Seasonal body image dissatisfaction: a bi-hemispheric panel analysis of social media users across 4 years
Bibliographic record
Abstract
PURPOSE: Seasonal body image refers to within-person variations in body image satisfaction that correspond with climatic seasonality (winter, spring, summer, and autumn). Previous cross-sectional research involving male participants from northern (UK, USA, and Canada) and southern hemisphere (Australia) nations reports a peak in body image dissatisfaction during the summertime, with a decrease in the wintertime. Big Data extracted from social media platforms provides a novel means of further exploring the seasonal body image hypothesis in a larger and more diverse sample across several years. METHODS: This study utilised panel data drawn from X/Twitter, a social media platform, to investigate the posts (N = 12,017,766) of users/authors (N = 1534) between 2020 and 2023. The panel consisted of authors from countries in both the northern and southern hemispheres. A template-driven search algorithm identified expressions of body image dissatisfaction (BID) in users' posts. RESULTS: The rate of BID (relative to the overall number of posts) was calculated for each calendar month. A statistically significant summer spike was observed in the Northern hemisphere, while the data were non-significant but directionally supportive of a similar summer spike in the Southern hemisphere. CONCLUSIONS: This study partially supports the seasonal body image hypothesis, adding nuance to the current understanding of seasonality. This research has implications for the timing of public health initiatives aimed at preventing body image issues and eating disorders. LEVEL OF EVIDENCE IV: Evidence obtained from multiple time series without intervention.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".