#Nicotineaddiction on TikTok: A quantitative content analysis of top-viewed posts
Bibliographic record
Abstract
INTRODUCTION: TikTok, the video-sharing app popular among youth, is a source of user-generated content about nicotine addiction with the potential to endorse or deter nicotine use among young viewers. We systematically analyzed content and themes of TikTok posts tagged #nicotineaddiction. METHODS: We conducted a quantitative content analysis of the visual and textual content of the 149 top-viewed English-language TikTok posts tagged #nicotineaddiction as of 1 March 2021. Posts were double-coded using a shared codebook, noting content creator characteristics, nicotine products featured, references to quitting, and overall themes of #nicotineaddiction expressed. We assessed the prevalence of post characteristics and themes overall and by apparent age of content creators (aged ≥21 years versus <21 years). RESULTS: The 149 posts analyzed received a mean and median of 62433 and 15800 likes, respectively. E-cigarettes were referenced or featured in 75% of posts; 58% featured a specific nicotine product brand, most commonly Puff Bar (23% of total) and JUUL (19%). Overall, 22% of posts mentioned quitting nicotine. The top themes of #nicotineaddiction expressed were physical or psychological consequences (e.g. withdrawal symptoms, 46%), physical or psychological benefits (e.g. tasting good, feeling 'buzzed', 28%), and social benefits (e.g. bonding with fellow users, 28%). Compared to those aged ≥21 years, posts by content creators likely <21 years (26%) less commonly mentioned quitting (p<0.01), had fewer followers (p<0.01), were more commonly from Canada (p<0.01) and less commonly from the US (p<0.01), and more commonly featured JUUL (p<0.05). CONCLUSIONS: While reaching a large and engaged audience, TikTok content creators suggest a range of benefits and consequences of nicotine addiction. Future research is warranted to examine this content's potential to influence young people's intentions to use or quit nicotine products.
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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 | 0.000 | 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) | 0.001 | 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".