Changes in industry marketing of electronic nicotine delivery systems on social media following FDA's prioritized enforcement policy: a content analysis of Instagram and Twitter posts
Bibliographic record
Abstract
Background In February 2020, FDA prioritized enforcement of flavored (other than tobacco- or menthol-flavored) cartridge-based electronic nicotine delivery systems (ENDS) without premarket authorization. To explore potential marketing changes, we conducted a content analysis of brands' social media posts, comparing devices and flavors before/after the policy. Methods We sampled up to three posts before (November 6, 2019–February 5, 2020) and after the policy (February 6–May 6, 2020) from brands' Instagram ( n = 33) and Twitter ( n = 30) accounts ( N = 302 posts). Two analysts coded posts for device type and flavor. We summarized coded frequencies by device, flavor, and device-flavor combination, and by platform. Results In posts mentioning devices and flavors, those featuring flavored (other than tobacco- or menthol-flavored) cartridge-based devices (before: 2.5%; after: 0%) or tobacco- or menthol-flavored cartridge-based devices (before: 0%; after: 2.8%) were uncommon while any flavor disposables were most common (before: 10.8%; after: 14.6%) particularly after the policy. Half of posts featured devices without flavor (before: 50.0%; after: 50.0%) and one-fifth had no device or flavor references (before: 21.5%; after: 18.8%). Conclusions In the months before and after the policy, it appears ENDS brands were not using social media to market flavored (excluding tobacco- or menthol-flavored) cartridge-based ENDS (i.e., explicitly prioritized) or tobacco- or menthol-flavored cartridge-based devices (i.e., explicitly not prioritized). Brands were largely not advertising specific flavored products, but rather devices without mentioning flavor (e.g., open/refillable, disposable devices). We presented a snapshot of what consumers saw on social media around the time of the policy, which is important to understanding strategies to reach consumers in an evolving ENDS landscape.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".