Do Spoilers Really Spoil? Using Topic Modeling to Measure the Effect of Spoiler Reviews on Box Office Revenue
Bibliographic record
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".