1CP_RT_J027 Filtering Unwanted Messages in Online Social Networking 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.
MODULES:
1.
OSN User module
2.
Filtering process module
3.
Online
setup assistant module
4.
Blacklisting
process
5.
Admin module
MODULES
DESCRIPTION:
1.
OSN User Module:
In this module, users can create and manage their
own “groups” (such like the new Face book groups pages). Each group has a
homepage that provides a place for subscribers to post and share (by posting
messages, images, etc.) and a block that provides basic information about the
group. Users can also enable additional features in their owned page like view
friends list and add friends by using friend’s requests as well as share their
images with selected group’s members. The status of their friends requests are
also updated in this module
2.
Filtering
Process Module:
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.
3.
Online setup assistant module:
In this module, we address the problem of
setting thresholds to filter rules, by conceiving and implementing within FW,
an Online Setup Assistant (OSA) procedure. 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 computing 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.
Blacklisting Process module:
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 is 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.
5. OSN Admin Module:
In this module, the admin manage all user’s
information including posting comments in the user status box. Each unwanted
message has an alert from admin that provides a place for post and share for
the respective user walls. And admin can see blocked message from the users and
also that provides information about the user who used the blocked message.
Admin can also enable additional features in their owned page like user list,
adding unwanted message, update unwanted messages, Blocked users list and
finally filter performance graph. And also in this module, we show the
performance evaluation of the system in the graph.
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/J2EE
ü Java Version :
JDK 1.6 & above.
ü DATABASE :
MYSQL
ü Tool :
Netbeans IDE 7.2.1
CONTACT US
1 CRORE PROJECTS
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Tamin Nadu, INDIA - 600 026
Email id: 1croreprojects@gmail.com
website:1croreprojects.com
Phone : +91 97518 00789 / +91 72999 51536
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