Characterizing the Gendered Twitter Discussion of COVID-19 Hoax
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
We collected data from Twitter and used content analysis to better understand the gendered discussion around COVID-19 as a hoax. We identified three main categories in the inductive stage of the research: (1) sympathetic to human rights & perceived injustice, (2) invincibility and superiority of COVID hoaxers, (3) conspiracies and/or hidden agendas. The findings of the study show that among all gender groups, the first category is the most dominant (44.4%), the third category is the second most frequent (35.6%), and the last category (19.9%) is the least frequent. However, when the discussion is centered on men (40.2%) and gender and sexual minorities (GSM; 69.6%) groups, the last category is the most dominant with regard to stigmatizing GSM groups by falsely associating them with progressive secret agendas. As for women's group, being sympathetic to human rights and the perceived injustice against them during the pandemic constitute the most dominant category (51.5%). We discuss the implications of the study in the conclusion.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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