17 Credibility and reach of nutrition influencers on social media
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
<h3>Background</h3> Nutrition influencers can reach large segments of the public, regardless of formal training or credentials. Though social media is a popular source of nutrition information, it may not be credible. Furthermore, the perceived credibility of nutrition information may be enhanced through social validation (i.e., popularity of the public figure), yet this phenomenon has not been examined. <h3>Objective</h3> To examine the credibility of nutrition influencers’ websites in relation to their social media reach. <h3>Methods</h3> Nutrition influencers identified through a key word search on popular search engines: Yahoo! Google, and Bing who had active public websites and Instagram accounts were included. ‘Tips to Spot Misinformation’ developed for the public by the Dietitians of Canada and PEN: Practice Evidence-Based Nutrition were used to create a credibility score for each website. Based on scores, websites were categorized as having low, moderate, or high credibility. The reach of each influencer was ascertained by combining the total number of followers/subscribers from five popular social media platforms (Instagram, Facebook, Twitter, YouTube, and Pinterest). <h3>Results</h3> Of the 39 websites, there were 12 (31%) high, 13 (33%) moderate, and 14 (36%) low credibility sites, and the average number of followers for each group were 186 775, 547 088 and 2 153 515, respectively. There was a significant difference in followers between the three groups (p = 0.017) and a significantly lower number of followers for influencers with high credibility websites compared to low credibility websites (p = 0.022), with more than 10 times fewer followers. <h3>Discussion</h3> Popular influencers with low credibility websites have enormous reach whereas influencers with highly credible websites lack the ability to reach large segments of the population. Further research is needed to understand how social validation influences the public’s eating behaviors and to identify strategies that will increase the visibility of highly credible information.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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