A system to filter unwanted messages from the

A System To Filter
Unwanted Messages
From The OSN User
Walls
Presented by DINESH GANAPATHI
Under The Guidance of Dr. T.SWARNA LATHA

Outline
• Introduction
• Related Work
• Filtered Wall Architecture
• Filtering Rules & Blacklist Management

Introduction
 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.
 whereas more than 30 billion pieces of content (web links, news stories, blog posts, notes,
photo albums, etc.) are shared each month.

Problem Raised
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.

Information
Filtering

OSN
Short Text
Classification

Policy-based
Personalization

Information Filtering
 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.

Short Text Classification
The major efforts in building a robust short text classifier are concentrated in
the extraction and selection of a set of characterizing and discriminant
features. 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 [4].
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.

Policy-based Personalization
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 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 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.

Modules
Filtering Rules
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

OL Setup for FR’sThresholds

Blacklist

OSA presents the user with a set of
messages selected from the dataset
discussed in Section VI-A. 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

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

Present Architecture
 The architecture in support of OSN services is
a two-tier Structure.
 Graphical User Interfaces
 Social Network Manager

Proposed New Architecture
 The architecture in support of OSN services is a threetier Structure.
1. The first layer, called Social Network Manager (SNM),
commonly aims to provide the basic OSN functionalities (i.e.,
profile and relationship management).
2. The second layer provides the support for external Social
Network Applications (SNAs).
3. The supported SNAs may in turn require an additional layer for
their needed Graphical User Interfaces (GUIs).

Filtering System

Words

Blacklist

Violence

Vulgarity

Sexually
Explicit

Post

Advantages
1

The blacklist guarantees 100% filtering of messages coming from
suspicious sources.

2

The process of detecting and filtering spam is transparent,
regulated by standards and fairly reliable.

3

Flexibility, and the possibility to fine-tune the settings. Rarely
make mistakes in distinguishing spam from legitimate messages.

4

Automatically Done.

5

Individual setting.

Title and Content Layout with Chart

6
5
4
3
2
1

0
Category 1

Category 2
Series 1

Category 3
Series 2

Series 3

Category 4

Two Content Layout with Table
• First bullet point here
• Second bullet point here
• Third bullet point here

Group A

Group B

Class 1

82

95

Class 2

76

88

Class 3

84

90

Group
A

Group
D

Group
Title

Group
C

Group
B

A system to filter unwanted messages from the

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