Anti-masking Posts on Instagram: Content Analysis During the COVID-19 Pandemic
Bibliographic record
Abstract
Background: The SARS-CoV-2 viral outbreak has been conflicts with the past-tense narrative elsewhere in the abstract.; the infodemic. Misinformation about the virus and disease it causes (COVID-19) has been linked with authority-questioning beliefs, co-branding with conspiracies, and other misinformation across social media. Distrust in simple occupational and public health tools we have at our disposal (like well-fitting face masks) has proliferated. Despite attempts to curb the spread of untrue or misleading information on COVID-19, this messaging persists on social media. Methods: Using a clean and cleared account, the 300 top posts under the hashtag #masksdontwork were collected on Instagram for thematic analysis over three weeks in June 2022, with three separate data collection dates. Themes contained in the posts were independently assessed by two coders and discrepancies were resolved by consensus. Results: The most dominant theme among posts was mistrust, including "government lies" and "media lies." Anti-masking rhetoric was the second most frequent theme, where "freedom" and "disbelief in data" were common sub-themes. Conclusion: Science denial and propaganda shared among Instagram users may represent an onramp to consumption of broader conspiracy theories and government distrust, in addition to having negative health effects and social consequences for workers regardless of whether they wear masks. Social media algorithms promote similar misinformation or authority-questioning beliefs to users who view related content. Addressing the spread of health-related misinformation can assist in deconstructing myths and increasing trust in public health authorities and prevent the spread of communicable diseases among workers and the public.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".