Publication Type

Conference Proceeding Article

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

Research Areas

Data Management and Analytics

Publication

14th International Conference, WAIM 2013, Beidaihe, China, June 14-16, 2013. Proceedings

First Page

502

Last Page

513

ISBN

9783642385612

Identifier

10.1007/978-3-642-38562-9_51

Publisher

Springer Berlin Heidelberg

City or Country

Beidaihe, China

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