The Necessity of Regulating Drinking Scenes on Social Media Platforms Focusing on YouTube Sulbang Videos: Public Opinion From Surveys and YouTube Content Analysis
Bibliographic record
Abstract
Background: Alcohol consumption is a major risk factor for diseases and social burdens worldwide. Despite this, depictions of alcohol use continue to rise across various social media platforms, increasing concerns about their potential impact, particularly on adolescents. While some guidelines exist to regulate alcohol portrayals in media, they remain largely advisory and lack legal enforcement. As alcohol-related content becomes more widespread on social media, the need for stronger regulatory measures is growing. Objective: This study aimed to analyze the content of sulbang (broadcasts featuring alcohol consumption) on YouTube and to assess public opinions regarding the regulation of alcohol-related broadcasts on social media platforms such as YouTube. Methods: To evaluate public attitudes toward appropriate regulations on alcohol depictions in web-based media, a survey was conducted with 1500 adults (aged 20-74 years) residing in South Korea. Participants were recruited through stratified multistage sampling, with a 21.8% (n=1500) response rate from 6880 invitations. The survey included Likert-scale and rank-ordered questions, with reliability assessed using Cronbach α. Additionally, a content analysis of 318 YouTube (sulbang) videos was conducted based on the Korean government's media alcohol scene guidelines. Two trained coders independently analyzed the videos, achieving high intercoder reliability (Cohen κ=0.92). Results: This study found that exposure to sulbang content was significantly higher among individuals with higher education levels (n=33, 26.2% graduate degree holders), lower income groups (P<.001), and women. Younger individuals and heavy drinkers were also more likely to engage with such content, with heavy drinkers showing a significantly higher likelihood (P<.001). Regarding public opinion, 83.1% (n=1247) of respondents supported some form of regulation on sulbang content. However, heavy drinkers were less inclined to agree (coefficient: -0.3652; P<.001). Age was positively associated with stronger support for regulation (coefficient: 0.21984; P<.001), while women were significantly more likely than men to advocate for stricter restrictions (coefficient: 0.37827; P<.001). Exposure frequency also had the strongest correlation with support for regulation (coefficient: 1.0278; P<.001). The analysis of 318 YouTube videos revealed an average Like ratio of 97.9% (range: 32.7-100.0), indicating predominantly positive viewer responses, with a median Video Power Index of 939.6 (range: 10.4-84,821.7). Content analysis based on the Media Drinking Scene Guidelines showed that 89.0% (n=283) of the videos glorified drinking, often portraying alcohol as a stress reliever or a source of recovery. Additionally, 92.8% (n=295) of the videos depicted binge drinking or drunkenness, and 27.7% (n=88) of the videos featured celebrities or notable figures consuming alcohol. Furthermore, 42.8% (n=136) of the videos presented distorted drinking norms, such as glorifying high tolerance or linking alcohol to sexual advances. In contrast, only 0.6% (n=2) of the videos were age-restricted, and 31.1% (n=99) included any warning message. Conclusions: Given the potential influence of alcohol-related content on drinking perceptions and behaviors, regulatory measures should be explored to mitigate possible risks. Strengthening content guidelines and increasing awareness could help address concerns about alcohol-related social media exposure.
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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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".