Information Systems Frontiers

, Volume 19, Issue 2, pp 411–420 | Cite as

Do big data support TV viewing rate forecasting? A case study of a Korean TV drama

  • Jongchang Ahn
  • Kyungran Ma
  • Ook Lee
  • Suaini Sura


This study focuses on big data, including data from social networking sites (SNS), and data that can complement prior researches on TV viewing rate prediction. The paper analyzes the variables, which influence the average minute rating (AMR) and share rating (SHR) through regression analysis after gathering buzz data on a 20-episode drama series in Korea. The R-square value of regression analysis results shows that the consumer-generated media (CGM) variable including SNS items explained 64 % of both AMR and SHR. However, the Media variable is not statistically significant. For SNS items, the Korean SNS me2DAY and DaumYozm are statistically significant for AMR and SHR, but Twitter is not significant. This study contributes to practitioners’ ability to alleviate the hurdles of broadcasting production communities on the difficulty of predicting viewing rate in advance. Thus, it is possible to determine whether to invest production cost persistently or to adjust the broadcasting volume based on viewers’ response.


SNS Big data TV rating CGM AMR SHR 


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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Jongchang Ahn
    • 1
  • Kyungran Ma
    • 2
  • Ook Lee
    • 1
  • Suaini Sura
    • 1
  1. 1.Department of Information SystemsHanyang UniversitySeoulRepublic of Korea
  2. 2.Department of Media & Technology ManagementUniversity of CologneKolnGermany

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