Abstract
In this study, we applied data mining tools to build a business intelligence platform combining different information, obtained through social media mining, and integrating it with information about the characteristics of movies and their box offices. We used data from the social media following of both the film and its main actors. In addition, data was collected from the number of movie publications on Facebook, Instagram, and Twitter, as well as the corresponding engagement. We built different prediction models of box office revenue by empirically incorporating the effect of the movie’s community on social media (movie and actors’ followers), its activity on social media, and users’ reactions to the movie’s activity. Our analyses are based on Fama-Macbeth two-step regressions applied to panel data. The prediction models confirm the value of social media posts and their engagement, especially on Facebook and on Instagram, to increase the film’s performance. The results show that the volume of the movie’s and actors’ followers, the actions, and the users’ reactions on social media positively affect the box office. Then, the study supports the importance of building a business intelligence system, so companies can take advantage of their social media accounts. Film studios must be proactive with the access and the treatment of different data sources available to the movie’s online community through data mining techniques to attract more viewers and to increase their box office.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
MPAA.: A comprehensive analysis and survey of the theatrical and home entertainment market environment for 2018 (2018). https://www.mpaa.org/wp-content/uploads/2019/03/MPAA-THEME-Report-2018.pdf
Box Office Mojo. https://www.boxofficemojo.com/. Accessed 01 June 2019
Mudambi, S.M., Schuff, D.: What makes a helpful review? A study of customer reviews on Amazon.com (SSRN Scholarly Paper No. ID 2175066). Social Science Research Network, Rochester, NY (2010)
Ding, C., Cheng, H.K., Duan, Y., Jin, Y.: The power of the “like” button: the impact of social media on box office. Decis. Support Syst. 94, 77–84 (2017)
Oh, C., Roumani, Y., Nwankpa, J.K., Hu, H.-F.: Beyond likes and tweets: Consumer engagement behaviour and movie box office in social media. Inf. Manage. 54(1), 25–37 (2017)
Baek, H., Oh, S., Yang, H.-D., Ahn, J.: Electronic word-of-mouth, box office revenue and social media. Electron. Commer. Res. Appl. 22, 13–23 (2017)
Choudhery, D., Leung, C. K.: Social media mining: prediction of box office revenue. In: Proceedings of the 21st International (2017)
Huang, J., Boh, W.F., Goh, K.H.: A temporal study of the effects of online opinions: information sources matter. J. Manag. Inf. Syst. 34(4), 1169–1202 (2017)
Najafi, H.L., Miller, D.W.: Predicting long-term box office performance of movies using social media and its comparison with traditional survey method. Int. J. Res. Rev. Appl. Sci. 26(3), 120–135 (2016)
Vujić, S., Zhang, X.: Does Twitter chatter matter? Online reviews and box office revenues. Appl. Econ. 50(34–35), 3702–3717 (2018)
Duan, W., Gu, B., Whinston, A.B.: Do online reviews matter? —an empirical investigation of panel data. Decis. Support Syst. 45(4), 1007–1016 (2008)
Aral, S., Walker, D.: Creating social contagion through viral product design: a randomized trial of peer influence in networks. Manage. Sci. 57(9), 1623–1639 (2011)
Lovett, A., Munden, G., Saat, M.R., Barkan, C.P.L.: High-speed rail network design and station location: model and sensitivity analysis. Transp. Res. Rec. 2374(1), 1–8 (2013)
Rishika, R., Kumar, A., Janakiraman, R., Bezawada, R.: The effect of customers’ social media participation on customer visit frequency and profitability: an empirical investigation. Inf. Syst. Res. 24(1), 108–127 (2012)
Kerrigan, F.: Film Marketing, 2nd edn. Routledge, London (2017)
Duan, W., Gu, B., Whinston, A.B.: The dynamics of online word-of-mouth and product sales—an empirical investigation of the movie industry. J. Retail. 84(2), 233–242 (2008)
Reinstein, D.A., Snyder, C.M.: The influence of expert reviews on consumer demand for experience goods: a case study of movie critics*. J. Ind. Econ. 53(1), 27–51 (2005)
Liu, Y.: Word of mouth for movies: its dynamics and impact on box office revenue. J. Mark. 70(3), 74–89 (2006)
Stieglitz, S., Dang-Xuan, L., Bruns, A., Neuberger, C.: Social Media Analytics. Bus. Inf. Syst. Eng. 6(2), 89–96 (2014)
Mishne, G., Glance, N.S.: Predicting movie sales from blogger sentiment. In: AAAI Spring Symposium: Computational Approaches to Analyzing Weblogs, pp. 155–158 (2006)
Neter, J., Wasserman, W., Kutner, M.H.: Applied linear regression models. University of Minnesota, Minneapolis (1989)
Lipizzi, C., Iandoli, L., Marquez, J.E.R.: Combining structure, content and meaning in online social networks: the analysis of public’s early reaction in social media to newly launched movies. Technol. Forecast. Soc. Chang. 109, 35–49 (2016)
Bhattacharjee, B., Sridhar, A., Dutta, A.: Identifying the causal relationship between social media content of a Bollywood movie and its box-office success-a text mining approach. Int. J. Bus. Inf. Syst. 24(3), 344–368 (2017)
Liu, T., Ding, X., Chen, Y., Chen, H., Guo, M.: Predicting movie box-office revenues by exploiting large-scale social media content. Multimed. Tools App. 75(3), 1509–1528 (2014). https://doi.org/10.1007/s11042-014-2270-1
Singh, N., Raw, R.S., Chauhan, R.K.: Data mining with regression technique. J. Inf. Syst. Commun. 3(1), 199–202 (2012)
Fan, W., Gordon, M.D.: The power of social media analytics. Commun. ACM 57(6), 74–81 (2014)
He, W., Wu, H., Yan, G., Akula, V., Shen, J.: A novel social media competitive analytics framework with sentiment benchmarks. Inf. Manage. 52(7), 801–812 (2015)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Usero, B., Hernández, V., Quintana, C. (2022). Social Media Mining for Business Intelligence Analytics: An Application for Movie Box Office Forecasting. In: Arai, K. (eds) Intelligent Computing. Lecture Notes in Networks and Systems, vol 283. Springer, Cham. https://doi.org/10.1007/978-3-030-80119-9_65
Download citation
DOI: https://doi.org/10.1007/978-3-030-80119-9_65
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-80118-2
Online ISBN: 978-3-030-80119-9
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)