YouTube as a source of misinformation on COVID-19 vaccination: a systematic analysis
Bibliographic record
Abstract
Introduction Vaccines for SARS-CoV-2 have been accessible to the public since December 2020. However, only 58.3% of Americans are fully vaccinated as of 5 November 2021. Numerous studies have supported YouTube as a source of both reliable and misleading information during the COVID-19 pandemic. Misinformation regarding the safety and efficacy of COVID-19 vaccines has negatively impacted vaccination intent. To date, the literature lacks a systematic evaluation of YouTube’s content on COVID-19 vaccination using validated scoring tools. The objective of this study was to evaluate the accuracy, usability and quality of the most widely viewed YouTube videos on COVID-19 vaccination. Methods A search on YouTube was performed on 21 July 2021, using keywords ‘COVID-19 vaccine’ on a cleared-cache web browser. Search results were sorted by ‘views’, and the top 150 most-viewed videos were collected and analysed. Duplicate, non-English, non-audiovisual, exceeding 1-hour duration, or videos unrelated to COVID-19 vaccine were excluded. The primary outcome was usability and reliability of videos, analysed using the modified DISCERN (mDISCERN) score, the modified Journal of the American Medical Association (mJAMA) score and the COVID-19 Vaccine Score (CVS). Results Approximately 11% of YouTube’s most viewed videos on COVID-19 vaccines, accounting for 18 million views, contradicted information from the WHO or the Centers for Disease Control and Prevention. Videos containing non-factual information had significantly lower mDISCERN (p<0.001), mJAMA (p<0.01) and CVS (p<0.001) scores compared with videos with factual information. Videos from government sources had higher mJAMA and CVS scores, but averaged three times the ratio of dislikes to likes, while videos containing non-factual information averaged 14 times more likes than dislikes. Conclusion As the COVID-19 pandemic evolves, widespread adoption of vaccination is essential in reducing morbidity, mortality, and returning to some semblance of normalcy. Providing high-quality and engaging health information from reputable sources is essential in addressing vaccine hesitancy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 | 0.094 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.017 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".