Conference Proceeding Article
Event detection has been an important task for a long time. When it comes to Twitter, new problems are presented. Twitter data is a huge temporal data flow with much noise and various kinds of topics. Traditional sophisticated methods with a high computational complexity aren’t designed to handle such data flow efficiently. In this paper, we propose a mixture Gaussian model for bursty word extraction in Twitter and then employ a novel time-dependent HDP model for new topic detection. Our model can grasp new events, the location and the time an event becomes bursty promptly and accurately. Experiments show the effectiveness of our model in real time event detection in Twitter.
HDP, Gaussian mixture, Twitter, event detection
Databases and Information Systems | Numerical Analysis and Scientific Computing
Data Management and Analytics
14th International Conference, WAIM 2013, Beidaihe, China, June 14-16, 2013. Proceedings
Springer Berlin Heidelberg
City or Country
WANG, Xun; ZHU, Feida; JIANG, Jing; and LI, Sujian.
Real Time Event Detection in Twitter. (2013). 14th International Conference, WAIM 2013, Beidaihe, China, June 14-16, 2013. Proceedings. 502-513. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/1820