Bibliographic record
Abstract
This study investigated polarization on Twitter related to the COVID-19 pandemic by examining tweets containing #Plandemic (suggests the pandemic is a hoax) or #StayHome (encourages compliance with health recommendations). Over 35,000 tweets from over 25,000 users were collected in April 2020 and examined using sentiment and social network analyses. Compared to #StayHome tweets, #Plandemic tweets came from a more tightly connected network, were higher in negative emotional content, and could be sub-divided into specific categories of misinformation and conspiracy theories. To evaluate the stability of users' COVID-related perspectives, the prevalence of pro- and anti-mask sentiment was measured in same users' tweets approximately four months later. Results revealed substantial stability over time, with 40% of #Plandemic users tweeting anti-mask hashtags compared to just 2% of #StayHome users. Findings demonstrate COVID-related polarization on Twitter and have implications for public health interventions to quell the propagation of misinformation.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".