=Paper=
{{Paper
|id=None
|storemode=property
|title=Trust and Reputation in Social Internetworking Systems
|pdfUrl=https://ceur-ws.org/Vol-590/sasweb10_5.pdf
|volume=Vol-590
}}
==Trust and Reputation in Social Internetworking Systems==
https://ceur-ws.org/Vol-590/sasweb10_5.pdf
Trust and Reputation in Social Internetworking
Systems
Lora Aroyo1 , Pasquale De Meo2 , Domenico Ursino2
1
Department of Computer Science, VU University Amsterdam, De Boelelaan 1081,
1081 HV Amsterdam, The Netherlands
2
DIMET, Università “Mediterranea” di Reggio Calabria, Via Graziella, Località Feo
di Vito, 89060 Reggio Calabria, Italy
l.m.aroyo@cs.vu.nl,demeo@unirc.it,ursino@unirc.it
Abstract A Social Internetworking System (SIS) is the scenario arising
when Web users decide to affiliate to multiple social networks. Recent
studies show an increasing user tendency of creating multiple identities
on different social systems and exposing, in each of them, different traits
of their personalities and tastes. This information provides a better pic-
ture of user needs and enhances the quality of services they can use.
In the next years a large growth of SIS phenomenon is foreseeable. In
order to boost the level of user participation in a SIS, suitable mecha-
nisms capable of discerning reliable users must be designed. We propose
a model to represent a SIS, a software architecture to gather real data
and analyze the structural properties of a SIS. In concrete use cases
with different contexts and different levels of protection of data, we in-
troduce an ontology-based model to compute trust and reputation in a
SIS. This research is collaborative effort between the Vrije Universiteit
Amsterdam and the University of Reggio Calabria in the context of a
Marie Curie Fellowship.
1 Introduction
Social media applications, such as blogs, multimedia and link sharing sites, ques-
tion and answering systems, wikis and online forums, are growing at an unprece-
dented rate and are estimated to generate a significant amount of the contents
currently available on the Web [14]. Social media applications are a significant
part of a more meaningful kind of applications, named Web 2.0 applications,
which aim to provide a platform for information sharing and collaboration among
users on the Web.
In social media applications, users form communities, typically modelled as
social networks. Users are driven to get in touch and become friends of other
users, create and publish their own contents (like videos or photos), share these
contents with others, rate and comment contents posted by others. Examples of
popular Web-based social networks are Facebook, MySpace and LinkedIn.
The value of social networks expresses in multiple ways. For instance, users
may take an advantage of their interactions with other users to find information
2 L. Aroyo, P. De Meo, D. Ursino
relevant to them or they can explore connections existing in a social network
to get in touch with user with whom they may profitably interact: many Web
users, as an example, indicate that they were able to get a job through their
contacts in LinkedIn1 .
A further advantage is that social networks allow to disseminate new knowl-
edge in a widespread fashion, to diffuse innovations, to spread opinions (e.g.,
social or political messages) among members, to advertise new products [16].
The power of social networks has been fully recognized by institutional ac-
tors like museums, TV broadcaster, academic and government institutions. For
instance, the Rijksmuseum Amsterdam is exploring the added-value of providing
artworks online, allowing users to express their opinions on them or contribute to
describing artwork’s. Furthermore, major European broadcasters, such as BBC
and RAI are experimenting with Web 2.0 technologies to improve interactivity
and participation of TV consumers.
Users often decide to affiliate to multiple social networks: for instance, in a
recent survey, Ofcom found that 39% of UK adults with at least one social net-
working profile has indeed two or more profiles2 . We call Social Internetworking
System (hereafter SIS) the scenario arising when many users decide to affiliate
with multiple social networks. Companies are discovering the potential of social
internetworking and are promoting systems capable of supporting social inter-
networking tasks. For instance, Google has recently proposed Open Social [4], a
set of APIs to access social sites, like LinkedIn or MySpace. Some systems (like
FriendFeed [2]) allow the users to share their activities with other users in mul-
tiple social networks; Gathera [3], also provide the users with a single interface
to handle their accounts on multiple social networks.
The main goal of these systems is to offer a technological platform to en-
sure data portability among different social networks. The major bottleneck for
the success of a SIS is the absence of mechanism that helps users in finding
other “reliable” users with whom they can profitably interact and discloses the
presence of malicious users or spammers.
In the past, significant research efforts have been done to define and handle
trust and reputation, as a large body of literature highlights [1,11,12,15,19,22].
However, in our opinion, several reasons explain a further investigation. A
first research question is to provide a model capable of representing a SIS, its
components and their relationships. In addition, it is necessary to gather real
data about a SIS in order to understand its structural properties and clarify to
what extent a SIS differs from traditional social networks.
A second issue depends on the fact that the concepts of trust and reputation
may assume different meanings according to the scenario in which a user operates
in. For instance, in communities like Question & Answering systems (in which
users are allowed to pose questions, to answer questions raised by other users
1
http://www.mainstreet.com/article/career/employment/social-media-
job-seeker-s-best-new-tool
2
http://www.ofcom.org.uk/advice/media literacy/medlitpub/medlitpubrss/
socialnetworking/annex3.pdf
Trust and Reputation in Social Internetworking Systems 3
and, finally to rate received answers), the reputation of a user coincides with
his level of expertise on a particular topic; in a Web community like YouTube,
the reputation a user coincides with the quality of contents he generated. This
requires to define a procedure to compute, in an abstract and general fashion,
the reputation of a user and to specialize it in concrete domains.
As a final research challenge, it would be necessary to define a model to
represent trust and reputation in different contexts. In addition, it is useful
to observe that, in different contexts, different policies for accessing, publishing
and re-distributing data may exist. For instance, in the case of a TV broadcaster
which delivers online part of its archive of resources, users are allowed for instance
to use some resources (e.g., for educational purposes) but are forbidden to re-use
those protected by copyright. To address these issues, it would be beneficial to
design an ontology to model the key concepts of trust and reputation in different
environments characterized by different levels of protection of data.
In this paper we propose a methodology to handle trust and reputation in a
SIS. The paper summarizes the research activities we are planning to carry out
in the context of a Marie Curie Intra-European Fellowships for Career Devel-
opment (IEF), a funding opportunity provided by EU Commission. The paper
is structured as follows: in Section 2 we review existing trust and reputation
models and illustrate the challenges arising in a SIS. In Section 3 we illustrate
a model to represent the features of a SIS along with a software architecture
we are currently implementing to gather real data from a SIS and analyze its
structural properties. In Section 4 we provide a general model to compute trust
and reputation in a SIS and illustrate the steps we are planning to specialize it
in real contexts; in particular, we plan to adapt our notion of trust on data gath-
ered within two research projects, namely NoTube [18] and Agora [5]. In Section
5 we discuss a possible ontology-based model to represent trust and reputation
in environments characterized by different levels of protection of data. Finally,
in Section 6 we draw our conclusions.
2 Background and challenging issues
In virtual communities the term trust is generally exploited to indicate the re-
liance that a community member associates with another one. Trust values are
“local parameters” in the sense that specifying the trust of a user A toward a
user B is equivalent to indicate how much A perceives B as reliable.
The opinion of the whole community of users toward a member of the com-
munity itself is known as reputation. In the past, the issue of computing and
handling trust and reputation in virtual communities has been deeply investi-
gated and several models and approaches to facing it have been proposed.
Here we discuss some of these approaches and outline the challenges we
encounter in the context of a SIS. Existing approaches can be classified into
two categories:
Graph-Based Approaches. A first category of approaches model a user
community as a graph G in which nodes represent users [1,11,12,22]. An edge
4 L. Aroyo, P. De Meo, D. Ursino
linking two nodes v and u indicates that the user v explicitly trusts the user u.
The graph G is usually sparse because a user typically evaluates a handful of
other users; as a consequence, various techniques have been proposed to infer
implicit trust relationships. In detail, the approach of [1] applies a maximum
network flow algorithm on G to compute trust between any pair of users. In
[11] the authors apply a modified version of the Breadth First Search algorithm
on G to infer multiple values of reputation for each user; these values are then
aggregated by applying a voting algorithm to produce a final (and unique) value
of reputation for each user. The approach of [12] considers paths up to a fixed
length k in G and propagates the explicit trust values on them to obtain the im-
plicit ones. In [22] trust values are computed by applying a spreading activation
algorithm.
Graph-based approaches leverage on explicit trust relationships declared be-
tween pairs of users. As a consequence, they neglect to consider a broad range
of activities that, in a SIS (e.g., the activity of rating resources) are a precious
and reliable indicator of trust.
Link-Based approaches. A second category of approaches use ranking al-
gorithms such as PageRank [8] or HITS [17], which have been successfully applied
in the context of Web Search, to find trust values. For instance, [15] proposes an
approach based on PageRank to measure peer reputation in a peer-to-peer net-
work. The approach of [19] defines a probabilistic model of trust which strongly
resembles that described in [15]; however, differently from this last, the approach
of [19] computes and handles trust values and not reputation values. In [9] the
authors present an algorithm which computes global reputation values in a peer-
to-peer network; the proposed algorithm uses a personalized version of PageRank
along with information about the past experiences of peers.
Experimental tests indicated that link-based methods can obtain precise re-
sults and are often attack-resistant, i.e., they can resist to attempts conceived to
manipulate reputation scores.
We observe that in some approaches trust is conceived as a measure of per-
formance. For instance, in [15], the trust of a peer depends on the success of
downloading a file from it and, then, trust depends on parameters like the num-
ber of corrupted files stored in the peer or the number of connections with the
peer that have been lost. By contrast, in our case, trust should quantitatively
encode the confidence of a user in the opinions formulated by other ones.
We can observe that both graph-based and link-based approaches try to
model trust and reputation in a “force-mass-acceleration” style. In other words,
these approaches try to capture all factors influencing trust and reputation and
combine them in a set of equations. The resulting model is too complicated to be
handled and it may not provide significant results. In our opinion, the assessment
of trust and reputation critically depends on the concrete domain in which we
are operating in and we believe that an universal model of trust is not possible.
To better clarify this concept, we report some results emerging from the
PrestoPrime project [7], an EU financed project devoted to study and develop
Trust and Reputation in Social Internetworking Systems 5
practical solutions for the long-term preservation of digital media objects, pro-
grammes and collections.
In the context of PrestoPrime, two pilot demonstrators were developed. In
the first one, in a game-like environment, users were asked to label videos by
applying simple keywords (tags). Experiments with users showed that a satisfac-
tory measure of trust between a pair of users who do not know each other can
be obtained by considering the tags they apply to label a video and computing
the degree of match of the set of tags they inserted.
In the second demonstrator, users were provided with a small annotation
environment allowing them to label museum objects with four main entry fields
(i.e., “who”, “where”, “what”, and “when”). This allowed us to create links
between museum objects on the basis of the key dimensions “who”, “where”,
“what”, and “when”; as an example, objects coming from different museum
collections can be tied if they refer to the same artistic and historical context
and this produces a more complete description of cultural movements.
The notion of trust developed in the context of the first demonstrator is not
applicable for the second one, and other factors influencing trust and reputation
need to be studied.
A further challenge we are in charge of studying depends on the fact that, in
some cases, real organizations often decide to make available on the Web their
own resources and often allow end users to enrich their descriptions through
metadata like tags. For instance, think of the case of public TV broadcasters
like BBC which offers online a large number of contents referring to its TV pro-
grammes. Each organization may use different policies for accessing, distributing
and labelling the contents they produce and disseminate. For instance, a digital
content may published online only in some specific cases (e.g., if the material
must be used in education) while its usage is forbidden for commercial purposes.
This proves that, in the process of defining trust and reputation, it is necessary
to consider not only the application context but also te level of data protection
about available resources.
3 Defining a basic model of social internetworking
The first goal of our research is to find a suitable model to represent a Social
Internetworking System (SIS) and interactions between humans that can take
place in it.
To this purpose, our model must fit two requirements:
– Requirement 1. The model should be rich enough to represent a wide range
of heterogeneous entities (i.e,. users, resources, posts, comments, ratings, and
so on) and their interactions (e.g., users may declare to be friends or they
may rate resources).
– Requirement 2. The model should be easy to manipulate and intuitive.
Clearly, Requirements 1 and 2 are conflicting each other and a suitable trade-
off is compulsory. Traditional approaches to modelling social networks are usually
6 L. Aroyo, P. De Meo, D. Ursino
based on graphs. Nodes in graphs represent social network actors (e.g., users)
while edges identify relationships between them.
We believe that graph-based models are not satisfactory in the context of a
SIS for several reasons.
A first weakness relies on the role the nodes would have if we would decide
to represent a SIS through a graph. Generally, a social network consists of
homogeneous nodes, i.e., all nodes represent objects sharing the same nature. As
claimed by Requirement 1, in a SIS heterogeneous entities co-exist and these
heterogeneities must be properly modelled.
A further limitation is that graph-based models are able to represent one-
dimensional networks, i.e., edges of a graph specify that only one particular kind
of relationship may exist between nodes. On the contrary, we expect that a SIS
should be represented through a multi-dimensional network because various type
of interactions may involve entities of the same type or of different nature: for
instance, an edge should link a user u and a resource r to indicate that u has
posted r or an edge should tie two users to indicate that they declared to be
friends.
Finally, edges in graphs highlight binary relationships between nodes they
link. In a SIS, it could be useful to consider n-ary relationships (e.g., an edge
may glue together a user u, a resource r and a tag t under the hypothesis that
u applied t to label r).
We are currently studying a more sophisticated model in which a SIS is rep-
resented through an hypergraph such that: (i) nodes are labelled and the label
of a node reflects the nature of the object represented by the node itself; (ii)
multiple hyperedges may run between two nodes to indicate that multiple inter-
actions may take place between two arbitrary entities; (iii) hyperedges denote
relationships involving two or more entities.
In addition to defining a model to represent a SIS, we are also interested in
gathering data from real social networks in order to understand the properties
showed by a real SIS. For instance, it would be interesting to check whether
properties typical of real social networks (e.g., the small world phenomenon) still
emerge in a SIS.
Such a task is quite complex because, in different networks, a user may have
different identities so it would be extremely hard to join information scattered
across multiple networks.
To address this issue, we used the Google Social Graph API [6]. Social Graph
API allows human users or software applications to access public connections
between people on the Web. In particular, Social Graph API can be queried
through an HTTP request and is able to return two kind of results:
– A list of public URLs that are associated with a person. For instance, given a
user u, Social Graph API reveals the URLs of the blog of u and his Twitter
page.
– A list of public declared connections between people. For instance, it returns
the list of persons who, in at least one social network, have a link to the blog
of u or any other page referable to u.
Trust and Reputation in Social Internetworking Systems 7
Figure 1. The architecture of our crawler
At the moment of writing, we have implemented a simple crawler to explore
and gather data from a SIS. The architecture of our system is shown in Figure
1. In particular, a Java crawler invokes Social Graph API by sending a seed URL
associated with a user u; the API sends back the list of people who are somewhat
related with u. The Java crawler gets these data and launches a Breadth-First-
Search like procedure to find new URLs and connections. Retrieved results are
permanently stored in a relational DBMS implemented in MySQL.
A key issue in gathering user data is privacy. In fact, Social Graph API
handles and returns only URLs of public pages and publicly declared connections
between them. The API is not able to reveal non-public information (e.g., private
profile pages or Web pages accessible to a restricted circle o persons). Despite
these limitations, users may unintentionally leave digital traces of their interac-
tions and, by putting together these traces, confidential data may be disclosed.
As a consequence, informing users about privacy risks is not enough to avoid
privacy violations, but complex algorithms are required.
Traditional approaches to protecting privacy (e.g., those based on k-anonymity
principle) have been proved to not be effective in the context of social networks
[13]. With regard to this, we point out that some successful approaches (relying
on the idea of carrying out a sequence of random insertions and deletion of edges
in the social network graph) have been proposed [13] and, despite privacy is not
the main focus of our work, we plan to study whether these approaches can be
extended to a SIS.
4 Defining reputation in a Social Internetworking System
As a further step, we are interested in studying a model of reputation for a SIS.
The notion of trust/reputation in the context of a SIS (and, in general, for Web
8 L. Aroyo, P. De Meo, D. Ursino
2.0 applications) is hard to define because, as shown in Section 2, in different
contexts, they may assume different meanings.
We propose a methodology to compute reputation in a SIS which operate
in two stages: in the former stage we review and analyze the factors capable of
influencing the value of reputation in a SIS. In the latter stage we consider some
concrete domains and specialize our methodology to them.
Intuitively, we assume that the reputation of a user depends on the following
facts:
The reputation of a user depends on the relationship he created in the SIS.
We suggest to use the hypergraph model introduced in Section 3 to represent
users and their relationships in a SIS. Past user interactions are analyzed to
determine the level of trust a user u confers to a user v and this information
is used to weight edges in the hypergraph representing the SIS. Since a user
generally interact with an handful of other users, the hypergraph we obtain is
sparse and a suitable algorithm to propagate trust values is necessary. Currently,
we are planning to use a link-based algorithm like PageRank. At the end of this
step, we are able to generate a vector r0 such that the i-th component of r0 equals
to the reputation of the i-th user.
Users with high level of reputation are also those who produce high quality
resources. The quality of a resource could be computed by consider the average
rating it got and, then, resources with a high average rating are also high qual-
ity resources. To avoid biases, we can pose a further requirement: the number
of ratings received by a resource must be statistically significant, i.e., we can
consider only resources which received at least Nmin ratings, being Nmin a suit-
able threshold. Such a requirement would avoid that resources evaluated by a
small number of users are deemed better than resources rated by a large mass
of human users.
The procedure described above resembles that applied in many social systems
like YouTube or Digg to evaluate the quality of a resource. We believe that such a
procedure is affected by several fallacies and it may incur in harsh inaccuracies.
In fact, spam or malicious users may tend to provide generous evaluations to
artificially inflate the evaluation of a resource. As a consequence, we need a more
complicated framework capable of putting together the reputation of users, the
quality of resources they post and the evaluations associated with resources. At
the current stage of the project we are considering, as a possible solution, the
following criterium:
A user has a high reputation if he authors high quality resources. A
resource, in its turn, is of high quality if it gets a high average rating and
it has been posted by users with high reputation.
The intuition provided above relies on a mutual reinforcement principle that
is similar, to some extent, the approach underlying HITS [17] algorithm. The
principle outlined above easily turns into a set of linear equations. In fact, let n
be the number of users composing a SIS and let m be the number of resources
00
they authored. Let r be an n-th dimensional array such that the i-th entry
Trust and Reputation in Social Internetworking Systems 9
00
of r equals to the reputation (to compute) of the i-th user and let q be an
m-th dimensional array such that the j-th entry of q equals to the quality (to
compute) of the j-th resource. Finally, let e be an m-th dimensional array such
that the j-th entry of e equals to the average rating of the j-th resource and let
A be an n-by-m matrix such that Aij equals 1 if the i-th user posted the j-th
resource and 0 otherwise.
According to this notation, we can write the following equations:
00
r ∝ Aq (1)
00
q ∝ AT r + e (2)
In both Equations 1 and 2, the symbol ∝ means “is proportional to”. As for
Equation 1, the i-th row of the the product Aq specifies the sum of the qualities
of the resources authored by the i-th user. This immediately follows from the
definition of product between a matrix and a vector. Interestingly enough, the
A matrix can be interpreted as the adjacency matrix of a bipartite graph whose
nodes represent users and resources and edges link a user to the resources he
authored. In Equation 2, the symbol AT is the transpose of A. As in the previous
case, AT matrix can be viewed as the adjacency matrix of a bipartite graph
whose nodes represent resources and users and edges link a resource to the user
who authored it. Observe that the same model holds if we assume that a resource
has been posted by one user or it has been posted by multiple users. The product
00
AT r is an m-th dimensional vector whose j-th entry specifies the reputation
(or the sum of the reputations) of the user (users) who posted the j-th resource.
By plugging Equation 2 into Equation 1 we obtain:
00
h 00
i 00 00
r ∝ A AT r + e ⇒ r − AAT r ∝ Ae ⇒
00 00 −1
I − AAT ∝ Ae ⇒ r ∝ I − AAT
⇒r Ae
−1
Since AAT is symmetric, its eigenvalues are real [20]. In particular, I − AAT
can be easily and effectively approximated by computing the dominant eigen-
vector of AAT . Such a result is of great practical impact because there exist
efficient numerical methods to compute dominant eigenvector of a symmetric
matrix (think of Lanczos method [20]) and, then, our methodology is suitable
also if the size of A gets very large; such a case if quite common in real cases be-
cause, in traditional social sites the number of users and resources they generate
(which correspond to the number of rows and columns of A) is huge.
00
Finally, we merge the arrays r0 and r into a single reputation value r as
follows:
00
r = αr0 + (1 − α) r (3)
The coefficient α ∈ [0, 1] is instrumental in weighting the contributions com-
ing from link analysis and the analysis of resources generated by a user. We plan
to tune α by applying a linear regression technique.
10 L. Aroyo, P. De Meo, D. Ursino
Once a theoretical model of reputation in a SIS has been defined, our in-
tention is to specialize it in concrete domains. In particular, we are interested in
monitoring and analyzing the behaviour of users in long-term experiments asso-
ciated with different domains; the notion of reputation, from abstract concept
turns into a concrete tool to aid user in better taking advantage of potentiali-
ties offered by the SIS. Experiments on real users allow us to get an iterative
assessment of the strengths and weaknesses of our notion of reputation as well
as indications for improvement.
To this purpose, we will use data gathered in the context of two research
projects, namely: (i) NoTube (an EU financed project on interactive television)
[18], and (ii) Agora [5] (a Dutch funded project on digital museums and audio-
visual archives). In the context of interactive television, trust/reputation values
represent the level of expertise of a user. This information could be exploited to
select in a personalized fashion contents to propose to the user. As for digital
museums we can study what parameters in the user behaviour are relevant for
producing authoritative annotations and what are the motivations for users to
participate in this labelling process. This information could be instrumental in
better using human mass potential in annotating artworks.
5 Building an ontology-based model of trust and
reputation in a Social Internetworking System
Once we have defined the concept of trust and reputation in concrete domains
it is advantageous to create a model capable of representing them in different
domains. To this purpose we plan to design an ontology capable of specifying
how reputation and trust specialize in different application contexts.
To the better of our knowledge, there are few approaches to designing on-
tologies to model trust. For instance, in [10], TrustOntology is presented. This
is an OWL ontology allowing each user to indicate the people he trusts. Trust
information is automatically composed to infer new values of trust for newcomer
users. In [21], the authors suggest a trust protocol in which the decision about
the trustworthiness of a message depends on many factors like the creator (who)
of the message (what), time (when), location (where), and intent (why). An on-
tology to capture factors influencing trust and a set of functions to evaluate trust
is presented.
Our goal is different from that of [10] and [21] we want to model how repu-
tation specializes in different contexts. In addition our ontology can be used to
represent a scenario in which different organizations decide to make available on
the Web their own resources. An organization (e.g., a cultural institution) may
let its users to freely use, copy and re-distribute available resources. Another
organization may apply different policies to protect data because some resources
can be freely disseminated, other resources are not accessible because protected
by copyright and, finally, some resources can be published online and re-used
for some purposes (e.g., as educational material) but their access is forbidden in
other cases.
Trust and Reputation in Social Internetworking Systems 11
The availability of such an ontology would offer us, on the long run, the
possibility of designing complex software applications running across multiple
social networks. For instance, we can think of a content-based recommender
system operating as follows:
1. A user issues a query.
2. The query is forwarded to multiple social system and a list of resources
matching the query is retrieved by each social system.
3. Retrieved resources are ranked on the basis of the reputation of the users
who created, on the application context and on the rights for its distribution.
4. A global list is produced by merging the previous ones.
Such an application is, in our opinion, capable of introducing relevant novel-
ties in the research field of Recommender Systems. In fact, the proposed applica-
tion is able to sift through different social sites (while traditional Recommender
Systems usually operate on a single resource repository) and is able to rank
resources on the basis of multiple and criteria.
6 Conclusions
In this paper we introduce the concept of Social Internetworking System, i.e., the
scenario arising when Web users decide to affiliate to multiple social networks.
We propose a model to represent a SIS and describe the main components of a
software architecture we are implementing to gather real data from a SIS and
analyze its structural properties. In concrete use cases with different contexts
and different levels of protection of data, we introduced an ontology-based model
to compute trust and reputation in a SIS. This research is collaborative effort
between the Vrije Universiteit Amsterdam and the University of Reggio Calabria
in the context of a Marie Curie Fellowship.
In the future we plan to gather a large amount of data about a SIS and
carry out an empirical study on them. The goal is to understand whether some
properties of real social networks (like small world phenomenon, power law dis-
tribution of in-degree and out-degree distributions, and so on) if they are still
confirmed in a SIS or if significant deviations emerge.
A further research line is to carry out a detailed review of existing literature
on the meaning of trust and reputation in different social site. Finally, we plan to
test the effectiveness of our ontology-based model with an experiment involving
real users. In particular, the validation phase will be strictly tied to the activity
of designing our ontology; in fact, we shall use feedbacks provided by users to
revise the structure of our ontology.
References
1. Advogato’s trust metric. http://www.advogato.org/trust-metric.html, 2000.
2. FriendFeed. http://friendfeed.com/, 2009.
12 L. Aroyo, P. De Meo, D. Ursino
3. Gathera. http://www.gathera.com/, 2009.
4. Google Open Social. http://code.google.com/intl/it-IT/
apis/opensocial/, 2009.
5. Agora: Creating the historic fabric for and providing web-enabled access to objects
in dynamic historical sequences. http://agora.cs.vu.nl/, 2010.
6. Google Social Graph API. http://code.google.com/intl/it-IT/apis/socialgraph/,
2010.
7. Prestoprime: Keeping audiovisual contents alive. http://www.prestoprime.org/,
2010.
8. S. Brin and L. Page. The anatomy of a large-scale hypertextual web search engine.
Computer Networks, 30(1-7):107–117, 1998.
9. P. Chirita, W. Nejdl, M. T. Schlosser, and O. Scurtu. Personalized Reputation
Management in P2P Networks. In Proc. of the ISWC Workshop on Trust, Security,
and Reputation on the Semantic Web, CEUR Workshop Proceedings, Hiroshima,
Japan, 2004. CEUR-WS.org.
10. J. Golbeck. Trust ontology. http://www.schemaweb.info/schema/SchemaDetails.aspx
?id=171, 2010.
11. J. Golbeck and J.A. Hendler. Inferring binary trust relationships in web-based
social networks. ACM Transactions on Internet Technology, 6(4):497–529, 2006.
12. R. Guha, R. Kumar, P. Raghavan, and A. Tomkins. Propagation of trust and
distrust. In Proc. of the International Conference on World Wide Web (WWW
’04), pages 403–412, New York, NY, USA, 2004. ACM.
13. M. Hay, G. Miklau, D. Jensen, D.F. Towsley, and P. Weis. Resisting structural
re-identification in anonymized social networks. Proceedings of the VLDB Endow-
ment, 1(1):102–114, 2008.
14. P. Heymann, G. Koutrika, and H. Garcia-Molina. Can Social Bookmarks Improve
Web Search? In Proc. of International Conference on Web Search and Data Mining
(WSDM 2008), pages 195–206, Stanford, California, USA, 2008. ACM Press.
15. S.D. Kamvar, M.T. Schlosser, and H. Garcia-Molina. The Eigentrust algorithm for
reputation management in P2P networks. In Proc. of the International Conference
on World Wide Web (WWW 2003), pages 640–651, Budapest, Hungary, 2003.
ACM Press.
16. J. Kleinberg. The convergence of social and technological networks. Communica-
tions of the ACM, 51(11):66–72, 2008.
17. J. M. Kleinberg. Authoritative sources in a hyperlinked environment. Journal of
the ACM, 46(5):604–632, 1999.
18. L. Nixon and L. Aroyo. NoTube – Making TV a Medium for Personalized Inter-
action. In Proc. the European Interactive TV Conference (EuroITV 2009), pages
22–25, Leuven, Belgium, 2009. University of Leuven.
19. M. Richardson, R. Agrawal, and P. Domingos. Trust Management for the Seman-
tic Web. In Proc. of International Conference on Semantic Web (ISWC 2003),
pages 351–368, Sanibel Island, FL, USA, 2003. Lecture Notes in Computer Sci-
ence, Springer.
20. G.W. Stewart. Matrix Algorithms: Basic Decompositions (Volume 1). Society for
Industrial Mathematics, 1998.
21. S. Toivonen and G. Denker. The Impact of Context on the Trustworthiness of Com-
munication: An Ontological Approach. In Proc. of the ISWC Workshop on Trust,
Security, and Reputation on the Semantic Web, volume 127 of CEUR Workshop
Proceedings. CEUR-WS.org, 2004.
22. C. Ziegler and G. Lausen. Propagation models for trust and distrust in social
networks. Information Systems Frontiers, 7(4-5):337–358, 2005.