Online Health Communities’ Portrayal of Obesity on Social Media Platforms in South Africa
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The rapidly increasing prevalence of obesity in South Africa, intertwined with extensive changes in diet, life expectancy, and nutritional status has led to a complex framing of obesity on social media. This has prompted the prioritization of media-based social and behavior change communication interventions leveraging social media for obesity prevention. This study was conducted to understand how obesity is constructed and represented on social media in South Africa. A media review of Facebook and Twitter platforms in South Africa was conducted over a six-month period using Meltwater software for data collection. The search yielded 13 500 posts and tweets. Data were cleaned and coded in Microsoft Excel. Content and framing analysis were performed to add insight into the nature of obesity discourse on social media. Portrayals of obesity on social media were dominated by stigmatizing imagery blaming individuals for unhealthy lifestyles, poor diets, and lack of physical activity. Future media-based social and behavior change communication interventions for obesity prevention can leverage social media to reach the broader public and insights into media portrayals of obesity have the potential to influence the shape and development of these behavioral interventions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it