Publication Type
Conference Proceeding Article
Version
acceptedVersion
Publication Date
6-2013
Abstract
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.
Keywords
HDP, Gaussian mixture, Twitter, event detection
Discipline
Databases and Information Systems | Numerical Analysis and Scientific Computing | Social Media
Publication
Web-age information management: 14th International Conference, WAIM 2013, Beidaihe, China, June 14-16: Proceedings
Volume
7932
First Page
502
Last Page
513
ISBN
9783642385612
Identifier
10.1007/978-3-642-38562-9_51
Publisher
Springer Verlag
City or Country
Berlin
Citation
WANG, Xun; ZHU, Feida; JIANG, Jing; and LI, Sujian.
Real Time Event Detection in Twitter. (2013). Web-age information management: 14th International Conference, WAIM 2013, Beidaihe, China, June 14-16: Proceedings. 7932, 502-513.
Available at: https://ink.library.smu.edu.sg/sis_research/1820
Copyright Owner and License
LARC
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1007/978-3-642-38562-9_51
Included in
Databases and Information Systems Commons, Numerical Analysis and Scientific Computing Commons, Social Media Commons