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.
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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
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