A Review of Food-Related Social Media and Its Relationship to Body Image and Disordered Eating
Bibliographic record
Abstract
BACKGROUND/OBJECTIVES: Appearance-related social media, such as "thinspiration" and "fitspiration" posts, have been shown to contribute to poor body image and disordered eating. Food-related social media is becoming increasingly popular; however, far less is known about its relationship to body image and disordered eating. METHODS: The current review searched PsycNet and PubMed (Medline) for all the literature examining food-related social media and its relationship with body image and/or disordered eating outcomes. RESULTS: From 796 initial hits, the search identified 16 relevant studies. The study designs and types of media examined varied widely, including mukbang videos, food blogs, and "What I Eat In A Day" videos. Findings on the relationship between food-related social media and outcome variables were quite mixed, perhaps speaking to the wide variety of media included in the review. CONCLUSIONS: The existing literature is sparce, but overall, it suggests a potential relationship between food-related social media, negative body image, and disordered eating. Additional experimental research is needed to clarify outcomes for different media types (e.g., food blogs versus mukbang videos) and to determine the direction of causality for each.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".