A SYSTEM TO FILTER UNWANTED MESSAGES FROM OSN USER WALLS

MAIN PROJECT PHASE-I Submitted by BALA A.V.p(11BCS010) JAGANAATHAN M. (11BCS035) MAHESWARI E. (11BCS048) ANBU SELVAN D. (12BCS303)

In partial fulfillment for the award of the degree Of Bachelor of Engineering In Computer Science and Engineering Dr. Mahalingam College of Engineering and Technology Pollachi - 642003 An Autonomous Institution Affiliated to Anna University, Chennai - 600 025 JAN 2015

Dr. Mahalingam College of Engineering and Technology Pollachi - 642003 An Autonomous Institution Affiliated to Anna University, Chennai - 600 025

BONAFIDE CERTIFICATE Certified that this main project report, “A SYSTEM TO FILTER UNWANTED MESSAGES FROM OSN USER WALLS” is the bonafide work of

BALA A.V. (11BCS010) JAGANAATHAN M. (11BCS035) MAHESWARI E. (11BCS048) ANBU SELVAN D. (12BCS303)

Who carried out the project work under my supervision.

Prof. Gowri Shankar A. HEAD OF THE DEPARTMENT Computer Science and Engineering Dr. Mahalingam College of Engineering and Technology, NPT-MCET Campus Pollachi – 642003 India

Kanagasabapathy T., ME., SUPERVISOR Assistant Professor Computer Science and Engineering Dr. Mahalingam College of Engineering and Technology, NPT-MCET Campus Pollachi – 642003 India

Submitted for the Autonomous End Semester Examination Main Project Viva-voce held on _______________________

INTERNAL EXAMINER

EXTERNAL EXAMINER

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A SYSTEM TO FILTER UNWANTED MESSAGES FROM OSN USER WALLS ABSTRACT The 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, and audio and video data. These types of data can stored and retrieved in real time cloud using Google app engine. 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. Our project proposes a content-based message filtering conceived as a key service for On-line Social Networks (OSNs). The system allows OSN users to have a direct control on the messages posted on their walls.

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ACKNOWLEDGEMENT First and foremost, we wish to express our deep unfathomable feeling, gratitude to our institution and our department for providing us a chance to fulfill our long cherished of becoming Computer Science engineers. We express our sincere thanks to our honorable Secretary Prof.C.Ramaswamy M.E.,F.I.V., for providing us with required amenities. We sincerely thank our director Dr.S.Vijayarangan M.E.,Ph.D.,F.I.E., for his moral support and encouragement for our project. We wish to express our hearty thanks to Dr.M.Ramakrishnan M.E.,Ph.D., Principal of our college, for his constant motivation and continual encouragement regarding our project work. We thank Dr.A.Rathinavelu M.Tech.,Ph.D.,MISTE.,MCSI.,MACM., Vice Principal of our college for his constant encouragement regarding our project work. We are grateful to Prof. A.Gowrishankar M.Tech., Head of the Department, Computer Science and Engineering, for his direction delivered at all times required. We also thank him for his tireless and meticulous efforts in bringing out this project to its logical conclusion. We are very grateful to project coordinator Ms. Priya V. M.E., Assistant professor, Department of computer science for her excellent guidance and support. Our hearty thanks to our guide Mr. T.Kanagasabapathy ME., Assistant professor for his constant support and guidance offered to us during the course of our project by being one among us and all the noble hearts that gave us immense encouragement towards the completion of our project.

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LIST OF ABBREVIATIONS (in alphabetic order) BL

: Blacklists

FR

: Filtering Rules

FW

: Filter Wall

ML

: Machine Learning

OSA : Online Setup Assistant OSN

: Online Social Network

RBFN : Radial Basis Function Networks

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TABLE OF CONTENTS

1.

INTRODUCTION ......................................................................................................................1

2.

OBJECTIVE ..............................................................................................................................2

3.

LITERATURE REVIEW ..............................................................................................................3

3.1

PRIVACY WIZARDS FOR SOCIAL NETWORKING SITES ........................................................3

3.2

A MACHINE LEARNING APPROACH TO WEB PAGE FILTERING USING CONTENT ..............3

3.3

AN ONLINE DOCUMENT CLUSTERING TECHNIQUE FOR SHORT WEB CONTENTS .............4

3.4

TOWARD THE NEXT GENERATION OF RECOMMENDER SYSTEMS.....................................4

3.5

QUALITY-DRIVEN INFORMATION FILTERING USING THE WIQA POLICY FRAMEWORK .....4

4.

SOFTWARE REQUIREMENTS ..................................................................................................6

4.1

FRONT END ........................................................................................................................6

4.2

BACK END ...........................................................................................................................6

5.

EXISTING SYSTEM ...................................................................................................................7

6.

PROPOSED SYSTEM ................................................................................................................8

7.

SYSTEM ARCHITECTURE .........................................................................................................9

8.

MODULES ............................................................................................................................ 10

8.1

USER REGISTRATION AND POSTING MESSAGES ON USER WALL ................................... 10

8.1.1

LOGIN FORM ............................................................................................................... 10

8.1.2

USER REGISTRATION ................................................................................................... 11

8.1.3

CHAT WINDOW .......................................................................................................... 12

8.1.4

POSTING MESSAGES ON OSN USER WALL ................................................................. 12

8.2

CONTENT BASED MESSAGE FILTERING IN FILTERED WALL ............................................ 13

8.3

ANALYZING THE MESSAGE BY EXPERTS .......................................................................... 13

8.4

APPLYING FILTERING RULES AND PREVENT UNWANTED MESSAGES ............................ 13

9.

EXPECTED OUTCOME .......................................................................................................... 15

10. REFERENCES ........................................................................................................................ 16 APPENDIX

A:

CODE .......................................................................................................... A.1

A.1 SOURCE CODE FOR FORM ................................................................................................. A.1 A.2 SOURCE CODE FOR ADMIN ............................................................................................... A.6

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LIST OF FIGURES Figure 1: Architecture Diagram ......................................................................................................9 Figure 2: User Login .................................................................................................................... 11 Figure 3: Registration Form ........................................................................................................ 11 Figure 4: OSN Wall ...................................................................................................................... 12 Figure 5: Posting Message .......................................................................................................... 13

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1.

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. According to Facebook statistics 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. 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).

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2.

OBJECTIVE One fundamental issue in today On-line 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. Our project proposes a system enforcing content-based message filtering conceived as a key service for On-line Social Networks (OSNs). The system 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 producing membership labels in support of contentbased filtering.

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3.

LITERATURE REVIEW

3.1

PRIVACY WIZARDS FOR SOCIAL NETWORKING SITES A template for designing a social network privacy wizard. The intuition for the

design comes from the observation that real users conceive their privacy preferences (which friends should be able to see which information) based on an implicit set of rules. Thus, with a limited amount of user input, it is usually possible to build a machine learning model that concisely describes a particular user's preferences, and then use this model to configure the user's privacy settings automatically. As an instance of this general framework, we have built a wizard based on an active learning paradigm called uncertainty sampling. The wizard iteratively asks the user to assign privacy "labels" to selected ("informative") friends, and it uses this input to construct a classifier, which can in turn be used to automatically assign privileges to the rest of the user's (unlabeled) friends. It has been focused on the privacy risks associated with publishing de-identified social network graphs for research. Even if all profile information is removed, it is often possible to re-identify individuals in the published data simply based on unique graph topologies. The problem, which is focused of this paper, is to make sure that Alice can even express this preference to the social networking site. However, even if the site hides Alice’s political affiliation, it may still be possible for an attacker to infer the hidden information

3.2

A MACHINE LEARNING APPROACH TO WEB PAGE FILTERING USING CONTENT This paper reports how to filter out irrelevant documents from a set of documents

collected from the Web. A machine-learning-based approach that combines Web content analysis and Web structure analysis. Each Web page contains set of contentbased and link-based features, which can be used as the input for various machine learning algorithms. 3

3.3

AN ONLINE DOCUMENT CLUSTERING TECHNIQUE FOR SHORT WEB CONTENTS Document clustering techniques have been applied in several areas, with the web as

one of the most recent and influential. Both general-purpose and text-oriented techniques exist and can be used to cluster a collection of documents in many ways. This work proposes a novel heuristic online document clustering model that can be specialized with a variety of text-oriented similarity measures. An experimental evaluation of the proposed model was conducted in the e-commerce domain. Performances were measured using a clustering-oriented metric based on FMeasure and compared with those obtained by other well-known approaches. The obtained results confirm the validity of the proposed method both for batch scenarios and online scenarios where document collections can grow over time.

3.4

TOWARD THE NEXT GENERATION OF RECOMMENDER SYSTEMS This paper presents an overview of the field of recommender systems and describes

the current generation of recommendation methods that are usually classified into the following

three

main

categories:

content-based,

collaborative,

and

hybrid

recommendation approaches. This paper also describes various limitations of current recommendation methods and discusses possible extensions that can improve recommendation capabilities and make recommender systems applicable to an even broader range of applications.

3.5

QUALITY-DRIVEN INFORMATION FILTERING USING THE WIQA POLICY FRAMEWORK Web-based information systems, such as search engines, news portals, and

community sites, provide access to information originating from numerous information providers. The quality of provided information varies as information providers have different levels of knowledge and different intentions. Users of web-based systems are therefore

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confronted with the increasingly difficult task of selecting high-quality information from the vast amount of web-accessible information. Online communities like MySpace, Facebook, Flickr, or YouTube are used by large numbers of information providers to share information. The quality of provided information varies widely, and again the amount of accessible information blurs relevant information.

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4.

SOFTWARE REQUIREMENTS

4.1

FRONT END Java Platform, Enterprise Edition or Java EE is Oracle's enterprise Java computing

platform. The platform provides an API and runtime environment for developing and running enterprise software, including network and web services, and other large-scale, multi-tiered, scalable, reliable, and secure network applications. Java EE extends the Java Platform, Standard Edition (Java SE), providing an API for object-relational mapping, distributed and multi-tier architectures, and web services. The platform incorporates a design based largely on modular components running on an application server. Software for Java EE is primarily developed in the Java programming language. J2EE is a platform-independent, Java-centric environment from Sun for developing, building and deploying Web-based enterprise applications online.

4.2

BACK END MySQL is a relational database management system (RDBMS), and ships with no

GUI tools to administer MySQL databases or manage data contained within the databases. Users may use the included command line tools, or use MySQL "front-ends", desktop software and web applications that create and manage MySQL databases, build database structures, back up data, inspect status, and work with data records. MySQL is a popular choice of database for use in web applications, and is a central component of the widely used LAMP open source web application software stack (and other ‘AMP' stacks).LAMP is an acronym for "Linux, Apache, MySQL, Perl/PHP/Python." Free-software-open source projects that require a full-featured database management system often use MySQL.

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5.

EXISTING SYSTEM 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. 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. Limitations: 

Existing Filtering rules are not fit to the social networks.



Difficulties in defining the robust features essentially due to the fact that the description of the short text is concise, with many misspellings, nonstandard terms, and noise.



Quality of text classification is not efficient

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6.

PROPOSED SYSTEM The aim of the present work is 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 exchanging several types of content, including free text, image, and audio and video data can stored and retrieved in real time cloud using Google app engine. The RBFN categorizes short messages as Neutral and Non-neutral. 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. 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: 

Short text classification based on Machine Learning is efficient in social networks. Our techniques used well on large documents and complex data.



Online setup assistant (OSA) to help users in FR specification. This set of features is used in the classification process.

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7.

SYSTEM ARCHITECTURE User should login to their account to post the messages on user wall. In order to

post the messages, customize the filtering criteria to be applied to their walls, and a Machine Learning based soft classifier. The classification is based on either neutral or non-neutral. If the message is neutral it is directly posted on the wall. If it is non-neutral experts classification is applied on the messages. Based on the experts output it checks the relationship between them and the messages may be posted or it may blocked.

Figure 1: Architecture Diagram

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8.

8.1

MODULES 

User Registration and Posting Messages on User Wall.



Content Based Message Filtering in Filtered Wall.



Analyzing the message by experts.



Applying Filtering Rules and prevent unwanted messages.

USER REGISTRATION AND POSTING MESSAGES ON USER WALL Users of the social network give their profile details to the network at the creation

time. So every user gives their name, gender, profession, mobile number etc. This system will maintain the user’s profile details in database. After registration process user connect with their friends by friend request. Some User may post unwanted messages on another user’s wall. To avoid such problems we have implemented filtering technique to filter unwanted messages posted on user walls.

8.1.1 LOGIN FORM Initially, if users already have an account they need to give their corresponding user name and password to login. The specified username and password will check with the database for the authentication purpose. In case of authentication fails, there are prompt messages to indicate error message. User has to give their corresponding username and password. User can do signup with the option provided and register for their new account.

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Figure 2: User Login

8.1.2 USER REGISTRATION Suppose if user have no account then they can sign up by creating a new account. By clicking registration, User can able to fill the form with the specified fields such as Name, User Name, Password, City, and E-mail. Click Register button for account confirmation. If the user account has been created then user can login with their new username and password. Then the OSN wall will get displayed to the user.

Figure 3: Registration Form

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8.1.3 CHAT WINDOW After user have login to their account, OSN window will get displayed to the user. Using the search option the user can find their friends based on the details such as username and they can give request to their friends from the results. These requests can be seen by those friends in their chat window in the form of notifications. If the request is accepted, the notification of request acceptance is sent to the person who has actually sent the request. The friends who have been accepted the requests and for whom the user has accepted are displayed in the “Friends list”.

Figure 4: OSN Wall

8.1.4 POSTING MESSAGES ON OSN USER WALL We can upload images and post comments for those images on the OSN user wall. The particular action can be performed by the upload option from the left panel. Once the image is uploaded the image can be viewed in home. A text box appears below the image in order to post the comments about that image.

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Figure 5: Posting Message

8.2

CONTENT BASED MESSAGE FILTERING IN FILTERED WALL The Filtered wall is used to filter the unwanted messages. Machine Learning is the

text classifier which is used to classify the messages and short texts posted by users. Documents processed in content-based filtering are mostly textual in nature and this makes content-based filtering close to text classification. Machine learning techniques classify the message into neutral and non-neutral. After that non-neutral messages are further divided into several classes.

8.3

ANALYZING THE MESSAGE BY EXPERTS To analyze the results of the Machine Learning technique, the messages are

evaluated by our experts. Our experts evaluate the non-neutral messages under Violence, politics, offensive, hate, sexual harassment categories. Issues regarding the consistency between the opinions of experts are considered. Bag of Words, contextual features yield good performance in text categorization. We consider the experts overall accuracy and consistency in analyzing messages and stored the results.

8.4

APPLYING FILTERING RULES AND PREVENT UNWANTED MESSAGES User’s details are stored in real time cloud using Google app engine database.

Before posting the message filtering rules checking the relation between the message

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creator and receiver and also the profile creation date etc., some users are temporary blacklist because of their activities. The Machine learning output and experts analyzing results regarding to a user’s unwanted message, the message will be blocked and display the results in the message creator wall. So, based on content based message filtering by Machine Learning and profile checking process it prevent unwanted messages in a social network.

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9.

EXPECTED OUTCOME As a result user can sign in to their account. If a user does not have an account they

can sign up to create a new account. Then the chat window will be displayed. User can search their friends based on their name, e-mail id and location. After that user can give request to the specific user. If the user accepted their friend request then they can post their commands on OSN wall. Either the commands will be displayed or it may be filtered based on the user specification.

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10. REFERENCES

[1] M. Chau and H. Chen, “A machine learning approach to web page filtering using content and structure analysis,” Decision Support Systems, vol. 44, no. 2, pp. 482–494, 2008. [2] M. Carullo, E. Binaghi, and I. Gallo, “An online document clustering technique for short web contents,” Pattern Recognition Letters,vol. 30, pp. 870–876, July 2009. [3] L. Fang and K. LeFevre, “Privacy wizards for social networking sites,” in Proceedings of the 19th international conference on Worldwide web (WWW 2010). New York, NY, USA: ACM, 2010, pp. 351–360.. [4] Adomavicius, G.andTuzhilin, “Toward the next generation ofrecommender 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. [5] C. Bizer and R. Cyganiak, “Quality-driven information filtering using the wiqa policy framework,” Web Semantics: Science, Services and Agents on the World Wide Web, vol. 7, pp. 1–10, January 2009. [6] R.E. Schapiro and Y. Singer, “Boostexter: a boosting-based system for text categorization,” Machine Learning, vol. 39, no. 2/3, pp. 135– 168, 2000. [7] F. Bonchi and E. Ferrari, Privacy-aware Knowledge Discovery: Novel Applications and New Techniques. Chapman and Hall/CRC Press, 2010

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APPENDIX

A:

CODE

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A.1

31: inp=$(_.targets,this), 32: defVal=inp.val(), 33: trueVal=(function(){ 34: var tmp=inp.is('input')?(tmp=label.html().match(/value=['"](.+?)['"].+/),!!tmp &&!!tmp[1]&&tmp[1]):inp.html() 35: return defVal==''?defVal:tmp 36: })() 37: trueVal!=defVal 38: &&inp.val(defVal=trueVal||defVal) 39: label.data({defVal:defVal}) 40: inp 41: .bind('focus',function(){ 42: inp.val()==defVal 43: &&(inp.val(''),_.hideEmptyFu(label),label.removeClass(_.invalidCl)) 44: }) 45: .bind('blur',function(){ 46: _.validateFu(label) 47: if(_.isEmpty(label)) 48: inp.val(defVal) 49: ,_.hideErrorFu(label.removeClass(_.invalidCl)) 50: }) 51: .bind('keyup',function(){ 52: label.hasClass(_.invalidCl) 53: &&_.validateFu(label) 54: }) 55: label.find('.'+_.errorCl+',.'+_.emptyCl).css({display:'block'}).hide() 56: }) 57: _.success=$('.'+_.successCl,_.form).hide() 58: }, 59: isRequired:function(el){ 60: return !el.hasClass(_.notRequiredCl) 61: }, 62: isValid:function(el){ 63: var ret=true 64: $.each(_.rx,function(k,d){ 65: if(el.is(k)) 66: ret=d.rx.test(el.find(d.target).val())

A.2

67: }) 68: return ret 69: }, 70: isEmpty:function(el){ 71: var tmp 72: return (tmp=el.find(_.targets).val())==''||tmp==el.data('defVal') 73: }, 74: validateFu:function(el){ 75: el.each(function(){ 76: var th=$(this) 77: ,req=_.isRequired(th) 78: ,empty=_.isEmpty(th) 79: ,valid=_.isValid(th) 80: if(empty&&req) 81: _.showEmptyFu(th.addClass(_.invalidCl)) 82: else 83: _.hideEmptyFu(th.removeClass(_.invalidCl)) 84: if(!empty) 85: if(valid) 86: _.hideErrorFu(th.removeClass(_.invalidCl)) 87: else 88: _.showErrorFu(th.addClass(_.invalidCl)) 89: }) 90: }, 91: getValFromLabel:function(label){ 92: var val=$('input,textarea',label).val() 93: ,defVal=label.data('defVal') 94: return label.length?val==defVal?'nope':val:'nope' 95: } 96: ,submitFu:function(){ 97: _.validateFu(_.labels) 98: if(!_.form.has('.'+_.invalidCl).length) 99: $.ajax({ 100:

type: "POST",

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name:_.getValFromLabel($('.name',_.form)),

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A.3

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phone:_.getValFromLabel($('.phone',_.form)),

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fax:_.getValFromLabel($('.fax',_.form)),

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state:_.getValFromLabel($('.state',_.form)),

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stripHTML:_.stripHTML

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A.4

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hideEmptyFu:function(label){

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init:function(){

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A.5

A.2 SOURCE CODE FOR ADMIN 1: 2: 3: 4: 5: 6: 7: 8: 9: 10: 11: 12: 16: 44:

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A.11