Big Social Data Analytics in Journalism and Mass Communication Journal Article uri icon



  • This article presents an empirical study that investigated and compared two “big data” text analysis methods: dictionary-based analysis, perhaps the most popular automated analysis approach in social science research, and unsupervised topic modeling (i.e., Latent Dirichlet Allocation [LDA] analysis), one of the most widely used algorithms in the field of computer science and engineering. By applying two “big data” methods to make sense of the same dataset—77 million tweets about the 2012 U.S. presidential election—the study provides a starting point for scholars to evaluate the efficacy and validity of different computer-assisted methods for conducting journalism and mass communication research, especially in the area of political communication.

publication date

  • June 1, 2016

has restriction

  • closed

Date in CU Experts

  • January 25, 2017 2:47 AM

Full Author List

  • Guo L; Vargo CJ; Pan Z; Ding W; Ishwar P

author count

  • 5

Other Profiles

International Standard Serial Number (ISSN)

  • 1077-6990

Electronic International Standard Serial Number (EISSN)

  • 2161-430X

Additional Document Info

start page

  • 332

end page

  • 359


  • 93


  • 2