Pro- and Antifluoride Use Messages on YouTube in Japan: Content Analysis
Bibliographic record
Abstract
Background: Dental caries is one of the most prevalent chronic conditions globally. In Japan, fluoride application-mainly via toothpaste, mouth rinses, and professional treatments-is a key preventive measure, as community water fluoridation is not implemented. Despite its proven effectiveness, fluoride use faces opposition from certain groups citing potential health risks. Social media platforms, especially YouTube, have become major sources of health information, but also facilitate the spread of misinformation, which may influence public perceptions and behaviors toward fluoride use. Objective: This study aimed to analyze YouTube videos addressing fluoride use for caries prevention, focusing on the types of information presented and comparing the messages shared by proponents and opponents of fluoride use. Methods: A comprehensive search was conducted on YouTube using fluoride-related keywords in Japanese. The top 50 videos for each keyword were screened, and after excluding irrelevant or duplicate content, 86 videos were analyzed. Videos were categorized as proponent ("pro"), opponent ("anti"), or others. The sources, intended audiences, and content themes were assessed. Interrater reliability was confirmed using the Cohen κ coefficient. Results: Of the 86 analyzed videos, 58% (n=50) were categorized as "pro," 22% (n=19) as "anti," and 20% (n=17) as others. Proponent videos, mainly produced by dental professionals, emphasized scientific evidence, such as the mechanism of fluoride in preventing caries and guideline-based recommendations. Opponent videos, largely uploaded by laypersons, highlighted potential dangers of fluoride, including health risks and additives, and frequently promoted fluoride-free products. Opponent videos had higher daily viewership and engagement than proponent videos. Conclusions: Anti-fluoride content on YouTube appears to reach broader audiences than expert-generated profluoride videos. Opponent messages tend to use emotionally charged communication, whereas proponents focus on scientific information. These differences in style may influence public perceptions of fluoride use. Public health professionals should develop engaging and accessible communication strategies to counter misinformation and promote evidence-based practices.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".