Efficiently identify local frequent keyword co-occurrence patterns in geo-tagged Twitter stream

Wang, X; Zhang, Y; Zhang, W; Lin, X

Efficiently identify local frequent keyword co-occurrence patterns in geo-tagged Twitter stream

Wang, X Zhang, Y

Zhang, W Lin, X

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Publication Type:: Conference Proceeding
Citation:: SIGIR 2014 - Proceedings of the 37th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2014, pp. 1215 - 1218
Issue Date:: 2014-01-01

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Field	Value	Language
dc.contributor.author	Wang, X	en_US
dc.contributor.author	Zhang, Y https://orcid.org/0000-0002-2674-1638	en_US
dc.contributor.author	Zhang, W	en_US
dc.contributor.author	Lin, X	en_US
dc.date.issued	2014-01-01	en_US
dc.identifier.citation	SIGIR 2014 - Proceedings of the 37th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2014, pp. 1215 - 1218	en_US
dc.identifier.isbn	9781450322591	en_US
dc.identifier.uri	http://hdl.handle.net/10453/32988
dc.description.abstract	With the prevalence of the geo-position enabled devices and services, a rapidly growing amount of tweets are associated with geo-tags. Consequently, the real time search on geotagged Twitter streams has attracted great attentions. In this paper, we advocate the significance of the co-occurrence of keywords for the geo-tagged tweets data analytics, which is overlooked by existing studies. Particularly, we formally introduce the problem of identifying local frequent keyword co-occurrence patterns over the geo-tagged Twitter streams, namely LFP query. To accommodate the high volume and the rapid updates of the Twitter stream, we develop an inverted KMV sketch (IK sketch for short) structure to capture the co-occurrence of keywords in limited space. Then efficient algorithms are developed based on IK sketch to support LFP queries as well as its variant. The extensive empirical study on real Twitter dataset confirms the effectiveness and efficiency of our approaches. Copyright 2014 ACM.	en_US
dc.relation.ispartof	SIGIR 2014 - Proceedings of the 37th International ACM SIGIR Conference on Research and Development in Information Retrieval	en_US
dc.relation.isbasedon	10.1145/2600428.2609548	en_US
dc.title	Efficiently identify local frequent keyword co-occurrence patterns in geo-tagged Twitter stream	en_US
dc.type	Conference Proceeding
utslib.for	0806 Information Systems	en_US
utslib.for	080109 Pattern Recognition and Data Mining	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
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

With the prevalence of the geo-position enabled devices and services, a rapidly growing amount of tweets are associated with geo-tags. Consequently, the real time search on geotagged Twitter streams has attracted great attentions. In this paper, we advocate the significance of the co-occurrence of keywords for the geo-tagged tweets data analytics, which is overlooked by existing studies. Particularly, we formally introduce the problem of identifying local frequent keyword co-occurrence patterns over the geo-tagged Twitter streams, namely LFP query. To accommodate the high volume and the rapid updates of the Twitter stream, we develop an inverted KMV sketch (IK sketch for short) structure to capture the co-occurrence of keywords in limited space. Then efficient algorithms are developed based on IK sketch to support LFP queries as well as its variant. The extensive empirical study on real Twitter dataset confirms the effectiveness and efficiency of our approaches. Copyright 2014 ACM.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/32988