The Volume and Tone of Twitter Posts About Cannabis Use During Pregnancy: A Scoping Review Protocol
Bibliographic record
Abstract
Introduction: Cannabis use has increased in Canada since its legalization in 2018, including among pregnant women who may be motivated to use the substance to reduce symptoms of conditions, such as nausea and vomiting. However, a growing body of research suggests that cannabis use during pregnancy may harm the developing fetus. Patients increasingly seek medical advice from online sources, but these platforms are often used to spread anecdotal descriptions or misinformation. Given the possible disconnect between online messaging and evidence-based research about the effects of cannabis use during pregnancy, there is a potential for advice taken from social media to cause harm. We propose a scoping review of Twitter to quantify the volume and tone of English-language posts related to cannabis use in pregnancy posted since January 2012. Methods and Analysis: Using Arksey and O’Malley’s framework for scoping reviews, we will collect publicly available posts from Twitter that mention cannabis use during pregnancy and employ the Twitter Application Programming Interface (API) for Academic Research to extract data from included tweets on author, favourites and retweets, health effect mentions, sentiment, and location. These data will be used to quantify how cannabis use during pregnancy is discussed on Twitter and to build a qualitative profile of supportive and opposing posters. Ethics and Dissemination: Research ethics approval is not required for publicly accessible Twitter data. We will disseminate this review’s findings through traditional channels, including preprint and peer-reviewed publications and presentations at academic conferences. In addition, we will share our findings through professional and institutional social media accounts and web pages associated with the research team.
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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 | 0.107 | 0.131 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.019 | 0.012 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.047 | 0.012 |
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".