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

Author

Listed:
  • Jongchang Ahn

    (Hanyang University)

  • Kyungran Ma

    (University of Cologne)

  • Ook Lee

    (Hanyang University)

  • Suaini Sura

    (Hanyang University)

Abstract

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.

Suggested Citation

  • Jongchang Ahn & Kyungran Ma & Ook Lee & Suaini Sura, 0. "Do big data support TV viewing rate forecasting? A case study of a Korean TV drama," Information Systems Frontiers, Springer, vol. 0, pages 1-10.
    • Handle: RePEc:spr:infosf:v::y::i::d:10.1007_s10796-016-9659-5
    DOI: 10.1007/s10796-016-9659-5
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    References listed on IDEAS

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    Cited by:

    1. Ajaya Kumar Swain & Ray Qing Cao, 2019. "Using sentiment analysis to improve supply chain intelligence," Information Systems Frontiers, Springer, vol. 21(2), pages 469-484, April.

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    Keywords

    SNS; Big data; TV rating; CGM; AMR; SHR;
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