Filter unwanted messages from walls and blocking non legitimate users in osn

International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print),
ISSN 0976 - 6375(Online), Volume 6, Issue 5, May (2015), pp. 47-54© IAEME
47
FILTER UNWANTED MESSAGES FROM WALLS AND
BLOCKING NON-LEGITIMATE USERS IN OSN
Pallavi Shinde1
, Prof. Vishal Mogal2
1,2
Department of Computer Engineering,
RMD Sinhgad School of Engineering, Warje, Pune-58, India
Savitribai Phule Pune University,
ABSTRACT
In recent years, on-line Social Networks (OSNs) became a crucial part of daily life. Users
build particular networks to represent their social relationships. Users are able to upload and share
information associated to their personal lives. The privacy risks of such behaviour are typically
unnoticed. Nowadays OSNs give little or no support to stop unwanted messages on user walls. For
that purpose, a new system is designed to permit OSN users to have a direct control on the messages
posted on their walls. This can be achieved through a flexible rule based system, that allows users to
customise the filtering criteria to be applied to their walls, and a Machine Learning (ML) primarily
based soft classifier mechanically labelling messages in support of content based filtering. The
system make use of a ML soft classifier to enforce customizable content-dependent Filtering Rules
(FRs). And also the flexibility of the system in terms of filtering choices is increased through the
management of Blacklists. Filtered wall is a system to filter undesired messages from OSN walls.
This system approach decides when user should be inserted into a black list. The proposed system
offers security to the On-line Social Networks.
Keywords: Online Social Network (OSN), Machine Learning, Information Filtering, Content- based
Filtering.
1. INTRODUCTION
Today’s life is totally based on Internet. Now a days people cannot imagine life without
Internet. Information and communication technology plays vital role in today’s online networked
society. It has affected the online interaction between different users. Online Social networks (OSNs)
provide platform to meet different users and share information with them. Communication on these
web sites involves exchange of different content like text, image, audio and video. A social network
include personal messaging, blogs, chat facility, photos sharing facility and other ways to share text
and multimedia content. A wall may be a section in online site user profile where others can post
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ISSN 0976 – 6367(Print)
ISSN 0976 – 6375(Online)
Volume 6, Issue 5, May (2015), pp. 47-54
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messages. This OSN wall is a public writing space so others can view what has been written on wall.
So, in online sites there is possibility of posting illegal or undesirable messages on wall which is
visible to others too. There is a requirement to develop a lot of security techniques for different
communication technologies, particularly online social networks. Networking sites provide very
little support to prevent unwanted messages on user walls. Consider for example, facebook permits
users to state who is authorized and who is not authorized to insert messages in their walls i.e.
friends, friends of a friend (FOAF) or defined group of friends.
But there is no any content based preference for messages like sports, politics etc no matter of
the user who post them [8]. So it is impossible to prevent undesired messages. So, with the shortage
of classification or filtering tools, the user receives all messages posted by the users he or she
follows. So disadvantages of existing system are as follows-
LIMITATIONS OF EXISTING SYSTEM
1. However, no content-based preferences are supported and therefore it is not possible to prevent
undesired messages. No matter user who post them.
2. 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.
SYSTEM ARCHITECTURE OF EXISTING SYSTEM
Fig. 1: System Architecture of Existing System

49
So, to control this type of activity and prevent the unwanted messages which are written on user’s
wall we can implement filtering rules (FR) in our system. Also, Black List (BL) will maintain in this
system. In most cases, the user receives a noisy stream of updates from other users.
Our main aim behind this research is to filter unwanted messages from user wall in order to
improve the security and efficiency. The main idea of proposed system is to support for content
based preferences. We utilize the machine learning text categorization techniques based soft
classifier automatically labelling messages with the help of content based filtering. To classify the
text for labelling messages by using the short text classifier (STC). Short text classifier are
concentrated in the extraction and selection of a set of characterizing features.
So, in proposed mechanism, a user can specify what contents should not be displayed on
his/her wall, by specifying a set of filtering rules. FRs are permitted to specify filtering conditions
based on user profiles. In addition, system provides the support for user defined blacklist means list
of users that are temporarily prevented to post any kind of messages on user wall. We have using the
wordNet dictionary in our project which gives the synonyms of a particular word. By using the
wordNet dictionary we have increased the robustness of the system.
2. LITURATURE SURVEY
Our related work has using content-based filtering and policy based personalization to filter
unwanted contents in user’s space.
Content-based filtering
In content based filtering, each user is assumed to operate independently. As a result, a
content-based filtering system selects information items based on the correlation between the content
of the items and the user preferences [1, 8].
P.W. Foltz and S.T. Dumais [1] presented information retrieval (IR) techniques. There is no
any individual based filtering. In our approach, we have used information filtering (IF). Information
retrieval is very closely related to to information filtering. User preferences in information filtering
typically represent long term interest. While queries in information retrieval tend to represent short
term interest. Information filtering is typically applied to stream of incoming data, while in
information retrieval, changes in databases do not occur often. Filtering involves process of
removing information from stream, while IR involves process of finding information in that stream.
Policy based personalization
Policy based personalization is applicable in many different contexts. It adapts a service in
specific context according user defined policies. In online social networking sites user oriented
policies can define how communication between two parties or more can be handled. The policy
based personalization system is focuses on Twitter. It assigns a category to each tweet and shows
only those tweet to the user which are of his interest [2, 8]. Policy based personalization represent the
ability of the user to filter wall messages according to filtering criteria suggested by him.
B. Sriram, D. Fuhry, E. Demir, H. Ferhatosmanoglu, and M. Demirbas [2], this paper
describes classification of tweets into general but important categorizes by using the author
information and features within tweet. But our approach permits setting of FRs according to a variety
of criteria and classify the text to predefined set of classes like sports, politics etc.
L. Fang and K. LeFevre [3], this paper is related to privacy control but no facility for
stopping anyone for posting something on wall. Our approach can stop posting particular categories
as per user’s choice.

International Journal of Computer Engineering and Technology (IJCET), ISSN 0976
ISSN 0976 - 6375(Online), Volume 6, Issue 5, May (2015), pp.
Fong, P.W.L., Anwar, M.M., Zhao, Z.
based, history based. So that it is time consuming.
policy based personalization.
3. METHODOLOGY
The section is divided into following parts.
1) The first section shows the basic idea behind the approach.
2) The second section proposes an algorithm to filter out unwanted messages.
3.1 System Architecture
3.2 Algorithm
1. Post the message.
2. Load the model file in application.
3. Create instance for each category.
4. Classify the message and get the relevance of entered message with each category.
5. Checks for blocking/filtering criteria according
6. If message follows any of the condition then message gets filtered out.
7. Identify the number of attempts over or not.
8. If attempts get over then find the number of days user account
International Journal of Computer Engineering and Technology (IJCET), ISSN 0976
6375(Online), Volume 6, Issue 5, May (2015), pp. 47-54© IAEME
50
Fong, P.W.L., Anwar, M.M., Zhao, Z. [4], this paper describes policies such as topology
. So that it is time consuming. Our approach presents content based filte
The section is divided into following parts.
The first section shows the basic idea behind the approach.
The second section proposes an algorithm to filter out unwanted messages.
Fig.2: System Architecture
Load the model file in application.
Create instance for each category.
Classify the message and get the relevance of entered message with each category.
ng/filtering criteria according to message category, gender,
If message follows any of the condition then message gets filtered out.
Identify the number of attempts over or not.
If attempts get over then find the number of days user account needs to be blocked.
© IAEME
his paper describes policies such as topology
Our approach presents content based filtering and
The second section proposes an algorithm to filter out unwanted messages.
Classify the message and get the relevance of entered message with each category.
gender, age, threshold.
needs to be blocked.

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9. Block the user temporarily for blocking duration.
10. If the user is blocked temporarily by its n/2 friends then block the user permanently.
11. If message not belonging to the blocking criteria then post the message on the OSN wall.
4. RESULTS
4.1 Input Dataset
Post enter by the user is the input data. We have used arff file (Attribute Relationship File
Format). This file contains messages categories as other, politics, bollywood, sports, vulgar,
violence.
4.2 Outcomes
Following snapshots are showing results for practical work done. User name P’s profile as
shown below. Like facebook, our OSN also has options as news feed, requests, friends, find friends,
friend of friend. Also our OSN has options like filtered posts, filter rules.
Fig. 3: User Profile of OSN
In filter rule option, user can set the categories which he/she doesn’t want to display on
his/her profile. User can also set threshold, gender, age etc. Threshold is used for blocking purpose.

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Fig. 4: Filter Rules of OSN
Below snapshot shows that, user name Komal doesn’t want sport category message but some
of her friend post the sport category. So message has popped as sports category is blocked.
Fig. 5: Sports category blocked
Below snapshot shows that, sport category message had been posted 3 times, and threshold
was set as 0.7. Therefore, user got message as “You are blocked for another 5 days.”

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Fig. 6: Temporary Blocking of User
Permanent blocking of user is shown in below snapshot.
Fig. 7: Permanent blocking of user

54
5. CONCLUSION
This paper describes our work to provide unwanted message filtering for social networks. We
have given a system to filter undesired messages from OSN walls. This system approach decides
when user should be inserted into a black list. The system GUI and a set of tools which make
blacklists and filter rules specifications more easy and simple. We have used the wordNet dictionary
in our project which gives the synonyms of a particular word. By using the wordNet dictionary we
have increased the robustness of the system. We would prefer to remark that the system proposed
during this paper represents simply the core set of functionalities required to produce a sophisticated
tool for OSN message filtering.
REFERENCES
1. P.W. Foltz and S.T. Dumais, Personalized Information Delivery: An Analysis of Information
Filtering Methods, Comm. ACM, vol. 35, no. 12, pp. 51-60, 1992.
2. B. Sriram, D. Fuhry, E. Demir, H. Ferhatosmanoglu, and M. Demirbas, “Short Text
Classification in Twitter to Improve Information Filtering,” Proc. 33rd Int’l ACM SIGIR Conf.
Research and Development in Information Retrieval (SIGIR ’10), pp. 841-842, 2010.
3. Fang, L., LeFevre, K., Privacy wizards for social networking sites; In: WWW 10: Proceedings
of the 19th international conference on World Wide Web, pp. 351360. ACM, NewYork, NY,
USA, 2010.
4. Fong, P.W.L., Anwar, M.M., Zhao, Z., A privacy preservation model for facebook-style social
network systems; In: Proceedings of 14th European Symposium on Research in Computer
Security (ESORICS), pp. 303320, 2009.
5. F. Sebastiani, Machine Learning in Automated Text Categorization, ACM Computing Surveys,
vol. 34, no. 1, pp. 1-47, 2002.
6. M. Vanetti, E. Binaghi, B. Carminati, M. Carullo, and E. Ferrari, Content- Based Filtering in
On-Line Social Networks, Proc. ECML/PKDD Workshop Privacy and Security Issues in Data
Mining and Machine Learning( PSDML 10), 2010.
7. 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.
8. 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.
9. Mrs. L.Rajeswari and Dr.S.S.Dhenakaran, “Page Access Coefficient Algorithm For
Information Filtering In Social Network” International journal of Computer Engineering &
Technology (IJCET), Volume 4, Issue 3, 2013, pp. 60 - 69, ISSN Print: 0976 – 6367, ISSN
Online: 0976 – 6375.
10. Muhanad A. Al-Khalisy and Dr.Haider K. Hoomod, “Posn: Private Information Protection In
Online Social Networks” International journal of Computer Engineering & Technology
(IJCET), Volume 4, Issue 2, 2013, pp. 340 - 355, ISSN Print: 0976 – 6367, ISSN Online: 0976
– 6375.
11. Mr. Yogesh P Murumkar and Prof. Yogesh B. Gurav, “Sampling Online Social Networks
Using Outlier Indexing” International journal of Computer Engineering & Technology
(IJCET), Volume 5, Issue 6, 2014, pp. 11 - 18, ISSN Print: 0976 – 6367, ISSN Online: 0976 –
6375.

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