Box office sales and social media: A cross-platform comparison of predictive ability and mechanisms

• Published in: Decision Support Systems Vol. 147; p. 113517
• Main Authors: Bogaert, Matthias, Ballings, Michel, Van den Poel, Dirk, Oztekin, Asil
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01.08.2021; Elsevier Sequoia S.A
• Subjects: Algorithms; Box office sales; Data mining; Digital media; Machine learning; Marketing; Marketing analytics; Performance prediction; Prediction models; Predictive analytics; Sales; Sensitivity analysis; Social media; Social networks; User generated content; Marketing analytics; Box office sales; Data mining; Predictive analytics; Social media; Machine learning
• ISSN: 0167-9236; 1873-5797
• DOI: 10.1016/j.dss.2021.113517

This paper aims to determine the power of social media data (Facebook and Twitter) in predicting box office sales, which platforms, data types and variables are the most important and why. To do so, we compare several models based on movie data, Facebook data, and Twitter data. We benchmark these model comparisons using various prediction algorithms. Next, we apply information-fusion sensitivity analysis to evaluate which variables are driving the predictive performance. Our analysis shows that social media data significantly increases the predictive power of traditional box office prediction models. Facebook data clearly outperform Twitter data and including user-generated content next to marketer-generated always improves predictive power. Our sensitivity analysis reveals that volume and valence based combination variables pertaining to Facebook comments are the most important variables. Furthermore, we provide an in-depth analysis of the potential mechanisms driving differential predictive ability of Facebook and Twitter. Our findings suggest that Twitter has less of an impact on box office sales than Facebook because Twitter users have less source credibility than Facebook users. Our results are important for practitioners, marketers and academics who want to employ social media data for box office sales predictions. •We assess the value of social media data over movie data in predicting movie sales•We provide a theoretical framework to explain the differential predictive power•Facebook data is more indicative in predicting movie sales than Twitter data•User-generated content on Twitter suffers from a too-good-to-be-true-effect•Volume-based user-generated content variables are the most important