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

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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.

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Authors and Affiliations

  1. Department of Information Systems, Hanyang University, 222 Wangsimni-ro, Seongdong-gu, Seoul, 133-791, Republic of Korea

    Jongchang Ahn, Ook Lee & Suaini Sura

  2. Department of Media & Technology Management, University of Cologne, Universitaetsstrasse 24, 50923, Koln, Nordreihn-Westfalen, Germany

    Kyungran Ma

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  1. Jongchang Ahn
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  2. Kyungran Ma
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  4. Suaini Sura
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Correspondence to Ook Lee.

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Ahn, J., Ma, K., Lee, O. et al. Do big data support TV viewing rate forecasting? A case study of a Korean TV drama. Inf Syst Front 19, 411–420 (2017). https://doi.org/10.1007/s10796-016-9659-5

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