Debates about vaccines and climate change on social media networks: a study in contrasts
Bibliographic record
Abstract
Abstract Vaccines and climate change have much in common. In both cases, a scientific consensus contrasts with a divided public opinion. They also exemplify coupled human–environment systems involving common pool resources. Here we used machine learning algorithms to analyze the sentiment of 87 million tweets on climate change and vaccines in order to characterize Twitter user sentiment and the structure of user and community networks. We found that the vaccine conversation was characterized by much less interaction between individuals with differing sentiment toward vaccines. Community-level interactions followed this pattern, showing less interaction between communities of opposite sentiment toward vaccines. Additionally, vaccine community networks were more fragmented and exhibited numerous isolated communities of neutral sentiment. Finally, pro-vaccine individuals overwhelmingly believed in anthropogenic climate change, but the converse was not true. We propose mechanisms that might explain these results, pertaining to how the spatial scale of an environment system can structure human populations.
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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 | 0.001 | 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 | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".