Who do they think they are? A quantitative content analysis of exercise bloggers and their blogs
Bibliographic record
Abstract
Social media, including blogs, are popular conduits of exercise information that may influence the reader's thoughts and behaviours. It is unknown however, how bloggers represent themselves online, if they are qualified to give exercise advice, and what types of information they most commonly share on their blogs. This may cause confusion for readers and has the potential to contribute to misinformation, or unhealthy behaviours (e.g., exercise addiction). The current study used quantitative content analysis to examine the features of 194 popular fitness and exercise blogs, with a focus on blog authors. Additionally, 722 content pages from the blogs were analyzed for content type, post format, and interactive features. Results suggest that only 16.4% of bloggers report having fitness/exercise certifications although 57% report being a fitness/exercise professional. In addition to fitness/exercise, blog posts included content about related topics such as nutrition, and unrelated topics like fashion and politics. Blogs were highly interactive with 76.3% including comments sections. Most blogs included multimediality for content sharing with Facebook (90.7%), Twitter (86.1%), and Instagram (68.0%) most predominant. Blogs may provide an online space for like-minded exercisers to connect, foster a community of support, and learn more about various exercise modalities and facilities. Yet given the ambiguity of authorship, consumers may be left unaware if fitness and exercise bloggers are exercise experts, and whether or not blog content is a reliable source of exercise information.
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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 | 0.009 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".