Crowdfunding Campaigns and COVID-19 Misinformation
Bibliographic record
Abstract
Objectives. To understand whether and how crowdfunding campaigns are a source of COVID-19–related misinformation. Methods. We searched the GoFundMe crowdfunding platform using 172 terms associated with medical misinformation about COVID-19 prophylaxes and treatments. We screened resulting campaigns for those making statements about the ability of these searched-for or related terms to prevent or treat COVID-19. Results. There were 208 campaigns worldwide that requested $21 475 568, raised $324 305 from 4367 donors, and were shared 24 158 times. The most discussed interventions were dietary supplements and purported immune system boosters (n = 231), followed by other forms of complementary and alternative medicine (n = 24), and unproven medical interventions (n = 15). Most (82.2%) of the campaigns made definitive efficacy claims. Conclusions. Campaigners focused their efforts on dietary supplements and immune system boosters. Campaigns for purported COVID-19 treatments are particularly concerning, but purported prophylaxes could also distract from known effective preventative approaches. GoFundMe should join other online and social media platforms to actively restrict campaigns that spread misinformation about COVID-19 or seek to better inform campaigners about evidence-based prophylaxes and treatments.
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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 | 0.015 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".