Abstract

Social Media Network is one of the main source of data for various event detections. Here in this paper a new and efficient method for the Detection of Traffic in Online Social Network Data is proposed using Clustering and Classification of Data. The Planned Procedure applied here is based on SVM Supervised Learning based Clustering of Similar features of Traffic and then classify the Data using J48 Decision Tree to classify number of events performed in the Twitter Traffic. The Planned Procedure is then compared with the Existing Classification approached such as SVM and Naïve Bayes and C4.5, but the technique is more efficient in comparison.

References

• N. Hegde, L. Massoulié and L. Viennot, “Self-Organizing Flows in Social Networks”, in Structural Information and Communication Complexity, Lecture Notes in Computer Science, Vol. 8179, pp. 116–128, 2013. (Cited on page 5.)
• B.A. Prakash, M. Seshadri, A. Sridharan, S. Machiraju and C. Faloutsos, “EigenSpokes: Surprising Patterns and Scalable Community Chipping in Large Graphs”, in IEEE International Conference on Data Mining Work-shops (ICDMW), pp. 290-295, Dec. 2009.
• T. Sakaki, M. Okazaki, and Y. Matsuo,“Earthquake Shakes Twitter Users: Real-Time Event Detection by Social Sensors”, in Proc. of the 19th ACM International Conference on World Wide Web (WWW T10), New York, NY, USA
• V. Lampos and N. Cristianini, “Tracking the Flu Pandemic by Monitoring the Social Web”, in 2nd International Workshop on Cognitive Information Processing (CIP), pp. 411-416, June 2010.
• A. Toninelli, A. Pathak and V. Issarny, “Yarta: A Middleware for ManagingMobile Social Ecosystems”, in International Conference on Grid and Pervasive Computing (GPC), pp. 209–220, Oulu, Finland, May 2011.
• Y. Demchenko, Zhiming Zhao, P. Grosso, A. Wibisono, and C. de Laat. Addressing big data challenges for scientific data infrastructure. In Cloud Computing Technology and Science (CloudCom), 2012 IEEE 4th International Conference on, pages 614-617, Dec 2012.
• Eleonora D’Andrea, Pietro Ducange, Beatrice Lazzerini, and Francesco Marcelloni, “Real-Time Detection of Traffic From Twitter Stream Analysis” IEEE Transactions On Intelligent Transportation Systems, Vol. 16, No. 4, August 2015.
• C. Li, A. Sun, and A. Datta. Twevent: segment-based event detection from tweets. In 21st ACM International Conference on Information and Knowledge Management, CIKM’12, Maui, HI, USA, October 29 - November 02, 2012, pages 155–164, 2012.
• Flavio Chierichetti, Jon M. Kleinberg, Ravi Kumar, Mohammad Mahdian, and Sandeep Pandey. “Event detection via communication pattern analysis”. In Proceedings of the Eighth International Conference on Weblogs and Social Media, ICWSM 2014.
• J. Kleinberg. Bursty and hierarchical structure in streams. In Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 91–101, 2002.
• A. Ihler, J. Hutchins, and P. Smyth. Adaptive event detection with time-varying poisson processes. In Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 207–216, 2006.
• The Smarty project SMARTY è un progetto afferente al programma  (linea B), ammesso a finanziamento cosi come risulta dalla graduatoria approvata con Decreto Dirigenziale n. 5874 del 10.12.2012.
• H. Takemura and K. Tajima, “Tweet classification based on their life-time duration,” in Proc. 21st ACM Int. CIKM, Shanghai, China, 2012, pp. 2367–2370.