[KSCI] Korea Science Citation Index Service

http://dx.doi.org/10.6109/jicce.2011.9.3.295

Classifying Temporal Topics with Similar Patterns on Twitter

Yun, Hong-Won (Department of IT, Silla University)

Publication Information

Journal of information and communication convergence engineering / v.9, no.3, 2011 , pp. 295-300 More about this Journal

Abstract

Twitter is a popular microblogging service that enables the users to send and read short text messages. These messages are becoming source to analyze topic trends and identify relations among temporal topics. In this paper, we propose a method to classify the temporal topics on Twitter as a problem of grouping the similar patterns. To provide a starting point for a classification under the same topics, we identify the content word weighting scheme based on Latent Dirichlet Allocation (LDA). And we formulate how the temporal topics in the time window can be classified like peaky topics, constant topics, and periodic topics. We provide different real case studies which show the validity of the proposed method. Evaluations show that the proposed method is useful as a classifying model in the analysis of the temporal topics.

Keywords

Temporal topics; Peaky topics; Constant topics; Periodic topics; Twitter;

Citations & Related Records

Reference

1	Top 10 Twitter trends: Available: http://mashable.com
2	Twitter API: Available: http://apiwiki.twitter.com
3	Real time Twitter trends: Available:http://tweettabs.com
4	D. A. Shamma, L. Kennedy, and E. F. Churchill, "Peaks and Persistence: Modeling the Shape of Microblog Conversations," Proc. CSCW 2011, March, 2011.
5	C. X. Lin, B. Zhao, Q. Mei, and J. Han, "PET: A Statistical Model for Popular Events Tracking in Social Communities," Proc. KDD'10, pp. 929-938, July, 2010.
6	Long term average of tweet. Available: http://gigatweeter.com/analytics
7	K. Kireyev, L. Palen, K. M. Anderson, "Application of Topics Models to Analysis of Disaster-Related Twitter Data," In NIPS Workshop on Applications for Topic Models: Text and Beyond, December, 2009.
8	D. M. Blei, A. Y. Ng, and M. I. Jodan, " Latent Dirichlet Allocation," Journal of Machine Learning Research 3, pp. 993-1022, 2003.
9	K. Sparck-Jones, "A Statistical Interpretation of Term Specificity and its Application in Retrieval," Journal of Documentaion, Vol. 28, No. 1, 1973.
10	Trends on Twitter aggregator: Available: http://twopular.com
11	Trends in Twitter: Available: http://trendistic.com
12	M. guy, P. Earle, C. Ostrum, K. Gruchalla, and S. Horvath, "Integration and Dissemination of Citizen Reported and Seismically Derived Earthquake Information via Social Network Technologies," Proc. IDA 2010, pp. 42-53, 2010.
13	TechCrunch, Available: http://techcrunch.com/2010/06/08/twitter-190-million-users
14	C. Honeycutt and S. C. Herring, "Beyond Microblogging: Conversation and Collaboration via Twitter," Proc. the 42nd Hawaii International Conference on System Sciences 2009, IEEE Press, 2009.
15	T. Sakaki, M. Okazaki, Y. Matsuo, "Earthquake Shake Twiiter Users: Real-time Event Detection by Social Sensors," Proc. WWW 2010, pp. 851-860, April, 2010.
16	A. Hughes, L. Palen, "Twitter Adoption and Use in Mass Convergence and Emergency Events," Proc. the 2009 Information Systems for Crisis Response and Management Conference, 2009.
17	Ye Tian et al. "Topic Detection and Organization of Mobile Text Messages," Proc. CIKM'10, pp.1877-1880, October, 2010.
18	A. Java, X. Song, T. Finin, and B. Tseng, "Why We Twitter: An Analysis of a Microblogging Community," Proc. the 9th WebKDD and 1st SNAKDD 2007, LNCS 5439, pp. 118-138, 2009.