Do Spoilers Really Spoil? Using Topic Modeling to Measure the Effect of Spoiler Reviews on Box Office Revenue

Abstract

A sizable portion of online movie reviews contains 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 between January 2013 and December 2017. 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 more prominent for movies with limited release, smaller advertising spending, and moderate user ratings, and is stronger in earlier days after the movie’s release. These findings are consistent with the mechanism that more intense spoiler reviews can help consumers reduce their uncertainty about the quality of the movie and therefore encourage theater visits. Through online experiments, the authors provide further evidence in support of the uncertainty-reduction mechanism of spoiler reviews. Results from this study suggest that movie studios can benefit from consumers’ access to plot-intense reviews, and should actively monitor the content of spoiler reviews to better forecast box office performance.

Keywords: online word of mouth, spoilers, motion pictures, topic model, machine learning