Do Spoilers Really Spoil? Using Topic Modeling to Measure the Effect of Spoiler Reviews on Box Office Revenue. (March 2021)

• Record Type: Journal Article
• Authors: Ryoo, Jun Hyun (Joseph); Wang, Xin (Shane); Lu, Shijie
• Is Part Of: Journal of marketing. Volume 85:Number 2(2021)
• Page Start: 70
• Page End: 88
• Publication Date: 2021-03
• Subjects: machine learning -- motion pictures -- online word of mouth -- spoilers -- topic modeling; Marketing -- Periodicals; Marketing -- Management -- Periodicals; 658.8005
• DOI: 10.1177/0022242920937703
• Languages: English
• ISSNs: 0022-2429

Abstract

A sizable portion of online movie reviews contain spoilers, defined as information that prematurely resolves plot uncertainty. In this research, the authors study the consequences of spoiler reviews using data on box office revenue and online word of mouth for movies released in the United States. To capture the degree of information in spoiler review text that reduces plot uncertainty, the authors propose a spoiler intensity metric and measure it using a correlated topic model. Using a dynamic panel model with movie fixed effects and instrumental variables, the authors find a significant and positive relationship between spoiler intensity and box office revenue with an elasticity of .06. The positive effect of spoiler intensity is greater for movies with a limited release, smaller advertising spending, and moderate user ratings, and is stronger in the earlier days after the movie's release. Using an event study and online experiments, the authors provide further evidence that spoiler reviews can help consumers reduce their uncertainty about the quality of movies, consequently encouraging theater visits. Thus, movie studios may benefit from consumers' access to plot-intense reviews and should actively monitor the content of spoiler reviews to better forecast box office performance.