AJIBM  Vol.6 No.3 , March 2016
Television Meets Facebook: The Correlation between TV Ratings and Social Media
Abstract: This study examines the relationship between social media site Facebook and TV ratings drawing from audience factors of integration model of audience behavior. Based on context of Taiwan television network programs, this study collected measures for Facebook likes, shares, comments, posts for three genres of television shows and their Nielsen ratings over a period of eleven weeks, resulting in the size of sample more than 130 observations. This study applied multiple regression models and determined that the key social media measures correlate with TV ratings. In essence, TV shows with higher number of posts and engagement are likely to relate to higher ratings, special in drama shows. Subsequently, this study constructed the TV prediction models with measures for Facebook via SVR. The results suggested that prediction models are a good forecasting of which MAPE was between 10% - 20%, even less than 10%. This implies that TV network should be motivated to invest in social media and engage their audience and analysts can use social media as a mechanism of exante forecasting.
Cite this paper: Cheng, M. , Wu, Y. and Chen, M. (2016) Television Meets Facebook: The Correlation between TV Ratings and Social Media. American Journal of Industrial and Business Management, 6, 282-290. doi: 10.4236/ajibm.2016.63026.

[1]   Napoli, P.M. (2014) Measuring Media Impact an Overview of the Field. Lear Center’s Media Impact Project.

[2]   Facebook Reports Third Quarter 2015 Results (2015)

[3]   Subramanyan, R. (2011) The Relationship Between Social Media Buzz and TV Ratings. Nielsen Media and Entertainment.

[4]   Nielsen (2013) Nielsen Launches’ Nielsen Twitter TV Ratings.

[5]   Nielsen (2016) Nielsen to Launch “Social Content Ratings” with Measurement across Twitter and Facebook.

[6]   Harmony Institute (2013) A Better Way to Analyze Social Impact.

[7]   Harmony Institute (2013) Sharing Influence: Understanding the Influence of Entertainment in Online Social Networks.

[8]   Cheng, M.H., Wu, Y.C., Chen, M.C., Hsieh, B.Y. and Chen, C.C. (2015) A Study of Correlation TV Ratings and Social Media. JDA, 10, 55-58.

[9]   Napoli, P.M. (2011) Audience Evolution: Technology and the Transformation of Media Audience. Columbia University Press, New York.

[10]   Webster, J.G. (2014) The Marketplace of Attention: How Audiences Take Shape in a Digital Age. The MIT Press, Cambridge, MA, London, England.

[11]   Webster, J.G., Phalen, P.F. and Lichty, L.W. (2014) Ratings Analysis: Audience Measurement and Analytics. 4th Edition, Routledge, UK.

[12]   Wakamuya, S., Lee, R. and Sumiya, K. (2011) Towards Better TV Viewing Rates: Exploiting Crowd’s Media Life Logs over Twitter for TV Rating. ICUIMC’11, Seoul, Korea, February 2011, Article No. 39.

[13]   Mhaisgawali, A. and Giri, N. (2014) Detailed Descriptive and Predictive Analytics with Twitter Based TV Ratings. IJCAT, 1, No. 4,

[14]   Hsie, W.T., Chou, S.T., Cheng, Y.H. and Wu, C.M. (2013) Predicting TV Audience Rating with Social Media. IJCNLP, Workshop on Natural Language Processing for Social Media (Social NLP), Nagoya, October 2013, 1-5.

[15]   Huang, Y.Y., Yen, Y. A., Ku, T.W. and Lin, S.D. (2014) A Weight-Sharing Gaussian Process Model Using Web-Based Information for Audience Rating Prediction. Technologies and Applications of Artificial Intelligence Lecture Notes in Computer Science, 19th International Conference, TAAI 1014, Taipei, 21-23 November 2014, 198-208.

[16]   Oh, C., Sasser, S. and Almahmoud, S. (2015) Social Media Analytics Framework: The Case of Twitter and Super Bowl Ads. Journal of Information Technology Management, 26, No.1

[17]   Oh, C. and Yergeau, S. Social Capital, Social Media, and TV Ratings. International Journal of Business Information Systems.

[18]   Lewis, C.D. (1982) International and Business Forecasting Methods. Butterworths, London.