Country by Country Comparison of Thumbnail Features Contributing to Views Using AIME for YouTube
Bibliographic record
Abstract
This study proposes a thumbnail feature extraction method that contributes to the number of views on YouTube using AIME and examines the differences in thumbnail features by country using this method. In recent years, video media content, including YouTube, has become scattered all over the Internet and continues to increase enormously. For users to click and play this video media content, it is important to present appropriate thumbnails to creators. However, it is not known what kinds of thumbnail features contribute to the number of views. In this study, we propose a new method to extract thumbnail features that contribute to the number of views using AIME, which can extract features that contribute to the target variable by deriving the approximate inverse operator of a machine learning black box model. Furthermore, we collected YouTube videos from the U.S., Canada, Japan, France, Germany, the U.K., and Australia to show the differences in thumbnail features.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".