How are PreLaunch online movie reviews related to box office revenues?
Bibliographic record
Abstract
This paper studies the dynamic patterns of the prelaunch online movie reviews, or movie electronic word-of-mouth (eWOM), over time and investigates their relations to the subsequent box office revenues. The volume and valence of prelaunch eWOM have been shown to be early indicators of strong or weak box office. The time patterns of prelaunch eWOM evolution, which are essentially functional data, on the other hand, tend to be overlooked. We apply the functional principal component analysis, a dimension reduction technique in functional data analysis, to analyze the dynamic patterns of various quantile trajectories of the movie eWOM, instead of directly studying the whole eWOM functional data. The functional principal component (FPC) scores of quantile trajectories at various quantile levels are used to predict the box office revenues. We use the sparse group lasso method to select the quantile levels and individual FPC scores that make significant contributions to the prediction of box office revenues. The results show that compared with other measures, such as valence and variance, the top-end quantiles would be a better measure in capturing the relations between the prelaunch product ratings time pattern and launch sales.
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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 | 0.001 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".