GEAM: a general and event-related aspects model for Twitter event detection

You, Yue, Huang, Guangyan, Cao, Jian, Chen, Enhong, He, Jing, Zhang, Yanchun and Hu, Liang 2013, GEAM: a general and event-related aspects model for Twitter event detection, in Web Information Systems Engineering - WISE 2013, Springer, Berlin, Germany, pp. 319-332, doi: 10.1007/978-3-642-41154-0_24.

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Title GEAM: a general and event-related aspects model for Twitter event detection
Author(s) You, Yue
Huang, GuangyanORCID iD for Huang, Guangyan
Cao, Jian
Chen, Enhong
He, Jing
Zhang, Yanchun
Hu, Liang
Conference name International Conference on Web Information Systems Engineering (14th : 2013 : Nanjing, China)
Conference location Nanjing, China
Conference dates 13-15 Oct. 2013
Title of proceedings Web Information Systems Engineering - WISE 2013
Publication date 2013
Series Lecture Notes in Computer Science v.8181
Start page 319
End page 332
Total pages 14
Publisher Springer
Place of publication Berlin, Germany
Summary Event detection on Twitter has become a promising research direction due to Twitter's popularity, up-to-date feature, free writing style and so on. Unfortunately, it's a challenge to analyze Twitter dataset for event detection, since the informal expressions of short messages comprise many abbreviations, Internet buzzwords, spelling mistakes, meaningless contents etc. Previous techniques proposed for Twitter event detection mainly focus on clustering bursty words related to the events, while ignoring that these words may not be easily interpreted to clear event descriptions. In this paper, we propose a General and Event-related Aspects Model (GEAM), a new topic model for event detection from Twitter that associates General topics and Event-related Aspects with events. We then introduce a collapsed Gibbs sampling algorithm to estimate the word distributions of General topics and Event-related Aspects in GEAM. Our experiments based on over 7 million tweets demonstrate that GEAM outperforms the state-of-the-art topic model in terms of both Precision and DERate (measuring Duplicated Events Rate detected). Particularly, GEAM can get better event representation by providing a 4-tuple (Time, Locations, Entities, Keywords) structure of the detected events. We show that GEAM not only can be used to effectively detect events but also can be used to analyze event trends. © 2013 Springer-Verlag.
ISBN 9783642411533
ISSN 0302-9743
Language eng
DOI 10.1007/978-3-642-41154-0_24
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1.1 Full written paper - refereed
ERA Research output type E Conference publication
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Document type: Conference Paper
Collection: School of Information Technology
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