The assessment of the accuracy and credibility of cardiovascular health information on social media platforms
Bibliographic record
Abstract
Abstract Background Cardiovascular diseases (CVD) is a major cause of death worldwide. Effective and accurate dissemination of scientific information is key to addressing this public health problem. Social media platforms play major roles in public health promotion considering that a significant percent of the world population use social media. About 40% of the adults in the US use social media for health information. There is little knowledge about the accuracy and credibility of CVD related information posted on social media platforms. Purpose The study aimed to assess the scientific credibility and accuracy of cardiovascular health information of highly shared social media posts. Methods Cardiovascular related posts on social media platforms (Facebook and Twitter) were mined using the content analyzer platform (BuzzSumo). All articles published between June 2018 and June 2019, with over 10,000 shares, were selected and ranked by two physicians based on the accuracy and credibility of the titles, reported statistics, interpretation of results, sample size, and external validity. A point was scored for each of the five assessment criteria and articles with scores of less than five were considered to be misleading. Results A total of 50 articles, with 3,397,918 shares (median: 44350, IQR:25100–78000), were selected and scored. Misleading articles were 32/50 (64%) with 2,528,800 shares (median: 44350, IQR: 39575–80250). Twenty-six percent of the articles had misleading titles, 16% had misleading reported statistics, 30% had wrong interpretation of results, 36% reported small sample size studies, and 54% of them lacked external validity. Overall, articles on diet (41%) had more misleading titles than articles on other topics (14%), p=0.03. Conclusion More than half of the articles on widely used social media platforms contain misleading information on cardiovascular health. A concerted effort is needed to ensure the accuracy and credibility of public health information shared on these platforms to avoid misleading users. Figure 1 Funding Acknowledgement Type of funding source: None
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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 | 0.068 | 0.390 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.039 | 0.015 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 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".