Social Media and the Evolution of Vaccine Preferences During the COVID-19 Pandemic: Discrete Choice Experiment
Bibliographic record
Abstract
Background: Vaccine information and misinformation are spread through social media in ways that may vary by platform. Understanding the role social media plays in shaping vaccine preferences is crucial for policymakers and researchers. Objective: This study aims to test whether social media use is associated with changes in vaccine preferences during the COVID-19 pandemic in New Zealand, and whether trust in sources of information has a moderating role. Methods: Our data consist of a balanced panel of 257 web-based respondents in New Zealand in August 2020, October-November 2020, and March-April 2021. We use a novel approach with stated choice panel data to study transitions between different vaccine preference groups. We analyze the associations between these transitions and social media use. We classify respondents as resistant (never chose a vaccine), hesitant (chose a vaccine between 1 and 5 times), and provaccine (chose a vaccine 6 out of 6 times) in each wave of data. Results: We found a positive or neutral association between social media use and vaccine uptake. Facebook, Twitter (pre-2022), and TikTok users who are provaccine are less likely to become hesitant or resistant. Facebook and Instagram users who are hesitant are more likely to become pro. Some social media platforms may have a more positive association with vaccine uptake preferences for those who do not trust the government. Conclusions: The paper contributes to the wider literature, which shows social media can be associated with reinforcing both pro and antivaccination sentiment, and these results depend on where individuals get their information from and their trust in such sources.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".