Mining Hot Topics from Twitter Streams

Guo, J; Zhang, P; Tan, J; Guo, L

Mining Hot Topics from Twitter Streams

Guo, J Zhang, P

Tan, J Guo, L

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Publisher:: Elsevier
Publication Type:: Journal Article
Citation:: Procedia Computer Science, 2012, 9 (1), pp. 2008 - 2011
Issue Date:: 2012-01

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Field	Value	Language
dc.contributor.author	Guo, J	en_US
dc.contributor.author	Zhang, P https://orcid.org/0000-0001-7973-2746	en_US
dc.contributor.author	Tan, J	en_US
dc.contributor.author	Guo, L	en_US
dc.date.issued	2012-01	en_US
dc.identifier.citation	Procedia Computer Science, 2012, 9 (1), pp. 2008 - 2011	en_US
dc.identifier.issn	1877-0509	en_US
dc.identifier.uri	http://hdl.handle.net/10453/28902
dc.description.abstract	Mininghottopicsfrom twitter streamshas attractedalotof attentionin recent years.Traditionalhottopicmining from InternetWeb pages were mainly basedontext clustering.However, comparedtothetextsinWeb pages, twitter texts are relatively short with sparse attributes. Moreover,twitter data often increase rapidly withfast spreading speed, whichposesgreat challengetoexistingtopicmining models.Tothisend,we propose,inthispaper, a?exible stream mining approach for hot twitter topic detection. Speci?cally, we propose to use the FrequentPattern stream mining algorithm (i.e. FP-stream) to detect hot topics from twitter streams. Empirical studies on real world twitter data demonstrate the utility of the proposed method.	en_US
dc.publisher	Elsevier	en_US
dc.relation.ispartof	Procedia Computer Science	en_US
dc.relation.isbasedon	10.1016/j.procs.2012.04.224	en_US
dc.title	Mining Hot Topics from Twitter Streams	en_US
dc.type	Journal Article
utslib.citation.volume	1	en_US
utslib.citation.volume	9	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	08 Information and Computing Sciences	en_US
utslib.for	10 Technology	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access
pubs.consider-herdc	false	en_US
pubs.issue	1	en_US
pubs.volume	9	en_US

Abstract:

Mininghottopicsfrom twitter streamshas attractedalotof attentionin recent years.Traditionalhottopicmining from InternetWeb pages were mainly basedontext clustering.However, comparedtothetextsinWeb pages, twitter texts are relatively short with sparse attributes. Moreover,twitter data often increase rapidly withfast spreading speed, whichposesgreat challengetoexistingtopicmining models.Tothisend,we propose,inthispaper, a?exible stream mining approach for hot twitter topic detection. Speci?cally, we propose to use the FrequentPattern stream mining algorithm (i.e. FP-stream) to detect hot topics from twitter streams. Empirical studies on real world twitter data demonstrate the utility of the proposed method.

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http://hdl.handle.net/10453/28902