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GEAM: A general and event-related aspects model for Twitter event detection
conference contribution
posted on 2013-11-18, 00:00 authored by Y You, Guangyan HuangGuangyan Huang, J Cao, E Chen, J He, Y Zhang, L HuEvent 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.
History
Event
International Conference on Web Information Systems Engineering (14th : 2013 : Nanjing, China)Volume
8181Issue
Part 2Series
Lecture Notes in Computer SciencePagination
319 - 332Publisher
SpringerLocation
Nanjing, ChinaPlace of publication
Berlin, GermanyPublisher DOI
Start date
2013-10-13End date
2013-10-15ISSN
0302-9743eISSN
1611-3349ISBN-13
9783642411540Language
engPublication classification
E Conference publication; E1.1 Full written paper - refereedCopyright notice
2013, SpringerEditor/Contributor(s)
X Lin, Y Manolopoulos, D Srivastava, G HuangTitle of proceedings
Web Information Systems Engineering - WISE 2013Usage metrics
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