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

Authors:

Xiaoyang Wang,

Ying Zhang,

Wenjie Zhang,

Xuemin LinAuthors Info & Claims

SIGIR '14: Proceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval

Pages 1215 - 1218

https://doi.org/10.1145/2600428.2609548

Published: 03 July 2014 Publication History

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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 geo-tagged 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\xspace query. To accommodate the high volume and the rapid updates of the Twitter stream, we develop an inverted KMV sketch (IK\xspace sketch for short) structure to capture the co-occurrence of keywords in limited space. Then efficient algorithms are developed based on IK\xspace sketch to support LFP\xspace queries as well as its variant. The extensive empirical study on real Twitter dataset confirms the effectiveness and efficiency of our approaches.

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Cited By

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Wang GCong GZhang YHai ZYe J(2021)A Synopsis Based Approach for Itemset Frequency Estimation over Massive Multi-Transaction StreamACM Transactions on Knowledge Discovery from Data10.1145/346523816:2(1-30)Online publication date: 21-Jul-2021
https://dl.acm.org/doi/10.1145/3465238
Anagnostopoulos APetroni FSorella M(2018)Targeted interest-driven advertising in cities using TwitterData Mining and Knowledge Discovery10.1007/s10618-017-0529-732:3(737-763)Online publication date: 1-May-2018
https://dl.acm.org/doi/10.1007/s10618-017-0529-7
Hu YKang CTang JYin DChang Y(2017)Large-Scale Location Prediction for Web PagesIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2017.270263129:9(1902-1915)Online publication date: 1-Sep-2017
https://doi.org/10.1109/TKDE.2017.2702631
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Index Terms

Efficiently identify local frequent keyword co-occurrence patterns in geo-tagged Twitter stream
1. Human-centered computing
  1. Collaborative and social computing
    1. Collaborative and social computing systems and tools
2. Information systems
  1. World Wide Web

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Published In

SIGIR '14: Proceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval

July 2014

1330 pages

ISBN:9781450322577

DOI:10.1145/2600428

General Chairs:
Shlomo Geva
Queensland University of Technology
,
Andrew Trotman
University of Dunedin
,
Program Chairs:
Peter Bruza
Queensland University of Technology
,
Charles L.A. Clarke
University of Waterloo
,
Kal Järvelin
University of Tampere

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 03 July 2014

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SIGIR '14: The 37th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 6 - 11, 2014

Queensland, Gold Coast, Australia

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SIGIR '14 Paper Acceptance Rate 82 of 387 submissions, 21%;

Overall Acceptance Rate 792 of 3,983 submissions, 20%

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Cited By

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Wang GCong GZhang YHai ZYe J(2021)A Synopsis Based Approach for Itemset Frequency Estimation over Massive Multi-Transaction StreamACM Transactions on Knowledge Discovery from Data10.1145/346523816:2(1-30)Online publication date: 21-Jul-2021
https://dl.acm.org/doi/10.1145/3465238
Anagnostopoulos APetroni FSorella M(2018)Targeted interest-driven advertising in cities using TwitterData Mining and Knowledge Discovery10.1007/s10618-017-0529-732:3(737-763)Online publication date: 1-May-2018
https://dl.acm.org/doi/10.1007/s10618-017-0529-7
Hu YKang CTang JYin DChang Y(2017)Large-Scale Location Prediction for Web PagesIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2017.270263129:9(1902-1915)Online publication date: 1-Sep-2017
https://doi.org/10.1109/TKDE.2017.2702631
Schubert EWeiler MKriegel H(2016)SPOTHOTProceedings of the 28th International Conference on Scientific and Statistical Database Management10.1145/2949689.2949699(1-12)Online publication date: 18-Jul-2016
https://dl.acm.org/doi/10.1145/2949689.2949699

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