Emotional Expression and Mental Health Support in BTS Fandom Communities: A Natural Language Processing Study on YouTube Comments (Preprint)
Notice bibliographique
Résumé
BACKGROUND: The global rise of K-pop, particularly the influence of BTS-a South Korean boy band with over 90 million international fans known as ARMY-has shaped youth culture and online communities. Music fandoms are increasingly engaging digital platforms like YouTube not only for entertainment but also as spaces for emotional expression and mutual support. Despite growing interest in the mental health potential of music-based coping strategies, limited research has examined how fandom cultures differentially express emotional needs and supportive interactions online. OBJECTIVE: This study investigates specific mental health language patterns and coping mechanisms expressed by BTS fans in online spaces, examining how different linguistic features (including self-referential language and emotional expression patterns) may reflect psychological states and mental health needs. We utilize YouTube comments of fan-curated "sad" playlists of BTS. We further included YouTube comments from a Taylor Swift "sad" playlist as a reference group. The analysis aims to identify linguistic and emotional expression patterns in BTS fan comments and examine the potential mental health implications of music engagement in digital communities. METHODS: Using Natural Language Processing (NLP) and Linguistic Inquiry and Word Count (LIWC), we analyzed a total of 13,224 YouTube comments-11,772 comments on a BTS "sad playlist" video and 1,452 comments on a Taylor Swift equivalent. Statistical comparisons were conducted to evaluate differences in comment length, word count, pronoun use, and emotional valence. Representative comments were examined to contextualize the emotion classification results. RESULTS: BTS comments were significantly longer (M = 253.38 words) and had higher word counts (M = 38.93) compared to Taylor Swift comments (M = 89.84 words, M = 16.08), p < .001. BTS fans used more first-person singular pronouns (10.24% vs. 7.43%) and expressed greater sadness (19.8% vs. 7.0%). In contrast, Taylor Swift fans exhibited higher admiration (8.0% vs. 5.0%). Among reply comments, BTS fans demonstrated more caring (7.5% vs. 2.0%), gratitude (9.1% vs. 4.2%), and optimism (5.0% vs. 1.7%). Linguistic analysis also revealed a broader international user base for BTS, including higher proportions of Spanish (6.11%) and Portuguese (1.89%) comments. Examination of comment content showed that fans used these spaces to disclose personal struggles, express gratitude for the community, and offer peer support, with many describing the fandom as a safe space for emotional expression they could not access elsewhere. CONCLUSIONS: The findings underscore the significant role that music and fan communities-particularly BTS fandom-play in fostering emotional expression, mutual care, and informal mental health support online. These results suggest implications for culturally responsive, community-based, and digitally mediated mental health interventions among youth and global populations.
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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 | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| 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.
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