Conference Proceeding Article
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.
Twitter, microblogging, topic modeling
Databases and Information Systems | Numerical Analysis and Scientific Computing
Data Management and Analytics
European Conference on Information Retrieval (ECIR) 33rd, Dublim, 18-21 April
City or Country
Zhao, Wayne X., Jiang Jing, Wng Jianshu, He Jing, Lim Ee-Peng, Yan Hongfei and Li Xiaoming. 2011. Comparing Twitter and Traditional Media Using Topic Models. In Advances in Information Retrieval: 33rd European Conference on IR Research, ECIR 2011, Dublin, Ireland, April 18-21, 2011. Proceedings. Berlin: Springer Verlag.
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This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.