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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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© 2011 Springer-Verlag Berlin Heidelberg

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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)