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