Survey on Different Classification
International Conference On Recent Trends In Engineering Science And Management ISBN: 978-81-931039-2-0 Jawaharlal Nehru University, Convention Center, New Delhi (India), 15 March 2015 www.conferenceworld.in SURVEY ON DIFFERENT CLASSIFICATION TECHNIQUES FOR DETECTION OF FAKE PROFILES IN SOCIAL NETWORKS Ameena A1, Reeba R2 1,2, Department of Computer Science And Engineering, Sree Buddha College Of Engineering, Pattoor (India) ABSTRACT In the present generation, the social life of everyone has become associated with the online social networking sites. But with their rapid growth, many problems like fake profiles, online impersonation have also grown. There are no feasible solution exist to control these problems. In this paper, survey on different classification techniques for detection of fake profiles in social networks is proposed. This paper presents the classification techniques like Support Vector Machine, Naive Bayes and Decision trees to classify the profiles into fake or genuine classes. This classification techniques can be used as a framework for automatic detection of fake profiles, it can be applied easily by online social networks which has millions of profile whose profiles cannot be examined manually. Keywords : SVM, SNS, Decision Tree, Naive Bayes Classification, Support Vector Machine I. INTRODUCTION Social Networking Sites (SNS) are web-based services that facilitates individuals to construct a profile, which is either public or semi-public. SNS contains list of users with whom we can share a connection, view their activities in network and also converse. SNS users communicate by messages, blogs, chatting, video and music files. SNS also have many disadvantages such as information is public, security problem, cyber bullying and misuse and abuse of SNS platform.
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