#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 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 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".