An Investigation of Age-Differentiated Conversations About Electronic Nicotine Delivery Systems on Reddit
Bibliographic record
Abstract
Introduction: This study analyzes age-differentiated Reddit conversations about ENDS. Methods: This study combines 2 methods to (1) predict Reddit users' age into 2 categories (13-20 years [underage] and 21-54 years [of legal age]) using a machine learning algorithm and (2) qualitatively code ENDS-related Reddit posts within the 2 groups. The 25 posts with the highest karma score (number of upvotes minus number of downvotes) for each keyword search (i.e., query) and each predicted age group were qualitatively coded. Results: Of 9, the top 3 topics that emerged were flavor restriction policies, Tobacco 21 policies, and use. Opposition to flavor restriction policies was a prominent subcategory for both groups but was more common in the 21-54 group. The 13-20 group was more likely to discuss opposition to minimum age laws as well as access to flavored ENDS products. The 21-54 group commonly mentioned general vaping use behavior. Conclusions: Users predicted to be in the underage group posted about different ENDS-related topics on Reddit than users predicted to be in the of-legal-age group.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".