#Yoga on instagram: Understanding the nature of yoga in the online conversation and community
Bibliographic record
Abstract
Background and Aim: The purpose of the present study was to investigate #yoga on Instagram to better understand the nature of who is posting about yoga, in addition to whether the traditional teachings are present. Methods: A multimethod approach was utilized for this study. Using the Netlytic program, a text and content analysis ( n = 35,000) was conducted to examine authors' captions/comments associated with #yoga collected over 9 days. An image and caption coding scheme was developed and used to analyze 100 unique authors and images from the larger dataset. Results: The text analysis revealed #fitness was the most cited word ( n = 5491), suggesting an emphasis on the physical aspect of yoga. The content analysis suggested that the majority of words were categorized as good feelings ( n = 32,747; 51%) and appearance ( n = 30,351; 42%), while only a small amount was categorized as traditional teachings ( n = 1703; 3%). Images revealed mostly women ( n = 89; 89%), who were underweight ( n = 68; 68%), in minimal clothing (70%), demonstrating a basic pose ( n = 51; 51%), in an indoor environment ( n = 57; 57%). Conclusion: According to the text, content, and image analyses, #yoga on Instagram seems to emphasize the physical nature of yoga as consistent with the commercialization of yoga and not traditional teachings of the practice.
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How this classification was reachedexpand
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".