Public Opinions and Attitudes toward Non-invasive Prenatal Testing on Reddit: Content and Sentiment Analysis
Bibliographic record
Abstract
INTRODUCTION: Noninvasive prenatal testing (NIPT) can be used to detect fetal chromosomal abnormalities early in pregnancy. As eligibility criteria broaden and screening targets expand, gauging public acceptability of NIPT becomes increasingly important. Leveraging social media as a rich source of public discourse, the purpose of this study was to understand public opinions and attitudes toward NIPT on the social media platform Reddit. METHODS: We applied content and natural language processing techniques (i.e., sentiment analysis) to textual data collected from 4 Reddit communities focusing on the NIPT content posted from September 2012 to September 2022 (367 posts and 7,822 comments in total). RESULTS: Content analysis findings indicated that social media users consider NIPT to be worthwhile. Reasons NIPT was perceived to be not worthwhile related to unwanted anxiety, and the fact that NIPT results would not change anything about their approach to pregnancy were also expressed. The sentiment analysis identified more positive than negative emotions; the mean sentiment scores ranged from 0.48 to 1.22, depending on the specific Lexicon used. Specific emotions (i.e., trust, fear) were also identified. CONCLUSION: Our novel approach to understanding public perception and attitudes toward NIPT yielded results that are consistent with conventional patient-oriented research methods. These findings may not only contribute to ongoing improvements in prenatal patient care, research, and policy but also indicate that sentiment analysis applied to social media data can serve as a suitable means to assess public acceptability of NIPT, particularly as public dialogue on this topic increases over time.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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".