The Necessity of Regulating Drinking Scenes on Social Media Platforms Focusing on YouTube Sulbang Videos: Public Opinion From Surveys and YouTube Content Analysis
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,006 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,003 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».