A Clustering Approach for Evaluation of User Interaction on Facebook Social Network

  IJCOT-book-cover
 
International Journal of Computer & Organization Trends  (IJCOT)          
 
© 2017 by IJCOT Journal
Volume - 7 Issue - 1
Year of Publication : 2017
Authors Doaa Hassan
  10.14445/22492593/IJCOT-V39P302

MLA

Doaa Hassan "A Clustering Approach for Evaluation of User Interaction on Facebook Social Network", International Journal of Computer & organization Trends (IJCOT), V7(1):4-8 Jan - Feb 2017, ISSN:2249-2593, www.ijcotjournal.org. Published by Seventh Sense Research Group.

Abstract Social Networks analysis has been an important source of gathering information due to the large amount of data that can be generated from users’ discussions and participation on social media. One way to analyze social networks is by estimating the amount of user interaction and participation in them. This paper addresses this issue by applying the machine learning clustering technique for categorizing users of Facebook social network based on their participation and interaction on Facebook. Two main features have been used for performing clustering: The first, is the number of links established between a user and others on Facebook through friendship relations. The second, is the number of posts written by a user to the walls of others on Facebook through posting. Therefore, the proposed approach in this paper aims to obtain different clusters of users that are categorized based on their level of interaction on Facebook. Hence we can estimate the amount of user interaction on Facebook by determining to which cluster he/she belongs.

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Keywords-
Facebook social networks, user interaction, clustering.