Exploring the discourse around Zyn nicotine pouches on Instagram and TikTok: A Content Analysis (Preprint)
Bibliographic record
Abstract
Background: Oral nicotine pouches, such as Zyn, have rapidly grown in popularity in the United States, with sales increasing from 83.2 million cans in 2020 to 385 million cans in 2023. This growth has occurred alongside concerns about youth use. At the same time, Zyn's visibility on social media has also expanded, where youth-targeted content may shape perceptions and influence product uptake. Objective: This content analysis of Instagram and TikTok Zyn-related posts aimed to (1) examine their sentiment and content, (2) assess youth appeal, (3) identify potential misinformation, and (4) report the most frequently used hashtags in the selected posts as indicators of platform-specific framing and audience targeting. Methods: In March 2024, we collected 10,502 Instagram posts and 609 TikTok posts with #Zyn. We used a systematically developed codebook to guide the analysis of a random Instagram subsample (n=1200) and all TikTok posts (n=609). Interrater reliability was assessed using a Cohen κ of more than 0.80 and percent agreement. Results: Many coded posts expressed positive sentiment (n=789/887, 88.9%) and normalized Zyn use through comedic content (n=308/887, 35.2%). Posts revealed youth-targeted themes, including appealing flavors (n=417/887, 87.6%); Zyn usage methods (n=159/887, 18.1%); and associations with sports, athletic, and gym settings (n=70/887, 8%). Both platforms contained posts with potential misinformation. While TikTok featured more influencer-generated content, Instagram showcased more business and commercial content. Conclusions: Findings suggest that Zyn-related social media content may appeal to youth. Zyn and other oral nicotine pouches are often presented favorably with engaging content, underscoring the need for updated regulatory strategies to address potential misinformation and their appeal to youth.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".