YouTube as a source of information on COVID-19: a pandemic of misinformation?
Bibliographic record
Abstract
INTRODUCTION: The COVID-19 pandemic is this century's largest public health emergency and its successful management relies on the effective dissemination of factual information. As a social media platform with billions of daily views, YouTube has tremendous potential to both support and hinder public health efforts. However, the usefulness and accuracy of most viewed YouTube videos on COVID-19 have not been investigated. METHODS: A YouTube search was performed on 21 March 2020 using keywords 'coronavirus' and 'COVID-19', and the top 75 viewed videos from each search were analysed. Videos that were duplicates, non-English, non-audio and non-visual, exceeding 1 hour in duration, live and unrelated to COVID-19 were excluded. Two reviewers coded the source, content and characteristics of included videos. The primary outcome was usability and reliability of videos, analysed using the novel COVID-19 Specific Score (CSS), modified DISCERN (mDISCERN) and modified JAMA (mJAMA) scores. RESULTS: Of 150 videos screened, 69 (46%) were included, totalling 257 804 146 views. Nineteen (27.5%) videos contained non-factual information, totalling 62 042 609 views. Government and professional videos contained only factual information and had higher CSS than consumer videos (mean difference (MD) 2.21, 95% CI 0.10 to 4.32, p=0.037); mDISCERN scores than consumer videos (MD 2.46, 95% CI 0.50 to 4.42, p=0.008), internet news videos (MD 2.20, 95% CI 0.19 to 4.21, p=0.027) and entertainment news videos (MD 2.57, 95% CI 0.66 to 4.49, p=0.004); and mJAMA scores than entertainment news videos (MD 1.21, 95% CI 0.07 to 2.36, p=0.033) and consumer videos (MD 1.27, 95% CI 0.10 to 2.44, p=0.028). However, they only accounted for 11% of videos and 10% of views. CONCLUSION: Over one-quarter of the most viewed YouTube videos on COVID-19 contained misleading information, reaching millions of viewers worldwide. As the current COVID-19 pandemic worsens, public health agencies must better use YouTube to deliver timely and accurate information and to minimise the spread of misinformation. This may play a significant role in successfully managing the COVID-19 pandemic.
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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 | 0.013 | 0.085 |
| 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 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".