#Bartender: portrayals of popular alcohol influencer’s videos on TikTok©
Bibliographic record
Abstract
BACKGROUND: Despite widespread use of the short-video social media platform TikTok©, limited research investigates how alcohol is portrayed on the platform. Previous research suggests that a driver of alcohol content on TikTok©, in part, comes from bartenders demonstrating how to make drinks. This study aims to explore the characterizing patterns of how bartender influencers on TikTok© feature and incorporate alcohol in their videos. METHODS: We identified the global top 15 most followed bartenders on TikTok© in 2021 (cumulative 29.7 million subscribers) and the videos they posted in November and December 2021, the period just before Christmas and New Year, when alcohol tends to be more marketed than in other periods. The videos were coded based on five criteria: (1) the presence of alcohol or not; (2) alcohol categories; (3); alcohol brand(s) if visible; (4) the presence of candies and other sweet products; (5) presence of cues that refer to young people's interests. RESULTS: In total, we identified 345 videos, which received 270,325,600 views in total, with an average of 18,021,707 views per video. Among these 345 videos, 92% (n = 317) displayed alcohol in their cocktail recipes (249,275,600 views, with an average of 786,358 views). The most common types of alcohol present in videos were liquor, vodka, rum, and whiskey, all of which are high-ABV beverages. 73% (n = 230) displayed or mentioned an alcohol brand. 17% (n = 55) associated alcohol with sweet products such as different types of candy (53,957,900 views, with an average of 981,053 views per video). 13% (n = 43) contained cues appealing to young people (e.g., cartoons, characters) (15,763,300 views, with an average of 366,588 views per video). CONCLUSIONS: Our findings suggest a large presence of positively framed alcohol content posted by popular bartenders on TikTok©. As exposure to digital marketing is related to an increase in alcohol consumption, particularly among young people, regulations are needed to protect the public from alcohol-related harms.
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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 | 0.000 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 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".