Real time event detection in twitter

Authors:
Xun Wang;Feida Zhu;Jing Jiang;Sujian Li
Affiliations:
Key Laboratory of Computational Linguistics, Peking University, MOE, China;School of Information Systems, Singapore Management University, Singapore;School of Information Systems, Singapore Management University, Singapore;Key Laboratory of Computational Linguistics, Peking University, MOE, China
Venue:
WAIM'13 Proceedings of the 14th international conference on Web-Age Information Management
Year:
2013

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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.