Comparing Twitter and Traditional Media Using Topic Models

  • Wayne Xin Zhao
  • Jing Jiang
  • Jianshu Weng
  • Jing He
  • Ee-Peng Lim
  • Hongfei Yan
  • Xiaoming Li
Part of the Lecture Notes in Computer Science book series (LNCS, 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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References

  1. 1.
    Kwak, H., Lee, C., Park, H., Moon, S.: What is Twitter, a social network or a news media? In: Proceedings of the 19th WWW (2010)Google Scholar
  2. 2.
    Sakaki, T., Okazaki, M., Matsuo, Y.: Earthquake shakes Twitter users: real-time event detection by social sensors. In: Proceedings of the 19th WWW (2010)Google Scholar
  3. 3.
    Asur, S., Huberman, B.A.: Predicting the future with social media. WI-IAT (2010)Google Scholar
  4. 4.
    Petrović, S., Osborne, M., Lavrenko, V.: The Edinburgh Twitter corpus. In: Proceedings of the NAACL HLT 2010 Workshop (2010)Google Scholar
  5. 5.
    Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet allocation. JMLR (2003)Google Scholar
  6. 6.
    Weng, J., Lim, E.P., Jiang, J., He, Q.: TwitterRank: finding topic-sensitive influential twitterers. In: Proceedings of the Third ACM WSDM (2010)Google Scholar
  7. 7.
    Hong, L., Davison, B.D.: Empirical study of topic modeling in Twitter. In: Proceedings of the SIGKDD Workshop on SMA (2010)Google Scholar
  8. 8.
    Steyvers, M., Smyth, P., Rosen-Zvi, M., Griffiths, T.: Probabilistic author-topic models for information discovery. In: SIGKDD (2004)Google Scholar
  9. 9.
    Ramage, D., Dumais, S., Liebling, D.: Characterizing micorblogs with topic models. In: Proceedings of AAAI on Weblogs and Social Media (2010)Google Scholar
  10. 10.
    Titov, I., McDonald, R.: Modeling online reviews with multi-grain topic models. In: Proceeding of the 17th WWW (2008)Google Scholar
  11. 11.
    Li, P., Jiang, J., Wang, Y.: Generating templates of entity summaries with an entity-aspect model and pattern mining. In: Proceedings of the 48th ACL (2010)Google Scholar
  12. 12.
    Zhai, C., Velivelli, A., Yu, B.: A cross-collection mixture model for comparative text mining. In: Proceedings of the Tenth ACM SIGKDD (2004)Google Scholar
  13. 13.
    Paul, M., Girju, R.: Cross-cultural analysis of blogs and forums with mixed-collection topic models. In: Proceedings of the 2009 EMNLP (2009)Google Scholar
  14. 14.
    Leskovec, J., Backstrom, L., Kleinberg, J.: Meme-tracking and the dynamics of the news cycle. In: Proceedings of the 15th ACM SIGKDD (2009)Google Scholar
  15. 15.
    Teevan, J., Ramage, D., Morris, M.: #Twittersearch: A comparison of microblog search and web search. In: Proceedings of the Fourth ACM WSDM (2011)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Wayne Xin Zhao
    • 1
  • Jing Jiang
    • 2
  • Jianshu Weng
    • 2
  • Jing He
    • 1
  • Ee-Peng Lim
    • 2
  • Hongfei Yan
    • 1
  • Xiaoming Li
    • 1
  1. 1.Peking UniversityChina
  2. 2.Singapore Management UniversitySingapore

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