tual integrity. According to this view privacy revolves around person’s ability to
keep audience separate and to compartmentalize his social life.
Current online social networks don’t provide users fine grained mechanism to
separate and manage various audiences. Many social networks sites only provide
their users the option to collect one list of contacts, called ”friends”. Some of
the social networks offer functionality of creating separate lists which require
user’s time and efforts. Studies show that managing different lists is a burden
to many users and rarely applied [24]. Given the fact that Facebook users, for
instance, on average have 150 friends, this necessarily conflates different contexts.
Providing OSN users a mechanism which mimic real life interaction patterns to
larger extent would improve self presentation, and reduce privacy risks. It will
also enable users to avoid social convergence, and provide users opportunity to
present different sides of themselves to different audiences.
4.1 Social Interaction Based Audience Segregation Model
In online social networks individuals are connected with diverse audience such as
friends, family members, distant relatives, colleagues, old schoolmate etc. Some
of them are intimately known to user, whereas others are distant, loose, or even
unknown connections. This is main reason users want to make distinctions be-
tween the types of information they want to share with these different categories
of connections, and give different connections access to different content. So-
cial interaction based audience segregation can play vital role to achieve this
objective.
Fig. 1. An individual user’s social cirlce in online social network
Social interaction based audience segregation model considers an individual
user u has n number of friends, f1 , f2 , f3 , · · · , fn . The user and his friends can
interact with each other by k type of different interactions [t1 , t2 , · · · , tk ], either
initiated by user or his/her friend. Each type of interaction ti is assigned a weight
wi0 on the basis of its importance in developing friendship strength. Following
vector w0 represents the weights of all interactions:
w0 = [w10 , w20 , w30 , ..., wk0 ]
and w0 is normalized as:
1 0 0 0
w= [w , w , w , ..., wk0 ]
K 1 2 3
Pk 0
where K = i=0 wi .
The frequency of all k type interactions is also considered separately for user
initiated and friend initiated interactions on basis of their repeated occurrences
in communication. Let ai be the frequency of interaction ti and let Fu,j be the
vector representing the frequency of all k type of interactions initiated by the
user u to user j given as:
Fu,j = [a1 , a2 , a3 , ..., ak ]
where 1 ≤ j ≤ n. Similarly, Fj,u represents the frequency of all type of interac-
tions between user u and j initiated by j. The interaction intensity is calculated
by multiplication of each type of interactions frequency ai by its respective weight
wi and accumulation of all such interaction types separately for user initiated
interactions and friend initiated interactions. That is the interaction intensity of
user u with his friend j is computed as:
k
X
I(u,j) = Fu,j (i) ∗ wi (1)
i=0
where Fu,j (i) is the interaction frequency of interaction type ti and wi is nor-
malized weight as described above. Similarly, interaction intensity I(j, u) of user
j with u is computed.
k
X
I(j,u) = Fj,u (i) ∗ wi (2)
i=0
Finally, user and friend initiated interactions are multiplied by their respective
weights and accumulated to generate interaction intensity value of user u with
his friend j
Tj = α ∗ I(u,j) + (1 − α) ∗ I(j,u) (3)
Where 0 ≤ α ≤ 1 and α, 1 − α are respective weight for user and friend initiated
interactions.
There are three main contributions of this model. First, it considers all possi-
ble of set interactions among friends. Secondly, the model considers the direction
of interaction either from user to friend or vice versa. Finally, it assigns numeri-
cal weight to all interaction types based on their importance in the development
of friendship strength.
5 Related Literature
One of the research studies closely related to our work is done by Lerone et al.
[25]. The author has introduced interaction count based approach to determine
relationship strength. The author simply takes into consideration three types
of interactions and count them in order to calculate relationship strength. The
interaction intensity model by Lerone et al. [25] do not differentiate interactions
on the basis of initiative. Hence, it is possible that a malicious user intentionally
spam interactions to get access to sensitive profile data. Our model takes into
consideration this issue and resolves it by assign more weight to interactions
initiated by user himself. Our model has another advantage over [25] that it
considers all interaction types offered by online social networks.
Waqar et al. [26] extended work of [25] by applying data mining model to cal-
culate relationship strength, Whereas, the model is not validated on real OSNs
data. The author also conducted online survey to analyze Facebook user’s inter-
action behavior with their friends. Xiang et al. [12] proposed a model to infer
relationship strength based on profile similarity and interaction activity. Lizi
et al. [27] proposed interaction ranking based trustworthy friend recommenda-
tion model. Another interesting work by the author [28] proposed trust ranking
based recommendation model for suggesting the most trustworthy community
members. The author investigated four new interaction attributes that influence
trust in virtual communities. A recent work related to friend recommendation
is done by Zhao et al.[29]. The author proposed scalable and explainable friend
recommendation model for social network systems.
The majority of online social networks offer second degree access which means
a friend of a friend is able to access the user’s personal information. According to
Cuneyt et al. [11] friends can be source of privacy risk because this relationship
always implies the release of some personal information not only to friends, but
also to friends of a friend, which are strangers for the users. Another interesting
fact demonstrated by Frank et al. [30] that more users are willing to divulge
personal details to an adversary if there is a mutual friend connected to the
adversary and the user. Christo et al. [18] shows that users tend to interact
mostly with small subset of friends, often having no interactions with up to
50 percent of their friends. The author suggests a model for representing user
relationships based on user interactions. These works supports our idea that all
friends should not be give equal access to user personal information, but access
to personal information should be administrated based on interaction frequency
among users.
6 Conclusion and Future Work
In this paper, We proposed social interaction based audience segregation model
which mimic real life interaction patterns to larger extent. We also identified
the impact of various social interactions available to users in online social net-
works. There are three main contributions of our model. First of all, it consider
all possible of set interactions among friends. Secondly, the model considers the
direction of interaction either from user to friend or vice versa. Finally, all in-
teraction types are assigned a numerical weight in order to increase or decrease
its contribution in interaction intensity calculation based on its importance in
the development of relationship ties. Our future plans include implementation
of this model.
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