Comparing twitter and traditional media using topic models

  • Authors:

  • Wayne Xin Zhao;Jing Jiang;Jianshu Weng;Jing He;Ee-Peng Lim;Hongfei Yan;Xiaoming Li

  • Affiliations:

  • Peking University, China;Singapore Management University, Singapore;Singapore Management University, Singapore;Peking University, China;Singapore Management University, Singapore;Peking University, China;Peking University, China

  • Venue:
  • ECIR'11 Proceedings of the 33rd European conference on Advances in information retrieval
  • Year:
  • 2011

Quantified Score

Hi-index 0.00

Visualization

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.