Information Seeking in Social Media: A Review of YouTube for Sedentary Behavior Content
Bibliographic record
Abstract
BACKGROUND: The global prevalence of sedentary lifestyles is of grave concern for public health around the world. Moreover, the health risk of sedentary behaviors is of growing interest for researchers, clinicians, and the general public as evidence demonstrates that prolonged amounts of sedentary time increases risk for lifestyle-related diseases. There is a growing trend in the literature that reports how social media can facilitate knowledge sharing and collaboration. Social sites like YouTube facilitate the sharing of media content between users. OBJECTIVE: The purpose of this project was to identify sedentary behavior content on YouTube and describe features of this content that may impact the effectiveness of YouTube for knowledge translation. METHODS: YouTube was searched on a single day by 3 independent reviewers for evidence-based sedentary behavior content. Subjective data (eg, video purpose, source, and activity type portrayed) and objective data (eg, number of views, comments, shares, and length of the video) were collected from video. RESULTS: In total, 106 videos met inclusion criteria. Videos were uploaded from 13 countries around the globe (ie, Australia, Barbados, Belgium, Canada, Colombia, Kenya, New Zealand, Russia, South Africa, Spain, Ukraine, United Kingdom, United States). The median video length was 3:00 minutes: interquartile range (IQR) 1:44-5:40. On average, videos had been on YouTube for 15.0 months (IQR 6.0-27.5) and had been viewed 239.0 times (IQR 44.5-917.5). Videos had remarkably low numbers of shares (median 0) and comments (median 1). Only 37.7% (40/106) of videos portrayed content on sedentary behaviors, while the remaining 66 videos portrayed physical activity or a mix of behaviors. Academic/health organizations (39.6%, 42/106) and individuals (38.7%, 41/106) were the most prevalent source of videos, and most videos (67.0%, 71/106) aimed to educate viewers about the topic. CONCLUSIONS: This study explored sedentary behavior content available on YouTube. Findings demonstrate that there is confusion between physical activity and sedentary behaviors, that content is being uploaded to the site from around the globe, that content is primarily from health organizations and individuals with the purpose of educating fellow users, but that low views, comments, and shares suggest that sedentary behavior content is relatively underutilized on YouTube. Future research may wish to leverage social platforms, such as YouTube, to facilitate implementation and sharing of evidence-based sedentary behavior content.
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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 | 0.067 | 0.070 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.007 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".