Mediterranean Diet Information on TikTok and Implications for Digital Health Promotion Research: Social Media Content Analysis
Bibliographic record
Abstract
BACKGROUND: The Mediterranean diet has been linked to reduced risk for several cardiometabolic diseases. The lack of a clear definition of the Mediterranean diet in the scientific literature and the documented proliferation of nutrition misinformation on the internet suggest the potential for confusion among consumers seeking web-based Mediterranean diet information. OBJECTIVE: We conducted a social media content analysis of information about the Mediterranean diet on the influential social media platform, TikTok, to examine public discourse about the diet and identify potential areas of misinformation. We then analyzed these findings in the context of health promotion to identify potential challenges and opportunities for the use of TikTok in promoting the Mediterranean diet for healthy living. METHODS: The first-appearing 202 TikTok posts that resulted from a search of the hashtag #mediterraneandiet were downloaded and qualitatively examined. Post features and characteristics, poster information, and engagement metrics were extracted and synthesized across posts. Posts were categorized as those created by health professionals and those created by nonhealth professionals based on poster-reported credentials. In addition to descriptive statistics of the entire sample, we compared posts created by professionals and nonprofessionals for content using chi-square tests. RESULTS: TikTok posts varied in content, but posts that were developed by health professionals versus nonprofessionals were more likely to offer a definition of the Mediterranean diet (16/106, 15.1% vs 2/96, 2.1%; P=.001), use scientific citations to support claims (26/106, 24.5% vs 0/96, 0%; P<.001), and discuss specific nutrients (33/106, 31.1% vs 6/96, 6.3%; P<.001) and diseases related to the diet (27/106, 25.5% vs 5/96, 5.2%; P<.001) compared to posts created by nonhealth professionals. CONCLUSIONS: Social media holds promise as a venue to promote the Mediterranean diet, but the variability in information found in this study highlights the need to create clear definitions about the diet and its components when developing Mediterranean diet interventions that use new media structures.
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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 | 0.008 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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".