Do big data support TV viewing rate forecasting? A case study of a Korean TV drama
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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.
KeywordsSNS Big data TV rating CGM AMR SHR
- Engel, J. F., Warshaw, M. R., & Kinnear, T. C. (1991). Promotional Strategy: managing the marketing communication process (p. 13). Boston: Irwin.Google Scholar
- Frank W. B. (2012). What is a RATING. Media Literacy Clearinghouse. Available at: http://www.frankwbaker.com/ratingshare.htm Accessed 7 Nov 2014.
- Fu, T. C., Lee, K. K., Sze, D. C. M., Chung, F. L., & Ng, C. M. (2008). Discovering the Correlation between Stock Time Series and Financial News. In 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, 880–883.Google Scholar
- Gibs, J., & Bruich, S. (2010). Advertising Effectiveness: Understanding the Value of a Social Media Impression. In A special report for ad:tech San Francisco attendees, The Nielsen Company.Google Scholar
- Korea Communications Commission and Korea Internet Security Agency. (2012). A study on status of smart-phone use and internet use in 2011, Working paper, Seoul.Google Scholar
- Liu, B. (2010). Opinion Mining. working paper, Department of Computer Science, University of Illinois, Chicago.Google Scholar
- McKinsey Global Institute. (2011). Big data: the next frontier for innovation, competition, and productivity. Available at http://www.mckinsey.com/ Accessed 25 Dec 2012.
- Miles, J. (2005). Tolerance and variance inflation factor. Encyclopedia of Statistics in Behavioral Science.Google Scholar
- Mittermayer, M.A., & Knolmayer, G. (2006). Text Mining Systems for Market Response to News: A Survey. working Paper, No. 184, The Institute of Information Systems, University of Bern.Google Scholar
- Rafaeli, S., & Sudweeks, F. (1997). Networked interactivity. Journal of Computer-Mediated Communication, 2(4). doi: 10.1111/j.1083-6101.1997.tb00201.x.
- Sehgal, V., & Song, C. (2007). SOPS: Stock Prediction using Web Sentiment Department of Computer Science University of Maryland College Park. In The 7th IEEE International Conference on Data Mining: Workshops, 21–26.Google Scholar
- Tannenbaum, P. (1985). “Play it again, Sam”: repeated exposure to television programs. In Z. D. & J. Bryant (Eds.), Selective exposure to communication (pp. 225–241). Erlbaum: Hillsdale.Google Scholar
- van de Van, A. H., & Ferry, D. L. (1980). Measuring and assessing organization. New York: Wiley Inter-science.Google Scholar