=Paper=
{{Paper
|id=Vol-2037/paper26
|storemode=property
|title=A Model for Handling Multiple Social Networks, its Implementation
|pdfUrl=https://ceur-ws.org/Vol-2037/paper_26.pdf
|volume=Vol-2037
|authors=Francesco Buccafurri,Gianluca Lax,Serena Nicolazzo,Antonino Nocera
|dblpUrl=https://dblp.org/rec/conf/sebd/BuccafurriLNN17
}}
==A Model for Handling Multiple Social Networks, its Implementation==
https://ceur-ws.org/Vol-2037/paper_26.pdf
A Model for Handling Multiple Social Networks
and its Implementation
(discussion paper? )
Francesco Buccafurri, Gianluca Lax, Serena Nicolazzo, and Antonino Nocera
DIIES, University Mediterranea of Reggio Calabria
Via Graziella, Località Feo di Vito
89122 Reggio Calabria, Italy
E-mail:{bucca,lax,s.nicolazzo,a.nocera}@unirc.it
Abstract. Nowadays, users join several on-line social networks (OSNs)
so that design and development applications able to work on multi-
ple OSNs is a challenging issue. However, OSNs present relevant differ-
ences from both the adopted terminology (similar concepts have different
names) and the supported technology (for example, in the APIs provided
for data extraction). Consequently, the heterogeneity of OSNs does not
allow the design of applications with suitable abstraction with respect
to the specific OSNs processed. In this paper, we define a model aimed
at generalizing concepts, actions and relationships of existing social net-
works, which can be exploited as a middleware to implement applications
working on multiple social networks.
Keywords Multiple Social Networks, Facebook, Twitter, API.
1 Introduction
Over the past decade, online social networks have became part of people’s live.
Nowadays, most people have a profile in one or more online social networks like
Facebook, Twitter, Linkedin, MySpace, in which they spend a lot of time. This
is recognized as an important phenomenon from a social and economic point
of view, and, thus, in design and development processes of (Web) applications.
Indeed, often applications should be based on behaviors of a community, or take
advantage from these, so that modern Web applications should be social by de-
fault. In many cases, both personal information and social interactions coming
from social network profiles can be part of innovative solutions. Among these,
social Web applications are the most significant example, in which both people’s
identities and contents they produced are involved in the business process and
data are mostly owned by users, strongly interlinked and inherently polymorphic
?
This is a short version of the paper titled “A model to support design and devel-
opment of multiple-social-network applications” [3], which appears in Information
Science journal.
[1]. Indeed, despite the conceptual uniformity of the social-network universe, in
terms of structure, basic mechanisms, main features, etc., each social network
has in practice its own terms, resources, actions: for example, connected people
in Facebook are friends, whereas they are followers or followings in Twitter.
Consequently, there is the need of delaying the binding between abstract con-
cepts and concrete API calls, when applications operate across multiple social
networks: the abstract request of finding connected people is implemented dif-
ferently in Facebook and Twitter (this argument is discussed in Section 2). This
is a strong handicap for the design and implementation of applications enabling
internetworking functions among multiple social networks, and, then, for the
achievement of the above goal. As a matter of fact, little exists in terms of
models and languages to support social-network-based programming in large,
according to software engineering principles of genericity and polymorphism.
On the other hand, the power of the social-network substrate can be fully ex-
ploited only if we move from a single-social-network to a multiple-social-network
perspective, still keeping the user-centered vision, so that the above issue be-
comes crucial. The recent literature has highlighted that the aforementioned
multiple-social-network perspective opens a lot of new problems in terms of
analysis [12] but also new opportunities from the application point of view [14,
8, 15, 18]. Even though each single social network is an extraordinary source of
knowledge, the information power of the social-network Web can be considerable
increased if we see it as a huge global social network, composed of autonomous
components with strong correlation and interaction. Thus, social-network-based
programming should work at this abstraction level.
In this paper, we do an important step to cover the gap highlighted above,
by defining and implementing a model aimed at generalizing concepts, actions
and relationships of existing social networks. This paper is organized as follows.
Section 2 introduces the characteristics of the multiple-social-network scenario
that we model. We give a formal definition of the graph-based conceptual model
in Section 3. To validate our approach, in Section 4, we show how our model has
been profitably applied to two very relevant applications in the context of social
network analysis. Finally, our conclusions are summarized in Section 5.
2 Design specification
One of the motivation of this study is the strong heterogeneity in the repre-
sentation of concepts among different social networks. For instance, contacts
are represented by friends in Facebook and the relationship is symmetric, while
they are represented by followers and followings in Twitter and the correspond-
ing relationship is not symmetric. Again, the concept of appreciation becomes
+1 in Google+ and endorsement in about.me. Importantly, similar concepts
can mapped to each other but they have in general different features. Thus, an
integration step is necessary for our purpose. In this section, we prepare this inte-
gration step by grouping the main technical entities into a number of categories
to which the formal model presented in the next section maps. In particular, we
aim at modeling the following entities.
Profile. Social network sites are built around user profiles, a form of individ-
ual (or group) homepage, which provides a description of each registered user.
For example, in Twitter, at the moment of registration, a user can create his
profile typing his name, username, password and email address in the registra-
tion form. After, he can upload a profile picture and start following other people.
Moreover he can complete his profile adding a short biography, a position (the
place where he lives) and a link to his website or to one of his accounts on
other social networks. Another social network, about.me, is characterized by its
one-page user profiles, each with a large background image and short biography.
At the moment of registration a user has to fill the suitable form with his user-
name, email, password for the site and at a second step short biography, a short
description, a profile image and a background image.
Links to external social networks. An important feature provided by all the
social networks considered in this paper is the possibility for a user to add in
his profile a link toward one of his accounts in another social site or external
website. This feature is typically enabled during the creation of the user profile.
It is of particular interest in this paper because it encodes the basic information
allowing the possibility of seeing different social sites as members of a Multiple-
Social-Network environment.
Friendship. After creating a profile, participants are asked to invite their
friends to the site or to look at others’ profiles and add those people to their list
of friends. In Twitter, a user can follow another user, becoming his follower.
Only if this user follows him back the relationship is bidirectional. Differently
from Twitter, Facebook requires approval for two people to be linked as friends.
When someone links another as a friend, the recipient receives a message asking
for confirmation. Indeed, Facebook friendship is bidirectional, hence, once a user
accepts a friendship request of another user they become mutual friends.
Resources. A Social network resource is a Web asset such as a status update,
a photo, a web link or a video created and loaded by a user in his profile. As
for LinkedIn, a user can add a resource like a new item or a new file in his
profile. He can also embed a comment, a photo, a web link or a video in a new
status update. Also skills representing specific technical expertise can be seen as
a typology of resource, which are posted by users to describe their ability. This
way, his connections can like it, comment it and share it on their “wall”.
Actions on resources. So far, we stated that in addition to the content that
members add when they create their own profiles, social network sites typically
provide the possibility to share resources. After a resource is published by a user,
several actions can be performed on this resource: other users can appreciate it,
or re-share it, or it can be associated with a user through a mention on his
profile. Hereafter, we list the main actions a user can do on a resource according
to the different social networks analyzed in this paper.
Once a user write a tweet in Twitter, it will appear on the homepage of all
his followers, who can reply to it, make it one of their favourites or retweet it
(that is, forwarding it again on their own timeline). A tweet can contain also
a user mention. It can be done using the symbol @ followed by the referenced
username. To categorize tweets by keyword, people use the hashtag symbol #
before a relevant keyword or phrase (no spaces) in their tweets.
Clicking on like option on LinkedIn presents some differences w.r.t. the
Facebook like function. Indeed, on LinkedIn, when users click on the like link
underneath the various updates, this immediately forwards that particular up-
date out to all of the user first level connections. The share option, instead,
allows users to either redistribute the article (and partially modify it) as an up-
date to their connections, post it to a group (or multiple groups), or forward it
in a private message. Similarly to what happens in Twitter, also in LinkedIn
while a user publishes a resource he can mention one of his connections with the
@ symbol. He can also use a keyword as hashtag using the # symbol.
As for Flickr, by clicking on a photostream image, it is possible to open it
in the interactive photopage, thus allowing users to comment it and to embed
it on external websites. Moreover, images can be added to a user favourite list
or to user galleries. The main Google+ page consists of a “stream” of updates,
conversations and shared content. A user can make comments underneath con-
tent shared by other users, and he can appreciate contents clicking “+1” on it.
Google+ provides the referencing functionality in its posts. A user can mention
another user using the + or @ signs.
As for LiveJournal, users can interact with resources in different ways. For
instance, a user can leave a comment on a post of another user or share it
in his blog. He can also add to “Memories” a post. The Memories feature on
LiveJournal allows the organization of favorite resources with a keyword-based
archive system. Thanks to this functionality, a user can also add tags, or de-
scriptive keywords, to his own resources.
All the features of the OSNs described in this section are mapped by our
model, and this is formalized in the next section.
3 The conceptual model
To model at an abstract level the entities described in the previous section, we
use a graph. The set of nodes is partitioned into three disjoint sets P , R, and B,
which correspond to the set of social profiles, the set of resources, and the set of
bundles (which are resource containers), respectively.
An element of P models the profile of a user on a social network. It consists
in the tuple hurl, socialNetwork, screen-name, [personalInformation],
[picture]i, where url is the Web address that identifies and localizes the pro-
file, and socialNetwork is the commercial name of the social network which the
profile belongs to, screen-name is the name chosen by the user who registered
the profile to appear in the home-page of the profile or when posting a resource,
and, finally, personalInformation and picture are the information and the
image which the user inserted as related to the profile. The two last elements of
the tuple are optional (i.e., they can be null).
The set R models resources of the Web or created by users. A resource is rep-
resented by a tuple hurl, type, [description], [date]i, where url is the
Web address to access the resource, type indicates the type of the resource con-
tent, and finally, description and date, which are optional, represent the string,
inserted by the who published the resource, describing the resource itself and the
publishing date, respectively. For example, the most viewed video on YouTube
is a resource represented as h’https://www.youtube.com/watch?v=9bZkp7q1
9f0’, ’video/mp4’, ’PSY - GANGNAM STYLE’, ’07/15/2012’i.
Our model includes the bundle set B. Indeed, commonly users do not handle
a single resource, but most of the actions they do (e.g., publishing or sharing)
involve more resources simultaneously. For example, a user can publish more
photos or videos, can include a comment, and so on. In our model, we include all
resources handled simultaneously by a user in a bundle. A bundle is represented
by a tuple huri, [description], [date]i, where uri is the identifier of the
bundle, description, which is optional, is the string chosen by the user to be
shown with those resources and, finally, date represents the publishing date. As
we will see next, we represent the inclusion of a resource into a bundle by means
of containing edges.
In our model, relationships among profiles, resources and bundles are repre-
sented by direct edges of a graph. The set E of these edges is partitioned into 8
disjoint sets, named F , M , P u, S, T , Re, L, and Co.
The follow edge set F ⊆ E = {ps , pt | ps , pt ∈ P } models the fact that in the
(source) profile ps , it has been declared a certain type of relationship towards the
(target) profile pt . This kind of edge models different relationships. For exam-
ple, on Facebook or Flickr, it models friendships, on LinkedIn, job contacts,
and, on Twitter, followers. Observe that, typically, this kind of relationship oc-
curs between users of the same social network, because it is presumable that a
social network does not have interest in promoting links to profiles of another
(competitor) social network.
The me edge set M ⊆ E = {ps , pt | ps , pt ∈ P } denotes that the user with
profile ps has declared in this profile to have a second profile pt . This edge allows
a user to provide a link to its profile (typically) on a different social network or
(sometimes) on the same social network (as a sort of alias).
The publishing edge set P u ⊆ E = {ps , bt | ps ∈ P, bt ∈ B} indicates that the
user with profile ps has published in this profile a bundle bt . This edge models
one of the typical actions a user does when enriches his/her profile by publishing
resources.
The shared edge set S ⊆ E = {bs , bt | bs , bt ∈ B} specifies that the bundle
bs (published by a user) is derived from an already published bundle bt . This
type of edge is used when a user shares an existing bundle. Indeed, this action is
represented by two edges: a publishing edge (as described before) and a shared
edge from the new bundle to the existing one.
The tagging edge set T ⊆ E = {ps , brt , w | ps ∈ P, brt ∈ B ∪ R and w is a
word}, denotes that the user with profile ps assigned the word w to describe a
bundle or a resource br. By means of the tag mechanism, users contribute to
resource labelling, which is necessary to carry out several actions on resources,
such as searching or classification.
The referencing edge set Re ⊆ E = {bs , pt | bs ∈ B, pt ∈ P } models the fact
that a bundle bs includes a reference to the profile pt . For example, this occurs
when a tweet includes a user account name.
The like edge set L ⊆ E = {ps , pbrt | ps ∈ P, pbrt ∈ B ∪ R ∪ P } describes the
information that a user with the profile ps expressed a preference/appreciation
for a bundle, a resource or another user profile pbrt .
The containing edge set Co ⊆ E = {bs , rt | bs ∈ B, rt ∈ R} indicates that a
bundle bs contains the resource rt . For example, when a user publishes a photo
p and includes a comment c, this action is modeled by creating a bundle b with
a description c, a resource p, and finally, a containing edge from b to p.
Concerning how to practically map real-life data from social networks to each
component of the model, the reader can refer to [3]. In the next section, we show
how this model has been exploited at application level.
4 Case studies
Evaluating the accuracy of a model is a difficult task because often a golden
standard misses [2]. In these cases, evaluation can be done by humans (e.g., [13,
11]) or by applying the model to an application and evaluating the results (e.g.,
[16]). In this section, following the latter approach, we describe how our model
has been profitably applied to two applications very relevant in the context of
social network analysis.
The first application we discuss regards the extraction of information from
a multiple-social-network scenario. It is well known that any analysis activity
on social network users needs a preliminary task implementing the extraction of
data from social networks. In the past, several visit strategies have been adopted,
such as Breadth First Search [19], Random Walk [10] or Metropolis- Hastings
Random Walk [17]. In all these cases, data analysis focused on a single social
network and data extraction was a quite simple task because there was not the
problem of receiving data from different sources.
When data extraction involves different social networks, having a model that
is able to handle indifferently data from different social networks is a very useful
tool. In this case, it is possible to exploit a crawling task implementing the
following steps.
1. Selecting the starting account (seed). This step is very important to provide
data useful to the specified application. Usually, the starting account is ran-
domly selected from an available pool of accounts. For particular analysis,
the seed can be selected from those accounts having some characteristics,
for example, being a power user (i.e., they have a number of contacts much
higher than the average user [9]).
2. Building the sub-graph. In this step, the information about this account is
created: it includes the user account, contacts, published resources, and so
on. This step is strongly facilitated by our model. Indeed, by following the
procedures described in Section ??, we map all information extracted from
the different social networks to the components of our model (i.e., profiles,
resources, bundles, and their relationships).
3. Selecting the next account. There exist several strategies to implement this
step. A first possibility is to randomly select another profile (uniform sam-
pling), and this is feasible whenever a social network uses an identifier for
accounts and the domain of identifiers is known and limited. This occurs
for example for Facebook and Twitter [7]. Another possibility consists in
selecting one profile (i.e., a node of the graph) connected with the last vis-
ited profile by a follow edge or a me edge (see, for example, [10, 17]). Again,
it is also possible to select more than one (even all) of the profiles referred
above, as done for example in [4, 19]. Once one or more profiles have been
selected, Steps 2 and 3 are iterated until the desired amount of data have
been extracted or a stop condition has been reached.
The model defined here has been successfully used in the SNAKE system [6],
a tool supporting the extraction of data from social network accounts.
The second application that benefited from our model concerns the problem
of identifying users on the Web. A common approach to address this problem uti-
lizes profile matching techniques typically based on a set of identification proper-
ties, such as username, to find user corresponding identity. In [5], an improvement
of this approach is proposed. In particular, a new notion of profile similarity is
defined, by combining a string similarity between the associated usernames with
a contribution based on a suitable recursive notion of common-neighbor simi-
larity. The computation of the second contribution requires to compare profiles
coming from different social networks, which could be quite heterogeneous. The
use of our model allowed us to simplify this issue and to handle all profiles in
a uniform way. We can state that the success of the technique described in [5]
strongly relied on the model described in this paper.
5 Conclusion
It is a matter of fact that the multiplicity of social networks together with users’
membership overlap, result in a multiplicative effect in terms of information
power. Indeed, correlation, integration, negotiation of information coming from
different social networks offer a lot of strategic knowledge whose benefits are
still unexplored. In this paper, we have defined and implemented a model aimed
at creating a middleware on top of existing online social networks. The goal is
to provide a (conceptual) layer able to facilitate design and implementation of
applications relying on the internetworking nature of online social networks. By
means of two case studies, we showed the effectiveness of the proposed model.
References
1. G. Bell. Building social web applications. ”O’Reilly Media, Inc.”, 2009.
2. J. Brank, M. Grobelnik, and D. Mladenić. A survey of ontology evaluation tech-
niques. In In Proceedings of the Conference on Data Mining and Data Warehouses
(SiKDD 2005), 2005.
3. F. Buccafurri, G. Lax, S. Nicolazzo, and A. Nocera. A model to support design
and development of multiple-social-network applications. Information Sciences,
331:99–119, 2016.
4. F. Buccafurri, G. Lax, A. Nocera, and D. Ursino. Moving from social networks to
social internetworking scenarios: The crawling perspective. Information Sciences,
256:126–137, 2014. Elsevier.
5. F. Buccafurri, G. Lax, A. Nocera, and D. Ursino. Discovering missing me edges
across social networks. Information Sciences, 319:18–37, 2015.
6. F. Buccafurri, G. Lax, A. Nocera, and D. Ursino. A system for extracting structural
information from social network accounts. Software: Practice and Experience, 2015.
DOI: 10.1002/spe.2280.
7. M. Gjoka, M. Kurant, C. Butts, and A. Markopoulou. Walking in Facebook: A
case study of unbiased sampling of OSNs. In Proc. of the International Conference
on Computer Communications (INFOCOM’10), pages 1–9, San Diego, CA, USA,
2010. IEEE.
8. M. N. Jelassi, C. Largeron, and S. B. Yahia. Efficient unveiling of multi-members
in a social network. Journal of Systems and Software, 94:30–38, 2014.
9. S.-H. Lim, S.-W. Kim, S. Park, and J. H. Lee. Determining content power users in
a blog network: an approach and its applications. Systems, Man and Cybernetics,
Part A: Systems and Humans, IEEE Transactions on, 41(5):853–862, 2011.
10. L. Lovász. Random walks on graphs: A survey. Combinatorics, Paul Erdos is
Eighty, 2(1):1–46, 1993.
11. A. Lozano-Tello and A. Gómez-Pérez. Ontometric: A method to choose the ap-
propriate ontology. Journal of Database Management, 2(15):1–18, 2004.
12. V. S. A. Menezes, G. Zimbrão, and J. M. Souza. Group and link analysis of multi-
relational scientific social networks. Journal of Systems and Software, 86(7):1819–
1830, 2013.
13. P. Mika. Ontologies are us: A unified model of social networks and semantics. In
The Semantic Web–ISWC 2005, pages 522–536. Springer, 2005.
14. D. T. Nguyen, H. Zhang, S. Das, M. T. Thai, and T. N. Dinh. Least cost influence
in multiplex social networks: Model representation and analysis. In Data Mining
(ICDM), 2013 IEEE 13th International Conference on, pages 567–576. IEEE, 2013.
15. A. Papadimitriou, P. Symeonidis, and Y. Manolopoulos. Fast and accurate link pre-
diction in social networking systems. Journal of Systems and Software, 85(9):2119–
2132, 2012.
16. R. Porzel and R. Malaka. A task-based approach for ontology evaluation. In ECAI
Workshop on Ontology Learning and Population, Valencia, Spain, 2004.
17. D. Stutzback, R. Rejaie, N. Duffield, S. Sen, and W. Willinger. On unbiased
sampling for unstructured peer-to-peer networks. In Proc. of the International
Conference on Internet Measurements, pages 27–40, Rio De Janeiro, Brasil, 2006.
ACM.
18. Z. Sun, L. Han, W. Huang, X. Wang, X. Zeng, M. Wang, and H. Yan. Recommender
systems based on social networks. Journal of Systems and Software, 99:109–119,
2015.
19. S. Ye, J. Lang, and F. Wu. Crawling online social graphs. In Proc. of the Interna-
tional Asia-Pacific Web Conference (APWeb’10), pages 236–242, Busan, Korea,
2010. IEEE.