Cb4301449454

Miss Janhavi A. Patokar et al. Int. Journal of Engineering Research and Applications www.ijera.com
ISSN : 2248-9622, Vol. 4, Issue 3( Version 1), March 2014, pp.449-454
www.ijera.com 449 | P a g e
A Review Paper on Filtering Of Unwanted Messages from OSN
User Wall Using Content-Based Filtering Method
Miss Janhavi A. Patokar 1
, Prof. V.T.Gaikwad 2
Information Technology Department,
Sipna College of Engineering and Technology, Amravati.
Abstract-
In today On-line Social Networks (OSNs), one fundamental issue is to give users the ability to control the
messages posted on their own private space to avoid that unwanted content is displayed. Up to now OSNs
provide little support to this requirement. To fill the gap, we built a system which allows OSN users to have a
direct control on the messages posted on their walls. This is achieved through a flexible rule-based system, that
allows a user to customize the filtering criteria to be applied to their walls, and a Machine Learning based soft
classifier automatically labelling messages in support of content-based filtering.
Keywords - On-line Social Networks, Information Filtering, Short Text Classification, Policy-based
personalization.
I. INTRODUCTION
In the last years, On-line Social Networks
(OSNs) have become a popular interactive medium to
communicate, share and disseminate a considerable
amount of human life information. Daily and
continuous communication implies the exchange of
several types of content, including free text, image,
audio and video data. The huge and dynamic
character of these data creates the premise for the
employment of web content mining strategies aimed
to automatically discover useful information dormant
within the data and then provide an active support in
complex and sophisticated tasks involved in social
networking analysis and management. A main part of
social network content is constituted by short text, a
notable example are the messages permanently
written by OSN users on particular public/private
areas, called in general walls[1].
The aim of the present work is to propose
and experimentally evaluate an automated system,
called Filtered Wall (FW), able to filter out unwanted
messages from social network user walls. The key
idea of the proposed system is the support for
content- based user preferences. On-line Social
Networks (OSNs) are today one of the most popular
interactive medium to communicate, share and
disseminate a considerable amount of human life
information. Daily and continuous communications
imply the exchange of several types of content,
including free text, image, audio and video data.
According to Facebook statistics 1 average user
creates 90 pieces of content each month, whereas
more than 30 billion pieces of content (web links,
news stories, blog posts, notes, photo albums, etc.)
are shared each month. The huge and dynamic
character of these data creates the premise for the
employment of web content mining strategies aimed
to automatically discover useful information dormant
within the data. They are instrumental to provide an
active support in complex and sophisticated tasks
involved in OSN management, such as for instance
access control or information filtering. Information
filtering has been greatly explored for what concerns
textual documents and, more recently, web content.
However, the aim of the majority of these proposals
is mainly to provide users a classification mechanism
to avoid they are overwhelmed by useless data. In
OSNs, information filtering can also be used for a
different, more sensitive, purpose. This is due to the
fact that in OSNs there is the possibility of posting or
commenting
other posts on particular public/private areas, called
in general walls. Information filtering can therefore
be used to give users the ability to automatically
control the messages written on their own walls, by
filtering out unwanted messages.
We believe that this is a key OSN service
that has not been provided so far. Indeed, today
OSNs provide very little support to prevent unwanted
messages on user walls. For example, Facebook
allows users to state who is allowed to insert
messages in their walls (i.e., friends, friends of
friends, or defined groups of friends). However, no
content-based preferences are supported and
therefore it is not possible to prevent undesired
messages, such as political or vulgar ones, no matter
of the user who posts them. Providing this service is
not only a matter of using previously defined web
content mining techniques for a different application,
rather it requires to design ad-hoc classification
RESEARCH ARTICLE OPEN ACCESS

strategies. This is because wall messages are
constituted by short text for which traditional
classification methods have serious limitations since
short texts do not provide sufficient word occurrences.
The aim of the present work is therefore to
propose and experimentally evaluate an automated
system, called Filtered Wall (FW), able to filter
unwanted messages from OSN user walls. We exploit
Machine Learning (ML) text categorization
techniques [2] to automatically assign with each short
text message a set of categories based on its content.
To the best of our knowledge this is the first proposal
of a system to automatically filter unwanted messages
from OSN user walls on the basis of both message
content and the message creator relationships and
characteristics.
II. LITERATURE REVIEW
The main contribution of this paper is the
design of a system providing customizable content-
based message filtering for OSNs, based on ML
techniques. As we have pointed out in the
introduction, to the best of our knowledge we are the
first proposing such kind of application for OSNs.
However, our work has relationships both with the
state of the art in content-based filtering, as well as
with the field of policy-based personalization for
OSNs and, more in general, web contents. Therefore,
in what follows, we survey the literature in both these
fields.
2.1 Content-based filtering:
Information filtering systems are designed to
classify a stream of dynamically generated
information dispatched asynchronously by an
information producer and present to the user those
information that are likely to satisfy his/her
requirements [3]. In content-based filtering each user
is assumed to operate independently. As a result, a
content-based filtering system selects information
items based on the correlation between the content of
the items and the user preferences as opposed to a
collaborative filtering system that chooses items
based on the correlation between people with similar
preferences. Documents processed in content-based
filtering are mostly textual in nature and this makes
content-based filtering close to text classification. The
activity of filtering can be modeled, in fact, as a case
of single label, binary classification, partitioning
incoming documents into relevant and non relevant
categories [4]. More complex filtering systems
include multi-label text categorization automatically
labeling messages into partial thematic categories.
Content-based filtering is mainly based on
the use of the ML paradigm according to which a
classifier is automatically induced by learning from a
set of pre-classified examples. A remarkable variety
of related work has recently appeared, which differ
for the adopted feature extraction methods, model
learning, and collection of samples [5], [6], [7],
[8],[9]. The feature extraction procedure maps text
into a compact representation of its content and is
uniformly applied to training and generalization
phases. The application of content-based filtering on
messages posted on OSN user walls poses additional
challenges given the short length of these messages
other than the wide range of topics that can be
discussed. Short text classification has received up to
now few attention in the scientific community.
Recent work highlights difficulties in defining robust
features, essentially due to the fact that the
description of the short text is concise, with many
misspellings, non standard terms and noise.
Focusing on the OSN domain, interest in
access control and privacy protection is quite recent.
As far as privacy is concerned, current work is
mainly focusing on privacy-preserving data mining
techniques, that is, protecting information related to
the network, i.e., relationships/nodes, while
performing social network analysis [5]. Works more
related to our proposals are those in the field of
access control. In this field, many different access
control models and related mechanisms have been
proposed so far (e.g., [6,2,10]), which mainly differ
on the expressivity of the access control policy
language and on the way access control is enforced
(e.g., centralized vs. decentralized). Most of these
models express access control requirements in terms
of relationships that the requestor should have with
the resource owner. We use a similar idea to identify
the users to which a filtering rule applies. However,
the overall goal of our proposal is completely
different, since we mainly deal with filtering of
unwanted contents rather than with access control. As
such, one of the key ingredients of our system is the
availability of a description for the message contents
to be exploited by the filtering mechanism as well as
by the language to express filtering rules. In contrast,
no one of the access control models previously cited
exploit the content of the resources to enforce access
control. We believe that this is a fundamental
difference. Moreover, the notion of blacklists and
their management are not considered by any of these
access control models.
2.2 Policy-based personalization of OSN contents
Recently, there have been some proposals
exploiting classification mechanisms for
personalizing access in OSNs. For instance, in [11] a
classification method has been proposed to categorize
short text messages in order to avoid overwhelming
users of microblogging services by raw data. The
system described in [11] focuses on Twitter2 and
associates a set of categories with each tweet
describing its content. The user can then view only
certain types of tweets based on his/her interests. In

contrast, Golbeck and Kuter [12] propose an
application, called FilmTrust, that exploits OSN trust
relationships and provenance information to
personalize access to the website. However, such
systems do not provide a filtering policy layer by
which the user can exploit the result of the
classification process to decide how and to which
extent filtering out unwanted information. In contrast,
our filtering policy language allows the setting of FRs
according to a variety of criteria, that do not consider
only the results of the classification process but also
the relationships of the wall owner with other OSN
users as well as information on the user profile.
Moreover, our system is complemented by a flexible
mechanism for BL management that provides a
further opportunity of customization to the filtering
procedure. The only social networking service we are
aware of providing filtering abilities to its users is
MyWOT, a social networking service which gives its
subscribers the ability to: 1) rate resources with
respect to four criteria: trustworthiness, vendor
reliability, privacy, and child safety; 2) specify
preferences determining whether the browser should
block access to a given resource, or should simply
return a warning message on the basis of the specified
rating. Despite the existence of some similarities, the
approach adopted by MyWOT is quite different from
ours. In particular, it supports filtering criteria which
are far less flexible than the ones of Filtered Wall
since they are only based on the four above-
mentioned criteria. Moreover, no automatic
classification mechanism is provided to the end user.
Our work is also inspired by the many access control
models and related policy languages and enforcement
mechanisms that have been proposed so far for OSNs,
since filtering shares several similarities with access
control. Actually, content filtering can be considered
as an extension of access control, since it can be used
both to protect objects from unauthorized subjects,
and subjects from inappropriate objects. In the field
of OSNs, the majority of access control models
proposed so far enforce topology-based access
control, according to which access control
requirements are expressed in terms of relationships
that the requester should have with the resource
owner. We use a similar idea to identify the users to
which a FR applies. However, our filtering policy
language extends the languages proposed for access
control policy specification in OSNs to cope with the
extended requirements of the filtering domain. Indeed,
since we are dealing with filtering of unwanted
contents rather than with access control, one of the
key ingredients of our system is the availability of a
description for the message contents to be exploited
by the filtering mechanism. In contrast, no one of the
access control models previously cited exploit the
content of the resources to enforce access control.
Moreover, the notion of BLs and their management
are not considered by any of the above-mentioned
access control models.
III. ANALYSIS OF PROBLEM:
The use of effective and appropriate
methods in facilitating projects enhances its
effectiveness and efficiency. The method will be
applied in system analysis and design method where
an existing system is studied to proffer better options
to solving existing problems.
Indeed, today OSNs provide very little
support to prevent unwanted messages on user walls.
For example, Facebook allows users to state who is
allowed to insert messages in their walls (i.e., friends,
friends of friends, or defined groups of friends).
However, no content-based preferences are supported
and therefore it is not possible to prevent undesired
messages, such as political or vulgar ones, no matter
of the user who posts them. However, no content-
based preferences are supported and therefore it is
not possible to prevent undesired messages, no matter
of the user who posts them. Providing this service is
not only a matter of using previously defined web
content mining techniques for a different application,
rather it requires to design ad hoc classification
strategies. This is because wall messages are
constituted by short text for which traditional
classification methods have serious limitations since
short texts do not provide sufficient word occurrences.
IV. PROPOSED WORK:
The aim of the present work is therefore to
propose and experimentally evaluate an automated
system, called Filtered Wall (FW), able to filter
unwanted messages from OSN user walls. We exploit
Machine Learning (ML) text categorization
techniques [2] to automatically assign with each short
text message a set of categories based on its content.
The major efforts in building a robust short text
classifier are concentrated in the extraction and
selection of a set of characterizing and discriminate
features. The solutions investigated in this paper are
an extension of those adopted in a previous work [13]
from which we inherit the learning model and the
elicitation procedure for generating pre-classified
data.
The original set of features, derived from
endogenous properties of short texts, is enlarged here
including exogenous knowledge related to the
context from which the messages originate. As far as
the learning model is concerned, we confirm in the
current paper the use of neural learning which is
today recognized as one of the most efficient
solutions in text classification [2]. In particular, we
base the overall short text classification strategy on
Radial Basis Function Networks (RBFN) for their
proven capabilities in acting as soft classifiers, in
managing noisy data and intrinsically vague classes.

Moreover, the speed 2 in performing the learning
phase creates the premise for an adequate use in OSN
domains, as well as facilitates the experimental
evaluation tasks.
4.1 System Architecture:
Fig 1: Filtered Wall Conceptual architecture
The architecture in support of OSN services
is a three-tier structure (Figure 1). The first layer,
called Social Network Manager (SNM), commonly
aims to provide the basic OSN functionalities (i.e.,
profile and relationship management), whereas the
second layer provides the support for external Social
Network Applications (SNAs).4 The supported SNAs
may in turn require an additional layer for their
needed Graphical User Interfaces (GUIs). According
to this reference architecture, the proposed system is
placed in the second and third layers. In particular,
users interact with the system by means of a GUI to
set up and manage their FRs/BLs. Moreover, the GUI
provides users with a FW, that is, a wall where only
messages that are authorized according to their
FRs/BLs are published. The core components of the
proposed system are the Content-Based Messages
Filtering (CBMF) and the Short Text Classifier (STC)
modules. The latter component aims to classify
messages according to a set of categories. In contrast,
the first component exploits the message
categorization provided by the STC module to
enforce the FRs specified by the user. BLs can also
be used to enhance the filtering process. As
graphically depicted in Fig1, the path followed by a
message, from its writing to the possible final
publication can be summarized as follows:
 Step 1- After entering the private wall of
one of his/her contacts, the user tries to post
a message, which is intercepted by FW.
 Step 2- A ML-based text classifier extracts
metadata from the content of the message.
 Step 3- FW uses metadata provided by the
classifier, together with data extracted from
the social graph and users’ profiles, to
enforce the filtering and BL rules.
 Step 4- Depending on the result of the
previous step, the message will be
published or filtered by FW.
4.2 Filtering rules:
In defining the language for FRs
specification, we consider three main issues that, in
our opinion, should affect a message filtering
decision. First of all, in OSNs like in everyday life,
the same message may have different meanings and
relevance based on who writes it. As a consequence,
FRs should allow users to state constraints on
message creators. Creators on which a FR applies can
be selected on the basis of several different criteria;
one of the most relevant is by imposing conditions on
their profile’s attributes. In such a way it is, for
instance, possible to define rules applying only to
young creators or to creators with a given
religious/political view. Given the social network
scenario, creators may also be identified by
exploiting information on their social graph. This
implies to state conditions on type, depth and trust
values of the relationship(s) creators should be
involved in order to apply them the specified rules.
In general, more than a filtering rule can
apply to the same user. A message is therefore
published only if it is not blocked by any of the
filtering rules that apply to the message creator. Note
moreover, that it may happen that a user profile does
not contain a value for the attribute(s) referred by a
FR (e.g, the profile does not specify a value for the
attribute Hometown whereas the FR blocks all the
messages authored by users coming from a specific
city). In that case, the system is not able to evaluate
whether the user profile matches the FR. Since how
to deal with such messages depend on the considered
scenario and on the wall owner attitudes, we ask the
wall owner to decide whether to block or notify
messages originating from a user whose profile does
not match against the wall owner FRs because of
missing attributes.
4.3 Online setup assistant for FRs threshold:
We address the problem of setting
thresholds to filter rules, by conceiving and
implementing within FW, an Online Setup Assistant
(OSA) procedure. OSA presents the user with a set of
messages selected from the dataset. For each message,
the user tells the system the decision to accept or
reject the message. The collection and processing of
user decisions on an adequate set of messages
distributed over all the classes allows to compute
customized thresholds representing the user attitude
in accepting or rejecting certain contents. Such
messages are selected according to the following
process. A certain amount of non neutral messages
taken from a fraction of the dataset and not belonging
to the training/test sets, are classified by the ML in

order to have, for each message, the second level
class membership values.
4.4 Blacklists:
A further component of our system is a BL
mechanism to avoid messages from undesired
creators, independent from their contents. BLs are
directly managed by the system, which should be
able to determine who are the users to be inserted in
the BL and decide when users retention in the BL is
finished. To enhance flexibility, such information are
given to the system through a set of rules, hereafter
called BL rules. Such rules are not defined by the
SNM, therefore they are not meant as general high
level directives to be applied to the whole
community. Rather, we decide to let the users
themselves, i.e., the wall’s owners to specify BL
rules regulating who has to be banned from their
walls and for how long. Therefore, a user might be
banned from a wall, by, at the same time, being able
to post in other walls.
Similar to FRs, our BL rules make the wall
owner able to identify users to be blocked according
to their profiles as well as their relationships in the
OSN. Therefore, by means of a BL rule, wall owners
are for example able to ban from their walls users
they do not directly know (i.e., with which they have
only indirect relationships), or users that are friend of
a given person as they may have a bad opinion of this
person. This banning can be adopted for an
undetermined time period or for a specific time
window. Moreover, banning criteria may also take
into account users’ behavior in the OSN. More
precisely, among possible information denoting users’
bad behavior we have focused on two main measures.
The first is related to the principle that if within a
given time interval a user has been inserted into a BL
for several times, say greater than a given threshold,
he/she might deserve to stay in the BL for another
while, as his/her behavior is not improved. This
principle works for those users that have been already
inserted in the considered BL at least one time. In
contrast, to catch new bad behaviors, we use the
Relative Frequency (RF) that let the system be able to
detect those users whose messages continue to fail
the FRs. The two measures can be computed either
locally, that is, by considering only the messages
and/or the BL of the user specifying the BL rule or
globally, that is, by considering all OSN users walls
and/or BLs.
Advantages Of proposed System:
 A system to automatically filter unwanted
messages from OSN user walls on the basis
of both message content and the message
creator relationships and characteristics.
 The current paper substantially extends for
what concerns both the rule layer and the
classification module.
 Major differences include, a different
semantics for filtering rules to better fit the
considered domain, an online setup
assistant (OSA) to help users in FR
specification, the extension of the set of
features considered in the classification
process, a more deep performance
evaluation study and an update of the
prototype implementation to reflect the
changes made to the classification
techniques.
V. APPLICATION
 Filtering the unwanted messages enforces
protection and productivity policies for
businesses, schools, and libraries to reduce
legal and privacy risks while minimizing
administration overhead. Filtering provides
network administrators with greater control by
automatically and transparently enforcing
acceptable use policies.
 The Content filtering is designed to control
what content may or may not be viewed by a
reader, especially when used to restrict
material delivered over the Internet via the
Web, e-mail, or other means. Restrictions can
be applied at various levels:
 A government can attempt to apply them
nationwide.
 An employer to its personnel.
 A school to its teachers and/or students.
 A library to its patrons and/or staff.
 An individual to his or her own computer.
Conclusion
In this paper, we have presented a system to
filter out undesired messages from OSN walls. The
system exploits a ML soft classifier to enforce
customizable content-depended filtering rules.
Moreover, the flexibility of the system in terms of
filtering options is enhanced trough the management
of BLs. the system can automatically take a decision
about the messages blocked because of the tolerance,
on the basis of some statistical data (e.g., number of
blocked messages from the same author, number of
times the creator has been inserted in the BL) as well
as data on creator profile (e.g., relationships with the
wall owner, age, sex). As future work, we intend to
exploit similar techniques to infer BL and filtering
rules. As future work, we intend to exploit similar
techniques to infer BL and filtering rules.

References:
[1] Marco Vanetti, Elisabetta Binaghi, Elena
Ferrari, Barbara Carminati, Moreno
Carullo Department of Computer Science and
Communication University of Insubria 21100
Varese, Italy
[2] F. Sebastiani, “Machine learning in
automated text categorization,” ACM
Computing Surveys, vol. 34, no. 1, pp. 1–47,
2002.
[3] N. J. Belkin and W. B. Croft, “Information
filtering and information retrieval: Two sides
of the same coin?” Communications of the
ACM, vol. 35, no. 12, pp. 29–38, 1992.
[4] P. J. Hayes, P. M. Andersen, I. B. Nirenburg,
and L. M. Schmandt, “Tcs: a shell for
content-based text categorization,” in
Proceedings of 6th IEEE Conference on
Artificial Intelligence Applications (CAIA-
90). IEEE Computer Society Press, Los
Alamitos, US, 1990, pp. 320–326.
[5] G. Amati and F. Crestani, “Probabilistic
learning for selective dissemination of
information,” Information Processing and
Manage- ment, vol. 35, no. 5, pp. 633–654,
1999.
[6] A. Adomavicius, G.and Tuzhilin, “Toward
the next generation of recommender systems:
A survey of the state-of-the-art and possible
extensions,” IEEE Transaction on Knowledge
and Data Engineering, vol. 17, no. 6, pp.
734–749, 2005.
[7] M. J. Pazzani and D. Billsus, “Learning and
revising user profiles: The identification of
interesting web sites,” Machine Learning, vol.
27, no. 3, pp. 313–331, 1997.
[8] R. J. Mooney and L. Roy, “Content-based
book recommending using learning for text
categorization,” in Proceedings of the Fifth
ACM Conference on Digital Libraries. New
York: ACM Press, 2000, , pp. 195–204.
[9] Y. Zhang and J. Callan, “Maximum
likelihood estimation for filtering thresholds,”
in Proceedings of the 24th Annual
International ACM SIGIR Conference on
Research and Development in Information
Retrieval, 2001, pp. 294–302.
[10] B. Sriram, D. Fuhry, E. Demir, H.
Ferhatosmanoglu, and M. Demir- bas, “Short
text classification in twitter to improve
information filtering,” in Proceeding of the
33rd International ACM SIGIR Conference
on Research and Development in Information
Retrieval, SIGIR 2010, 2010, pp. 841–842.
[11] B. Sriram, D. Fuhry, E. Demir, H.
Ferhatosmanoglu, and M. Demir- bas, “Short
text classification in twitter to improve
information filtering,” in Proceeding of the
33rd International ACM SIGIR Conference
on Research and Development in Information
Retrieval, SIGIR 2010, 2010, pp. 841–842.
[12] J. Golbeck, “Combining provenance with
trust in social networks for semantic web
content filtering,” in Provenance and
Annotation of Data, ser. Lecture Notes

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