Cross-Site Bonding of Anonymous Users in Multiple Social Media Networks

International Journal on Recent and Innovation Trends in Computing and Communication ISSN: 2321-8169
Volume: 5 Issue: 7 678 – 683
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678
IJRITCC | July 2017, Available @ http://www.ijritcc.org
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0Cross-Site Bonding of Anonymous Users in Multiple Social Media Networks
0
Ambresh Bhadra Shetty
Assistant Professor,
Dept. of Studies in Computer Applications (MCA),
Visvesvaraya Technological University,
Centre for PG studies, Kalaburagi
ambresh.bhadrashetty@gmail.com
Pooja Guttedar
Student,MCA VI Semester,
Dept. of Studies in Computer Applications (MCA),
Visvesvaraya Technological University,
Centre for PG studies, Kalaburagi
poojadg666@gmail.com
Abstract: From last few years many online Social Media Networks (SMN) platforms came into existence only those users who connected
through similar site network can communicate each other by exchanging their information. For example, a Facebook user can connect only
with Facebook user not with cross site user like Twitter so to connect users through multiple cross site network we have introduced a new
social networking site calledCross platform network. Cross platform is a site which helps people to connect with huge number of different
online Social Media Networks such as Facebook, Twitter,and Wechat. Using Cross platform networkpeople can communicate each other by
exchanging the information from different sites.
Keywords: Similar Site, Cross Site, Social Media Network
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I. INTRODUCTION
Social Networks are one of the highest growing
industries in the world Social Networking sites such as
Facebook, Twitter, Wechat, Instagram are extremely a
powerful communication tools. Most of people think that
using these social media network they can communicate
each other easily and also it helps to run a successful
business.
Twitter is an online news and social networking service
where users post and interact with messages, "Tweets",
restricted to 140 characters [1]. Registered users can post
tweets, but those who are unregistered can only read them.
Based on the number of active users Facebook is
considered as most popular social networking sites in the
world. It has totally 2billion monthly users. Users of age
between 13 to 18 are consideredas minors therefore their
profiles are set to share with friends only. On year of 2010
Facebook has announced with domain name called fb.com
from American Farm Bureau Federation. It allow people to
share information like pictures, video’s what we have been
up to with friends.
In cross platform network they have proposed an
algorithm called Friend Relationship-Based User
Identification(FRUI). FRUI computes a match degree for all
hopeful User Matched Pairs (UMPs), and just UMPs with
top positions are considered as indistinguishable clients. We
likewise created two recommendations to enhance the
productivity of the calculation. Aftereffects of broad
investigations show that FRUI performs much superior to
anything current system structure-based calculations.
Cross-Platform research faces many new challenges. As
shown in Fig. 1, with the growth of different SMN platforms
on the Internet, the cross-platform approach is used to merge
as various SMN to create richer raw data and more complete
SMNs for social computing tasks. The main purpose of
using cross-platform SMN research is for identification of
users in multiple SMNs. Many studies have addressed the
user identification problem by examining public user profile
attributes, including screen name, birth- date, location,
gender, profile photo, etc. [4], [5], [7], [8], [9]. Since these
attributes are not used by any fake users. Cross platform
encompasses of the two groupings: The single-following
and the mutual-following associations. : The first one can
also be referred to as the tracking up of the associations or
the relations. It can be explained as: when the user with A
as name tag along the user with name B, in this case it is
considered as the A and the B user will be having the
tracking relationship, where one is known by the other but
the same thing is not with the other. This type of following
is seen in the SMNs relating to micro-blogging that takes in
Twitter. While the mutual-following associations are said to
be as friend associations, in which SMNs related to micro-
blogging they refer to commonly tracking associations
amongst the two users.

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Fig 1: Cross-application research to merge a variety of SMN’s
II. LITERATURE SURVEY
In year of 2011 B. Zhou and J. Pei introduced a technique
called the k-anonymity and l-diversity approaches to
provide privacy for different social networks to maintain
data privately by providing security. Because many online
social networks has lead to the problem of leaking
confidential information of individual person. This
requires a preservation of privacy before that network
data has introduced by service provider. Providing
privacy in different social networks has a one of most
important concern. Many published academics studies
have proposed solution for providing privacy but those
technique have not worked properly to overcome this
problem they have proposed a new technique called The
k-anonymity and l-diversity to maintain privacy [3].
In year of 2013 P. Jain and P. Kumaraguruhas developed
Finding Nemo, it is a method which matches accounts
called Facebook and Twitter. However,this text based
network search method has low accuracy and high
complexity in term of user identification,since only text of
same nick name are recognized while searching the friend
sets of friends [5].
In 2013 O.Goga linked accounts belonging to the same
person identity, based solely on the profile information.
Organization has started to collect a personal data of user
who generates their day to day activities through online.
In this work we need to set the capabilitiesmachine
learning to link aindependent accounts to maintain users
in different social networks. Based on that information
users provide their profiles publicly. Large scale
correlation approach helps to match account
betweendifferent social networks such as Twitter,
Facebookand Google+. In result it shows user names, real
names, location, photos using this information we can
identify 80% of the matching user account between
combinations of any two social networks [8].
Inyear of 2014 X. Qian et al. introduced a method called
Personalized Recommendation to combine the users based
on their interest and social circle. With the appearance and
notoriety of interpersonal organization, an ever increasing
number of clients get a kick out of the chance to share their
encounters, for example, evaluations, audits, and online
journals. The new factors of informal organization like
relational impact and intrigue in light of friend networks
bring openings and difficulties for recommender system
(RS) to tackle the cool begin and sparsity issue of datasets.
A portion of the social factors have been utilized as a part of
RS, yet have not been completely considered. In this paper,
three social variables, individual intrigue, relational intrigue
comparability, and relational impact, meld into a bound
together customized proposal demonstrate in light of
probabilistic grid factorization. The factor of individual
intrigue can make the RS prescribe things to meet clients'
distinctions, particularly for experienced clients. Also, for
chilly begin clients, the relational intrigue comparability and
relational impact can improve their connection among
highlights in the dormant space [10].
III. PROBLEM DEFINITION
The problem can be defined as in the existing we can send
or receive friend requests only to the person if he or she
holds an account in the same social networking site, for
example consider a person A who has an account in
Facebook and another person B has an account in Twitter,
now A cannot send
friend request to B because A & B are in different social
networking sites. To overcome this we are
designing a Cross-Site where in which the users of different
social networking sites can send or receive friend requests
with each other.

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III. ARCHITECTURE
In above Fig 2 cross site management server list all users
and authorize the register user to get login to
corresponding networks after that the user has to view his
own profile and send request to make friends with cross
site and also he can add posts like title, description, image
in both cross site as well as in same site. To make friends
in cross site admin has to give permission then only the
user can send request to cross site user. Here admin list all
friends from cross site and similar site network andview
all user posts with image description and finally chart
results to count number of users in same site and cross site
will be displayed.
IV. IMPLEMENTATION
FRUI ALGORITHM:
In this study, we propose an innovative approach to address
the challenges faced by previous studies. This new approach
focuses on the friendship structure, and develops the Friend
Relationship-based User Identification (FRUI) algorithm.
FRUI differs from the two existing algorithms, JLA and NS,
in the following aspects
(1) NS is suitable for directed networks, while JLA and
FRUI focus on undirected networks. JLA is restricted in
undirected networks by Conditional
Random Fields, while FRUI relies on friend relationships, as
this is more reliable and consistent with real-life friendship.
(2) JLA compares unmapped neighbors of nodes from one
of the two SMNs, while NS matches unidentified usersfrom
different networks by
comparing the mapped neighbors of each node. FRUI aims
to identify the most matched pairs among mapped users, but
does not iterate unmapped users. Therefore, it markedly
reduces computational complexity.

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V. RESULTS
Fig 3:All User and Authorize
In Fig. 3 admin is giving permission to the userto login into system after giving permission the status will be changed from
waiting to authorized
Fig 4: All Friend Request and Response
In Fig. 4 admin can view all friend request and response with respect to name of a person and social site

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Fig 5: Search Friends in Cross Site
In Fig. 5 user can search friends from cross site and send request to a particular person
Fig 6: Graph Result
In Fig. 6 graph result will display based on connected users that may either same site and cross site
VI. CONCLUSION
In this study we have designed new solution called
cross platform to identify unknown users across multiple
social media networks. We also developed a new method
called friend relationship based user identification (FRUI).
FRUI helps to computes a match degree for all hopeful User
Matched Pairs (UMPs), and UMPs with top positions are
considered as indistinguishable clients. Using this method
user can connect easily though different cross site network.
REFERENCES
[1] X. Ying and X. Wu, “Randomizing social networks: a
spectrum preserving approach”, 24, 26 April 2008.
[2] I. Konstas, V. Stathopoulos, and J.M. Jose, “On social
networks and recommendation “, 19-23 July 2009.
[3] B. Zhou and J. Pei, “The k-anonymity and l-diversity
approaches for privacy preservation in social networks
against neighborhood attacks”, 1 July 2011.

Volume: 5 Issue: 7 678 – 683
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[4] F. Abel, E. Herder, G.J. Houben, “Cross-system user
modeling and personalization on the social web”, 23 April
2012.
[5] P. Jain and P. Kumaraguru, "Finding Nemo: searching and
re-solving identities of users across online social
networks”, 26 Dec 2012.
[6] X. Kong, J. Zhang, and P.S. Yu, “Inferring anchor links
across multiple heterogeneous social networks”, 11 Dec
2013.
[7] R. Zafarani and H. Liu, "Connecting users across social
media sites: a behavioral-modeling approach "11-14
August 2013
[8] O. Goga, D. Perito, H. Lei, R. Teixeira, and R. Sommer,
"Large-scale Correlation of Accounts across Social
Networks”, April 2013.
[9] P. Jain, P. Kumaraguru, and A. Joshi, "@ i seek 'fb. me':
identifying users across multiple online social networks",
13 May 2013.
[10] X. Qian, H. Feng, G. Zhao, and T. Mei, "Personalized
Recommendation Combining User Interest and Social
Circle", 7July 2014.
[11] S. Tan, Y. Li, H. Sun, Z. Guan, X. Yan, J. Bu, C. Chen, and
X. He, "Interpreting the Public Sentiment Variations on
Twitter", 5 May 2014.

Cross-Site Bonding of Anonymous Users in Multiple Social Media Networks

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