Using User- and Marketer-Generated Content for Box Office Revenue Prediction: Differences Between Microblogging and Third-Party Platforms

Tingting Song · Jinghua Huang · Yong Tan · Yifan Yu

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Digital Marketing and Social Media Media and Digital Communication Cinema and Media Studies

Abstract

How to improve the predictive accuracy of box office revenue with social media data is a big challenge and is particularly important for movie distributors and cinema operators. In this research, we find that microblogging UGC (MUGC) is a significant predictor of box office revenue and has stronger predictive power than UGC on Douban! Movies (DUGC) based on our examination of 60 movies released in China in 2012. To increase the attendance rate of movies, cinema operators can consider previous valence and volume of MUGC before scheduling the current film screenings because these messages can quickly predict the future box office revenue of a movie. Besides, we find that the volume of enterprise microblogs (i.e., MGC) can predict both box office revenue and MUGC, indicating that movie distributors should optimize their online media strategy by shifting more resources to utilizing enterprise microblogging. Although rebroadcasting volume from microblogging platforms does not predict box office revenue directly, it can indirectly predict it via MGC. Accordingly, compared with third-party platforms, rebroadcasting as one of the key distinct functions of microblogging platforms also shows its usefulness in box office revenue prediction. Overall, metrics from microblogging platforms are more effective in predicting box office revenue than those from third-party platforms.

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References

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