From Hashtags to Headlines: Applying Framing Theory to COVID-19 Vaccine Misinformation across Social Platforms and Cable News
Bibliographic record
Abstract
This case study analyzes the dissemination of COVID-19 misinformation through the lens of framing theory. Social media misinformation has dramatically affected people’s vaccination intentions, and framing is the method by which people develop a clear concept regarding a problem. Drawing on the KFF COVID-19 Vaccine Monitor and a three-month public opinion monitor of over 1,000 posts on Twitter and Facebook, the study examines two prominent sources shaping online discourse: partisan leadership (e.g., Governor Ron DeSantis) and cable news (e.g., Fox News). Using Hallahan’s models of framing—especially framing of issues and framing of news—the analysis shows how selective emphasis, wording, and agenda building present “objective facts” in ways that mobilize skepticism toward vaccines and reinforce partisan divides. Findings indicate that persistent negative frames (e.g., “vaccines do not prevent infection”) travel widely on social platforms and interact with political identity to lower vaccination intent. The article discusses implications for public health communication and media practice in mitigating the effects of the infodemic.
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 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 | 0.012 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.009 | 0.017 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".