Box office sales and social media : a cross-platform comparison of predictive ability and mechanisms
- Author
- Matthias Bogaert (UGent) , Michel Ballings, Dirk Van den Poel (UGent) and Asil Oztekin
- Organization
- Abstract
- 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.
- Keywords
- Arts and Humanities (miscellaneous), Information Systems and Management, Management Information Systems, Developmental and Educational Psychology, Information Systems, Predictive analytics, Social media, Machine learning, Data mining, Box office sales, Marketing analytics, USER-GENERATED CONTENT, WORD-OF-MOUTH, ENGAGEMENT, PERFORMANCE, FACEBOOK, BEHAVIOR, REVIEWS, TWITTER, IMPACT, SENTIMENT
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Citation
Please use this url to cite or link to this publication: http://hdl.handle.net/1854/LU-8695508
- MLA
- Bogaert, Matthias, et al. “Box Office Sales and Social Media : A Cross-Platform Comparison of Predictive Ability and Mechanisms.” DECISION SUPPORT SYSTEMS, vol. 147, 2021, doi:10.1016/j.dss.2021.113517.
- APA
- Bogaert, M., Ballings, M., Van den Poel, D., & Oztekin, A. (2021). Box office sales and social media : a cross-platform comparison of predictive ability and mechanisms. DECISION SUPPORT SYSTEMS, 147. https://doi.org/10.1016/j.dss.2021.113517
- Chicago author-date
- Bogaert, Matthias, Michel Ballings, Dirk Van den Poel, and Asil Oztekin. 2021. “Box Office Sales and Social Media : A Cross-Platform Comparison of Predictive Ability and Mechanisms.” DECISION SUPPORT SYSTEMS 147. https://doi.org/10.1016/j.dss.2021.113517.
- Chicago author-date (all authors)
- Bogaert, Matthias, Michel Ballings, Dirk Van den Poel, and Asil Oztekin. 2021. “Box Office Sales and Social Media : A Cross-Platform Comparison of Predictive Ability and Mechanisms.” DECISION SUPPORT SYSTEMS 147. doi:10.1016/j.dss.2021.113517.
- Vancouver
- 1.Bogaert M, Ballings M, Van den Poel D, Oztekin A. Box office sales and social media : a cross-platform comparison of predictive ability and mechanisms. DECISION SUPPORT SYSTEMS. 2021;147.
- IEEE
- [1]M. Bogaert, M. Ballings, D. Van den Poel, and A. Oztekin, “Box office sales and social media : a cross-platform comparison of predictive ability and mechanisms,” DECISION SUPPORT SYSTEMS, vol. 147, 2021.
@article{8695508,
abstract = {{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.}},
articleno = {{113517}},
author = {{Bogaert, Matthias and Ballings, Michel and Van den Poel, Dirk and Oztekin, Asil}},
issn = {{0167-9236}},
journal = {{DECISION SUPPORT SYSTEMS}},
keywords = {{Arts and Humanities (miscellaneous),Information Systems and Management,Management Information Systems,Developmental and Educational Psychology,Information Systems,Predictive analytics,Social media,Machine learning,Data mining,Box office sales,Marketing analytics,USER-GENERATED CONTENT,WORD-OF-MOUTH,ENGAGEMENT,PERFORMANCE,FACEBOOK,BEHAVIOR,REVIEWS,TWITTER,IMPACT,SENTIMENT}},
language = {{eng}},
pages = {{15}},
title = {{Box office sales and social media : a cross-platform comparison of predictive ability and mechanisms}},
url = {{http://doi.org/10.1016/j.dss.2021.113517}},
volume = {{147}},
year = {{2021}},
}
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