An Investigation of Age-Differentiated Conversations About Electronic Nicotine Delivery Systems on Reddit
Bibliographic record
Abstract
•Machine learning and qualitative coding provide context to social media analysis.•Predicted Reddit user age groups allows nuanced comparisons on thematic topics by age group.•Opposition to flavor restrictions was prominent for both age groups.•Emergent themes by the age group 13–20 years were opposition to minimum age laws and flavored ENDS discussions.•Posts by the age group 21–54 years commonly mentioned general vaping use behavior. IntroductionThis study analyzes age-differentiated Reddit conversations about ENDS.MethodsThis 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.ResultsOf 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.ConclusionsUsers 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 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 | 0.001 |
| 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".