Revisiting the Role of Critical Reviews in Film Marketing
Bibliographic record
Abstract
This paper examines the impact of user generated content on film consumption choices. To date, a number of studies have addressed the impact of critical reviews on the performance of films at the box office. These studies were situated in a film marketing environment which preceded web 2.0 and the proliferation of user generated reviews which have resulted from the development of web 2.0 technologies. Literature on the impact which professional reviewers have on consumption of film (and other art forms) has developed separately from considerations of word of mouth. Word of mouth has been acknowledged as a key influencer for arts audiences and prior to the development of user generated content media, such word of mouth has been limited to actual friendship/ peer groups and could be seen as geographically and socially bounded. The development of the user generated review and the increasing importance of sites hosting such reviews can be viewed as a merging of the realms of the professional and amateur critic. This study uses a two stage method of analysis in order to explore the impact which such user generated reviews on the process of consumer choice. Through content analysis of user generated reviews on popular film websites as well as qualitative data collection concerned with consumer selection of film, we have evaluated the impact of user generated content on film choice. This study has implications for film consumers in terms of assisting them in selecting artistic products which fit with their tastes and for film professionals who need to understand how to navigate this new emerging terrain. The aim of this study was to (1) map out how film consumers search for and use reviews in the online environment and to (2) assess how this has changed the influence of and influencers in the area of critical reviews.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".