No shots, no problem: The anti-vaccination movement on social media
Bibliographic record
Abstract
Vaccine-hesitancy and vaccine rejection are huge problems facing global public health today. Diseases which were once considered to be virtually eliminated due to widespread vaccination are returning with a vengeance, as cases of measles, mumps and chickenpox pop up and spread throughout Europe and North America. As herd immunity diminishes, deadly infectious diseases spread more easily among vulnerable human hosts. This is all due to the growing trend of purposefully forgoing routine vaccinations. Anti-vaccine communities have been steadily growing online for years, and now wield a potent influence on social media. Widespread access to social media increases the ease with which misinformation about vaccines can spread and reach new audiences across the globe. In this paper, I will be seeking to answer the question: what is the anti-vaccine movement and how does this movement interact with social media? I will also examine what role identity plays in attracting new believers to the anti-vaccine community, if any.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".