Analyzing Google Trends Data on AI-Curated YouTube Content, Childhood Obesity, and Sleep Disorders
Bibliographic record
Abstract
Objectives: To use Google Trends to analyze whether interest in AI YouTube content and screen time is associated with trends in childhood obesity and sleep disorders. Background: Excessive screen time in children is linked to obesity, sleep disturbances, vision problems, and frequent headaches. Pediatric experts recommend limiting screen time, creating screen-free zones and encouraging healthy habits to reduce these physical health risks. Methods: Childhood obesity, sleep disorder, screentime, and AI YouTube used as search terms on Google Trends. We gathered search term data for the year 2024 to analyze a connection between all search terms. We displayed the popularity of each search term per week relative to each other from the past 12 months within the United States then gathered and exported the data as a CSV file. Results: AI YouTube’s relative search interest score rose steadily from 56 in January to 83 by July 2024 with a peak of 90 in November (x̄ = 72.528, yearly SD = 11.8). AI YouTube search interest scores also grew in popularity between Quarter 2 and 3 by about 12% .Sleep disorder relative search interest remained stable with an average of 26 - 28 until an uptick to 32 in October (x̄ = 26.717, SD = 3.5). Screentime relative search interest consistently remained within 14 - 17 (x̄ = 15.887, SD = 1.5). Childhood obesity relative search interest grew from 4 in January to 13 in April then falling to 2 in December (x̄ = 8.170, SD = 2.8). Conclusions: This suggests AI YouTube is becoming a popular search term. Sleep disorder and Childhood obesity popularity remains relatively stagnant throughout the year suggesting public interest may not be influenced by this external event. Screentime search interest followed a similar trajectory with Sleep disorder and Childhood obesity but slightly grew in popularity during the fourth quarter.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.012 | 0.016 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".