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Learning to annotate tweets with crowd wisdom

Published: 13 May 2013 Publication History

Abstract

In Twitter, users can annotate tweets with hashtags to indicate the ongoing topics. Hashtags provide users a convenient way to categorize tweets. However, two problems remain unsolved during an annotation: (1) Users have no way to know whether some related hashtags have already been created. (2) Users have their own way to categorize tweets. Thus personalization is needed. To address the above problems, we develop a statistical model for Personalized Hashtag Recommendation. With millions of "tweet, hashtag" pairs being generated everyday, we are able to learn the complex mappings from tweets to hashtags with the wisdom of the crowd. Our model considers rich auxiliary information like URLs, locations, social relation, temporal characteristics of hashtag adoption, etc. We show our model successfully outperforms existing methods on real datasets crawled from Twitter.

References

[1]
E. Khabiri, J. Caverlee, and K. Y. Kamath. Predicting semantic annotations on the real-time web. In HT, pages 219--228, 2012.
[2]
L. Yang, T. Sun, M. Zhang, and Q. Mei. We know what @you#tag: does the dual role affect hashtag adoption? In WWW, pages 261--270, 2012.

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WWW '13 Companion: Proceedings of the 22nd International Conference on World Wide Web
May 2013
1636 pages
ISBN:9781450320382
DOI:10.1145/2487788
Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

Sponsors

  • NICBR: Nucleo de Informatcao e Coordenacao do Ponto BR
  • CGIBR: Comite Gestor da Internet no Brazil

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 May 2013

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Author Tags

  1. recommender systems
  2. social media

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WWW '13
Sponsor:
  • NICBR
  • CGIBR
WWW '13: 22nd International World Wide Web Conference
May 13 - 17, 2013
Rio de Janeiro, Brazil

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WWW '13 Companion Paper Acceptance Rate 831 of 1,250 submissions, 66%;
Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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