Mining Hot Topics from Twitter Streams

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