Classification of Users in Online Video Social Networks
Kondra Mohan Raju1, E. Madhukar2

1Mr. Kondra Mohan Raju, M.Tech Studying, Department of Software Engineering, Sreenidhi Institute of Science and Technology, Jawaharlal Technological University, Hyderabad (Telangana), India.
2Mr. E. Madhukar, Associate Professor, Department of Cloud Computing, Sreenidhi Institute of Science and Technology, Hyderabad (Telangana), India.
Manuscript received on 8 December 2013 | Revised Manuscript received on 18 December 2013 | Manuscript Published on 30 December 2013 | PP: 39-40 | Volume-3 Issue-7, December 2013 | Retrieval Number: G1380123713/13©BEIESP
Open Access | Editorial and Publishing Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (

Abstract: There are many online video social networks, in which Youtube is the most popular. These networks provide users to upload their own videos respect to a particular discussion. The feature provided by the networks gives the user to upload any kind of content. This creates polluted content into the system. For Example, Spammers may upload unrelated content as response to popular which increases the count of view. There is another kind of users called promoters, will gain visibility to a particular content by uploading many number of responses to increase the rank of the video. By promoting this, video will appear top in the list. This kind of activities may jeopardize the trust of the users, and social network may fail to provide genuine content. To avoid such kind of activities, we are coming up to detect the spammers and promoters. In our system we built a system same as youtube functionality having users with classification as legitimate, promoters and spammers. To distinguish between the users we allow for content and characterization attributes. These attributes can help in classifying user class. To classify the users we may use supervised classification theory. The theory is implemented on test collection. This approach successfully classified the majority of the prompters and some of the legitimate users misclassified. And most of the spammers detected form legitimate users as distinguishing is hard difficult.
Keywords: Social Network, Promoters, Spammers, Video Sharing, Classification.

Scope of the Article: Classification.