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

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


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.


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.


[[1] I. Witten and E. Frank. Data Mining: Practical Machine Learning Tools and Techniques (Morgan Kaufmann Series in Data Management Systems). Morgan Kaufmann Publishers Inc., 2005.
[2] E. Trandafili, M. Biba, and A. Xhuvani Profiling Social Network Users with Machine Learning In Proceedings of BCI’13 , September 19-21, Thessaloniki, Greece, 2013.
[3] B. Viswanath, A. Mislove, M. Cha and K. P. Gummadi. On the Evolution of User Interaction in Facebook In Proceedings of the 2nd ACM SIGCOMM Workshop on Social Networks (WOSN’09) , August, 2009.
[4] WOSN 2009 Data Sets. Avilable at:http://socialnetworks.mpi-sws.org/ data-wosn2009.html.
[5] Q. Kong, W. Mao, and D. Zeng Predicting User Participation in Social Networking Sites In proceedings of 2013 IEEE International Conference on Intelligence and Security Informatics (ISI) , 2013.
[6] C. Wilson, B. Boe, A. Sala, K. P. N. Puttaswamy, and B.Y. Zhao User Interactions in Social Networks and their Implications In proceedings of EuroSys09, April 13 , Nuremberg, Germany, 2009.
[7] L. Jin, Y. Chen, T. Wang, P. Hu, and A. V. Vasilakos Understanding User Behavior in Online Social Networks: A Survey IEEE Communications Magazine , September 2013.
[8] M. Eslami, A. Aleyasen, R. Z. Moghaddam, and K. Karahalios Friend Grouping Algorithms for Online Social Networks: preference, bias, and implications In proceedings of SocInfo 2014 , Springer International Publishing Switzerland 2014.
[9] Python Software Foundation. Python Language Reference, version 2.7. Available athttp://www.python.org.
[10] P. Andritsos. Data Clustering Techniques-Qualifying Oral Examination Paper. University of Toronto, Department of Computer Science, March11, 2002.
[11] K. Chen. K-means Clustering. COMP24111 Machine Learning course, 2016. Avilable at:https://studentnet.cs.manchester.ac.uk/ugt/COMP24111/
[12] C. B Do and S. Batzoglou What is the expectation maximization algorithm?. Nature Biotechnology , volume 26, number 8, August 2008.
[13] J. Kleinberg Challenges in mining social network data: processes, privacy, and paradoxes In Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD ’07) , San Jose, California, USA August 12 - 15, 2007.
[14] http://www.facebook.com/.
[15] https://www.linkedin.com/
[16] https://twitter.com/
[17] Chapter 4: Measures of distance between samples: Euclidean Avialable at: ? michael/stanford/maeb4.pdf
[18] F. Erlandsson, A. Borg, H. Johnson, and P. Brodka. Predicting User Participation in Social Media In proceedings of NetSci-X 2016 , LNCS 9564, pp. 126135, 2016.
[19] P. Dayan Unsupervised Learning. The MIT Encyclopedia of the Cognitive Science, 1999.
[20] L. Rokach, O. Maimon. Clustering methods. Data Mining and Knowledge Discovery Handbook, pp. 321352, Springer, New York, 2005,
[21] W. Ponchai, B. Watanapa, and K. Suriyathumrongkul, Finding Charac- teristics of Influencer in Social Network using Association Rule Mining. In Proceedings of the 10th International Conference on e-Business (iNCEB2015) , November 23rd - 24th 2015.
[22] F. Erlandsson, P. Brdka, A. Borg, H. Johnson Finding Influential Users in Social Media Using

Facebook social networks, user interaction, clustering.