One-Sided Social Media Comments Influenced Opinions And Intentions About Home Birth: An Experimental Study
Bibliographic record
Abstract
As people increasingly turn to social media to access and create health evidence, the greater availability of data and information ought to help more people make evidence-informed health decisions that align with what matters to them. However, questions remain as to whether people can be swayed in favor of or against options by polarized social media, particularly in the case of controversial topics. We created a composite mock news article about home birth from six real news articles and randomly assigned participants in an online study to view comments posted about the original six articles. We found that exposure to one-sided social media comments with one-sided opinions influenced participants' opinions of the health topic regardless of their reported level of previous knowledge, especially when comments contained personal stories. Comments representing a breadth of views did not influence opinions, which suggests that while exposure to one-sided comments may bias opinions, exposure to balanced comments may avoid such bias.
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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 | 0.000 | 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 | 0.001 | 0.001 |
| 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".