Going viral: How a single tweet spawned a COVID-19 conspiracy theory on Twitter
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
In late March of 2020, a new hashtag, #FilmYourHospital, made its first appearance on social media. The hashtag encouraged people to visit local hospitals to take pictures and videos of empty hospitals to help “prove” that the COVID-19 pandemic is an elaborate hoax. Using techniques from Social Network Analysis, this case study examines how this conspiracy theory propagated on Twitter and whether the hashtag virality was aided by the use of automation or coordination among Twitter users. We found that while much of the content came from users with limited reach, the oxygen that fueled this conspiracy in its early days came from a handful of prominent conservative politicians and far right political activists on Twitter. These power users used this hashtag to build awareness about the campaign and to encourage their followers to break quarantine and film what is happening at their local hospitals. After the initial boost by a few prominent accounts, the campaign was mostly sustained by pro-Trump accounts, followed by a secondary wave of propagation outside the U.S. The rise of the #FilmYourHospital conspiracy from a single tweet demonstrates the ongoing challenge of addressing false, viral information during the COVID-19 pandemic. While the spread of misinformation can be potentially mitigated by fact-checking and directing people to credible sources of information from public health agencies, false and misleading claims that are driven by politics and supported by strong convictions and not science are much harder to root out.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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