Presentation of the filtered wall concept

7/26/2019 Presentation of the filtered wall concept

1/26

IMPLEMENTING FILTERED WALL IN ONLINE SOCIALNETWORKING SITE

SUPERVISOR

Mr.DHANASEKARAN S

Assistant professor

Department of Computer Science andEnineerin

DONE !"

MANAS" M#$%%&%%%'(')&* NIVEDHI+HA R#$%%&%%%'(',$* RAN-INI PRI"A R#$%%&%%%'(%%'*


2/26

ABSTRACT

The major issue in today's On-line Social Networking is to give users the

ability to control the messages being posted on their own private wall and

to avoid that unwanted messages being posted. Online social networking

provide little support to this requirement. e propose a system allowing

online social networking users to have a direct control on the messages

posted on their walls. This is achieved through a !le"ible system# that

allows users to customi$e the !iltering criteria to be applied to their

walls.The present work is to e"perimentally evaluate an automated system

called %iltered all#able to !ilter unwanted messages !rom user wall.


3/26

PROBLEM STATEMENT

&s more and more people are spending increasing amounts o! time on

social networking sites there is a growing concern !or the privacy and legalrights surrounding them.

This work provides a comprehensive solution !or the privacy# and

security trends associated with social media.

ut# like anything# as social networking sites become more popular

the risks that stem !rom them increases and the need !or new and updated

security becomes necessary.

These sites also state that they will not notice or compensate the user

i! they choose to take actions on their submitted content. (n order to

minimi$e the risks associated with this control o! a social networking site#

users should review and should take caution in what they post.


4/26

EXISTING SYSTEM

) Today# online social networking provide very little support to prevent

unwanted messages on user walls. %or e"ample# %ace book allows users to

state who is allowed to insert messages in their walls *i.e.# !riends# !riends

o! !riends# or de!ined groups o! !riends+.

) (t is not possible to prevent undesired messages# no matter o! the user who

posts them.

)

No content based pre!erences are supported and there!ore it is not possible

to prevent undesired messages such as political or vulgar ones# no matter o!

the user who posts them.


5/26

DISADVANTAGES OF EXISTING SYSTEM

) No content-based pre!erences are supported and there!ore it is not posible

to prevent undesired messages# such as political or vulguar ones#no matter

o! the user who posts them.

)

This is because wall messages are constituted by short te"t !or which

traditional classi!ication methods have serious limitations since short te"t

do not provide su!!icient word occurrences


6/26

PROPOSED SYSTEM

) The aim o! the present work is to propose and e"perimentally evaluate an

automated system# called %iltered all# able to !ilter unwanted messages

!rom online social networking user walls..) This system is to automatically !ilter unwanted messages !rom online

social networking user walls on the basis o! message content

characteristics.


7/26

LITERATURE SURVEY

,.Title Content-based Boo Re!o""end#n$ Us#n$ Lea%n#n$ &o% Te't

Cate$o%#(at#on

&uthor aymond /.0ooney # 1oriene oy#&ugust ,222

ecommender systems improve access to relevant products and

in!ormation by making personali$ed suggestions based on previous

e"amples o! a user's likes and dislikes.

&dvantages

This approach has the advantage o! being able to recommended

previously unrated items to users with unique interests using 01.

3isadvantages

4sers have to select productive strategies !or selecting good e"amples .


8/26

5.Title Ma!)#ne Lea%n#n$ #n A*to"ated Te't Cate$o%#(at#on

&uthor %abri$io Sebastiani#October 566,

&utomated categori$ation o! te"ts into prede7ned categories is done by a

general inductive process that automatically builds a classi7er by learning#

!rom a set o! preclassi7ed documents.

&dvantages

The advantages o! this approach over the knowledge engineering approach

are a very good e!!ectiveness# considerable savings in terms o! e"pert

manpower# and straight!orward portability to di!!erent domains.

3isadvantages

Three di!!erent problems namely document representation#classi!ier

construction and classi!ier evaluation.


9/26

8.Title A*to"ated Lea%n#n$ O& De!#s#on R*+es &o% Te't Cate$o%#(at#on

&uthor 9hidanand &pte# %red 3amerau# Sholom 0. eiss#,22:

This method is to automatically discover classi!ication patterns that can be

used !or general document categori$ation or personali$ed !iltering o! !ree

te"t.

&dvantages

Shows a large gain per!ormance.

3isadvantages

4sing dictionaries o! single word does not mean that the best solution

ignores phrases and combinations o! words.


10/26

:.Title Co"b#n#n$ P%o,enan!e #t) T%*st #n so!#a+ Neto%s &o%

Se"ant#! Web Content F#+te%#n$.

&uthor /enni!er ;olbeck

&n algorithm !or in!erring trust relationships using provenance

in!ormation and trust annotations in Semantic eb-based social networks.

&dvantages

%ilm trust is presented as an application and the results obtained with

%ilmTrust illustrate the success that can be achieved using this method.

3isadvantages

Networks are di!!erent.3epending on the subject about which the trust is

being e"pressed#the user communityand e!!ect o! these properties o! trust

can vary.


11/26

, data# to provide

benchmark and a check that corrections to the data did not introduce any new

anomalies.

3isadvantages

The number o! duplicates#!oreign language documents and other anamolies is

problematic and depends on the questions the researchers use.


12/26

SYSTEM ARC0ITECTURE


13/26

MODULES

) LOGIN AUT0ENTICATION AND REGISTRATION

1ogin

The login module presents visitors with a !orm o! username and password

!ields.(! the user enters valid username and password then they will be

granted access to additional resources on the website.

egistration

(t is the ability to create new users. New users have to give their details.

@aving their account gives many !eatures# including more editing options

and user pre!erences.


14/26

Login and Registration:


15/26

) PROFILE GENERATION 1

4serAs pro!ile details like pro!ile name# display picture and status are

entered by the user which gets stored in the database.

&uthori$ed users once logged into their pro!ile can see their details

and user can edit their pro!ile details which gets updated.


16/26

New user Existing User


17/26

) SEND FRIEND RE2UEST

(n this module user select !riend to send request and can later cancel

it i! they wish so.

The other user can accept or deny the !riend request.


18/26

Send Request

Cancel Request

Accept or Ignore Request


19/26

)ACCEPT FRIEND RE2UEST1

(n this module users add new !riends and view their pro!ile details.

1ogged users can see their !riend list and i! they wish can add !riends.

They can post messages in the wall o! the user who has accepted their

!riend request.


20/26

)POST STATUS1

(n this module user can post any post in public wall# and any !riend o!

user can post on the user wall.

(! the posted content is postable message the content gets posted on

the user wall. 4ser can view their recent post and can remove it i! they wish so.


21/26

View Recent posts Posted bt!e user

Posting in User"s #riend"s wallPosting in User"s wall


22/26

) FILTERING TE3T 1

This module manages posting comments in the user status bo".

Bach non postable content has an alert meassage denying the posting

o! message in user's wall.


23/26

Posting Content #iltering t!e Post


24/26

CONCLUSION

(n this work# we have presented a system to 7lter undesired messages !rom

OSN walls. The system e"ploits a so!t classi7er to en!orce customi$able

content-dependent !iltering method. This work is the !irst step o! a wider

project .The early encouraging results we have obtained on the

classi7cation procedure prompt us to continue with other work that will

aim to improve the quality o! classi7cation.


25/26

FUTURE WORK

(n particular# !uture plans contemplate a deeper investigation on two

interdependent tasks. The 7rst concerns the e"traction andCor selection o!

conte"tual !eatures that have been shown to have a high discriminative

power. The second task involves the learning phase. Since the underlying

domain is dynamically changing# the collection o! pre-classi7ed data may

not be representative in the longer term.. &dditionally# we plan to enhance

our system with a more sophisticated approach to decide when a user

should be inserted into a 1.


26/26

THANK YOU

Documents

Presentation of the filtered wall concept