Does Topic Consistency Matter? A Study of Critic and User Reviews in the Movie Industry
Bibliographic record
Abstract
Online review platforms often present reviews from both critics and general users. In this research, the authors propose a measure called “topic consistency” to capture the degree of overlap between critic and user review content. High topic consistency suggests greater information recall due to repeated presentation of the same topics, which may increase the memorability of movie attributes and therefore positively affect movie demand. The authors measure the topic consistency between critic and user reviews using topic models and further study the financial consequences of this measure using data for movies released in the United States. Topic consistency is positively associated with subsequent box office revenue, suggesting a positive relationship between topic consistency and movie demand. Furthermore, the effect of topic consistency on demand is the greatest for movies with mediocre review ratings and when the review ratings from critics are close to those from users. Using lab experiments, the authors provide evidence of the causal link between topic consistency and consumers’ willingness to watch a movie, and support for the potential mediation through the information recall of reviews. Movie producers and advertisers should consider highlighting or inducing a central theme for critics and users to discuss, as the more the review content of critics and users overlaps, the higher a movie's revenue.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.096 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".