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

Journal of Marketing, Forthcoming

64 Pages Posted: 29 Jun 2020

See all articles by Jun Hyun (Joseph) Ryoo

Jun Hyun (Joseph) Ryoo

University of Western Ontario, Richard Ivey School of Business

Xin (Shane) Wang

University of Western Ontario - Richard Ivey School of Business

Shijie Lu

University of Houston - C.T. Bauer College of Business

Date Written: June 1, 2020

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

Suggested Citation

Ryoo, Jun Hyun (Joseph) and Wang, Xin (Shane) and Lu, Shijie, Do Spoilers Really Spoil? Using Topic Modeling to Measure the Effect of Spoiler Reviews on Box Office Revenue (June 1, 2020). Journal of Marketing, Forthcoming, Available at SSRN: https://ssrn.com/abstract=3619517

Jun Hyun (Joseph) Ryoo

University of Western Ontario, Richard Ivey School of Business ( email )

Ivey Business School
, 1255 Western Road
London, Ontario N6G 0N1
Canada

Xin (Shane) Wang

University of Western Ontario - Richard Ivey School of Business ( email )

1255 Western Road
London, Ontario N6A 3K7
Canada

Shijie Lu (Contact Author)

University of Houston - C.T. Bauer College of Business ( email )

Houston, TX 77204-6021
United States

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