Mining Hot Topics from Twitter Streams

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, aflexible stream mining approach for hot twitter topic detection. Specifically, 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.