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European Conference on Information Retrieval

ECIR 2011: Advances in Information Retrieval pp 338–349Cite as

Comparing Twitter and Traditional Media Using Topic Models

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Part of the Lecture Notes in Computer Science book series (LNISA,volume 6611)

Abstract

Twitter as a new form of social media can potentially contain much useful information, but content analysis on Twitter has not been well studied. In particular, it is not clear whether as an information source Twitter can be simply regarded as a faster news feed that covers mostly the same information as traditional news media. In This paper we empirically compare the content of Twitter with a traditional news medium, New York Times, using unsupervised topic modeling. We use a Twitter-LDA model to discover topics from a representative sample of the entire Twitter. We then use text mining techniques to compare these Twitter topics with topics from New York Times, taking into consideration topic categories and types. We also study the relation between the proportions of opinionated tweets and retweets and topic categories and types. Our comparisons show interesting and useful findings for downstream IR or DM applications.

Keywords

  • Twitter
  • microblogging
  • topic modeling

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  • DOI: 10.1007/978-3-642-20161-5_34
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Author information

Authors and Affiliations

  1. Peking University, China

    Wayne Xin Zhao, Jing He, Hongfei Yan & Xiaoming Li

  2. Singapore Management University, Singapore

    Jing Jiang, Jianshu Weng & Ee-Peng Lim

Authors
  1. Wayne Xin Zhao
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  2. Jing Jiang
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  3. Jianshu Weng
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  4. Jing He
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  5. Ee-Peng Lim
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  6. Hongfei Yan
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  7. Xiaoming Li
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Editor information

Editors and Affiliations

  1. Information School, University of Sheffield, Regent Court, 211 Portobello Street, S1 4DP, Sheffield, UK

    Paul Clough

  2. CLARITY: Centre for Sensor Web Technologies, School of Computing, Dublin City University, Glasnevin, Dublin 9, Ireland

    Colum Foley, Cathal Gurrin & Hyowon Lee,  & 

  3. Centre for Next Generation Localisation, School of Computing, Dublin City University, Glasnevin, Dublin 9, Ireland

    Gareth J. F. Jones

  4. TNO Human Factors, Brassersplein 2, 2612 CT, Delft, The Netherlands

    Wessel Kraaij

  5. Yahoo! Research, 177 Diagonal, 08018, Barcelona, Spain

    Vanessa Mudoch

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© 2011 Springer-Verlag Berlin Heidelberg

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Cite this paper

Zhao, W.X. et al. (2011). Comparing Twitter and Traditional Media Using Topic Models. In: , et al. Advances in Information Retrieval. ECIR 2011. Lecture Notes in Computer Science, vol 6611. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20161-5_34

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  • DOI: https://doi.org/10.1007/978-3-642-20161-5_34

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-20160-8

  • Online ISBN: 978-3-642-20161-5

  • eBook Packages: Computer ScienceComputer Science (R0)