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Detecting dynamic association among twitter topics

Published: 16 April 2012 Publication History

Abstract

Over the last few years, Twitter is increasingly becoming an important source of up-to-date topics about what is happening in the world. In this paper, we propose a dynamic topic association detection model to discover relations between Twitter topics, by which users can gain insights into richer information about topics of interest. The proposed model utilizes a time constrained method to extract event-based spatio-temporal topic association, and constructs a dynamic temporal map to represent the obtained result. Experimental results show the improvement of the proposed model compared to static spatio-temporal method and co-occurrence method.

References

[1]
Sarmay, A., Jainy, A. and Yu, C. 2011. Dynamic relationship and event discovery. In WSDM'11, pp. 207--216.
[2]
Kleinberg, J. 2002. Bursty and hierarchical structure in streams. In SIGKDD'02, pp. 373--397.
[3]
Sehgal, A. K. and Srinivasan, P. 2007. Profiling topics on the web. In WWW workshop' 07, pp. 1--8.
[4]
Liu, J., Dong, X. and Halevy, A. Y. 2006. Answering structured queries on unstructured data. In WebDB'06.
[5]
Song, S., Li, Q. and Zheng, N. 2010. A spatio-temporal framework for related topic search in micro-blogging. In AMT' 10, pp 63--73.
[6]
Terachi, M., Saga, R. and Tsuji, H. 2006. Trends recognition in journal papers by text mining. In IEEE/SMC'06, pp. 4784--4789.
[7]
Grinev, M., Grineva, M., Boldakov, A., Novak, L., Syssoev, A., Lizorkin, D. 2009. Sifting Micro-blogging Stream for Events of User Interest. In SIGIR' 09, pp. 838.

Cited By

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  • (2019)Discovering Events from Social Media for Emergency PlanningProceedings of the 20th Annual International Conference on Digital Government Research10.1145/3325112.3325213(109-116)Online publication date: 18-Jun-2019
  • (2017)A simple yet effective method for summarizing microblogging users with their representative tweets2017 International Conference on Asian Language Processing (IALP)10.1109/IALP.2017.8300605(310-313)Online publication date: Dec-2017
  • (2016)Dynamic Online HDP model for discovering evolutionary topics from Chinese social textsNeurocomputing10.1016/j.neucom.2015.06.047171:C(412-424)Online publication date: 1-Jan-2016
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Published In

cover image ACM Other conferences
WWW '12 Companion: Proceedings of the 21st International Conference on World Wide Web
April 2012
1250 pages
ISBN:9781450312301
DOI:10.1145/2187980

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  • Univ. de Lyon: Universite de Lyon

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 16 April 2012

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Author Tags

  1. burst detection
  2. dynamic temporal map
  3. topic association
  4. twitter

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  • Poster

Conference

WWW 2012
Sponsor:
  • Univ. de Lyon
WWW 2012: 21st World Wide Web Conference 2012
April 16 - 20, 2012
Lyon, France

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Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Cited By

View all
  • (2019)Discovering Events from Social Media for Emergency PlanningProceedings of the 20th Annual International Conference on Digital Government Research10.1145/3325112.3325213(109-116)Online publication date: 18-Jun-2019
  • (2017)A simple yet effective method for summarizing microblogging users with their representative tweets2017 International Conference on Asian Language Processing (IALP)10.1109/IALP.2017.8300605(310-313)Online publication date: Dec-2017
  • (2016)Dynamic Online HDP model for discovering evolutionary topics from Chinese social textsNeurocomputing10.1016/j.neucom.2015.06.047171:C(412-424)Online publication date: 1-Jan-2016
  • (2016)Mining opinion summarizations using convolutional neural networks in Chinese microblogging systemsKnowledge-Based Systems10.1016/j.knosys.2016.06.017107:C(289-300)Online publication date: 1-Sep-2016
  • (2015)Detecting Representative Tweets of Microblogging UsersProceedings of the Eighth International C* Conference on Computer Science & Software Engineering10.1145/2790798.2790815(110-112)Online publication date: 13-Jul-2015
  • (2015)A Temporal-Topic Model for Friend Recommendations in Chinese Microblogging SystemsIEEE Transactions on Systems, Man, and Cybernetics: Systems10.1109/TSMC.2015.239126245:9(1245-1253)Online publication date: Sep-2015
  • (2015)Recommending Hashtags to Forthcoming Tweets in Microblogging2015 IEEE International Conference on Systems, Man, and Cybernetics10.1109/SMC.2015.348(1998-2003)Online publication date: Oct-2015
  • (2015)Classifying and ranking microblogging hashtags with news categories2015 IEEE 9th International Conference on Research Challenges in Information Science (RCIS)10.1109/RCIS.2015.7128928(540-541)Online publication date: May-2015
  • (2015)Topic dynamics in Weibo: a comprehensive studySocial Network Analysis and Mining10.1007/s13278-015-0282-05:1Online publication date: 14-Jul-2015
  • (2014)Context Based Semantic Relations in TweetsState of the Art Applications of Social Network Analysis10.1007/978-3-319-05912-9_2(35-52)Online publication date: 15-May-2014

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