A System to Filter Unwanted Messages
from OSN User Walls
ABSTRACT:
One fundamental issue in today’s Online Social
Networks (OSNs) 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, in this
paper, we propose a system allowing OSN users to have a direct control on the
messages posted on their walls. This is achieved through a flexible rule-based
system, which allows users to customize the filtering criteria to be applied to
their walls, and a Machine Learning-based soft classifier automatically
labeling messages in support of content-based filtering.
EXISTING SYSTEM:
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.
DISADVANTAGES
OF EXISTING SYSTEM:
·
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
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.
PROPOSED SYSTEM:
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 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 (STC) are concentrated in the extraction and selection of a set of
characterizing and discriminant features. The solutions investigated in this
paper are an extension of those adopted in a previous work by us from which we
inheritthe learning model and the elicitation procedure for generating
preclassified 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. 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 in performing the learning phase creates the
premise for an adequate use in OSN domains, as well as facilitates the
experimental evaluation tasks. We
insert the neural model within a hierarchical two level classification
strategy. In the first level, the RBFN categorizes short messages as Neutral
and Nonneutral; in the second stage, Nonneutral messages are classified
producing gradual estimates of appropriateness to each of the considered
category. Besides classification facilities, the system provides a powerful
rule layer exploiting a flexible language to specify Filtering Rules (FRs), by
which users can state what contents, should not be displayed on their walls.
FRs can support a variety of different filtering criteria that can be combined and
customized according to the user needs. More precisely, FRs exploit user
profiles, user relationships as well as the output of the ML categorization
process to state the filtering criteria to be enforced. In addition, the system
provides the support for user-defined Blacklists (BLs), that is, lists of users
that are temporarily prevented to post any kind of messages on a user wall.
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.
SYSTEM ARCHITECTURE:
SYSTEM CONFIGURATION:-
HARDWARE CONFIGURATION:-
ü Processor - Pentium –IV
ü Speed - 1.1
Ghz
ü RAM - 256
MB(min)
ü Hard Disk -
20 GB
ü Key Board -
Standard Windows Keyboard
ü Mouse - Two
or Three Button Mouse
ü Monitor - SVGA
SOFTWARE CONFIGURATION:-
ü Operating System : Windows XP
ü Programming Language :
JAVA
ü Java Version :
JDK 1.6 & above.
ü DATABASE :
MYSQL
ü Tool :
Netbeans IDE 7.0
REFERENCE:
Marco Vanetti, Elisabetta Binaghi, Elena
Ferrari, Barbara Carminati, and Moreno Carullo “A System to Filter Unwanted
Messages from OSN User Walls”- IEEE
TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, VOL. 25, NO. 2, FEBRUARY 2013.
FUTURE
WORK / OUR CONTRIBUTION:
As the future work and our contribution
we enhance the system by creating a instance randomly notifying a message
system that should instead be blocked, or detecting modifications to profile
attributes that have been made for the only purpose of defeating the filtering
system. Automatically user will get a mail notification.