Topic detection and tracking evaluation overview
Topic detection and tracking
The computation of word associations: comparing syntagmatic and paradigmatic approaches
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
Why we twitter: understanding microblogging usage and communities
Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 workshop on Web mining and social network analysis
Proceedings of the 16th ACM SIGSPATIAL international conference on Advances in geographic information systems
Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
New event detection and topic tracking in Turkish
Journal of the American Society for Information Science and Technology
Earthquake shakes Twitter users: real-time event detection by social sensors
Proceedings of the 19th international conference on World wide web
Streaming first story detection with application to Twitter
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Hip and trendy: Characterizing emerging trends on Twitter
Journal of the American Society for Information Science and Technology
Evidential location estimation for events detected in Twitter
Proceedings of the 7th Workshop on Geographic Information Retrieval
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This paper aims to enhance event detection methods in a micro-blogging platform, namely Twitter. The enhancement technique we propose is based on lexico-semantic expansion of tweet contents while applying document similarity and clustering algorithms. Considering the length limitations and idiosyncratic spelling in Twitter environment, it is possible to take advantage of word similarities and to enrich texts with similar words. The semantic expansion technique we implement is based on syntagmatic and paradigmatic relationships between words, extracted from their co-occurrence statistics. As our technique does not depend on an existing ontology or a lexicon database such as Word Net, it should be applicable for any language. The proposed technique is applied on a tweet set collected for three days from the users in Turkey. The results indicate earlier detection of events and improvements in accuracy.