Content analysis of conversations on Reddit: reactions to FDA’s ENDS prioritized enforcement policy
Bibliographic record
Abstract
Introduction: On January 2, 2020, the FDA announced a policy focused in part on prioritizing enforcement of flavored (other than tobacco- or menthol-flavored) cartridge-based electronic nicotine delivery systems (ENDS) without premarket authorization. Methods: We used a query to identify Reddit conversations relevant to the policy from January 2 to May 6, 2020. Our sample included 576 posts (46 posts and 530 accompanying comments). Two analysts coded posts for mentions of use behaviors (e.g., switching, quitting), purchasing behaviors (e.g., purchasing from retailer new to the user), and flavored products. We summarized frequencies of coded data and provided illustrative quotes. Results: Only 21.0% (121/576) of posts mentioned use behavior. Switching behavior was the most common use behavior mentioned (50.4%, 61/121). Most switching behavior posts focused on ENDS-related switching (91.8%, 56/61). The most common ENDS-related switching behaviors mentioned were switching to an open tank (45.9%, 28/61) or device with refillable pods/cartridges (44.3%, 27/61); 8.2% (5/61) mentioned switching to disposables. Just 15.5% (89/576) of posts mentioned purchasing behavior, with the most common being purchasing from a retailer new to the user (32.6%, 29/89). Only 6.8% (39/576) of posts mentioned specific flavors. Conclusion: Reddit posts about the policy commonly discussed switching to non-cartridge-based ENDS products, such as open tank systems or disposable devices, and purchasing products from different online sources that were still selling these products. Findings suggest that publicly available Reddit data can complement data from traditional sources (e.g., surveys, sales) to understand potential unintended consequences associated with policies by exploring the public's reactions.
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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 | 0.009 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".