Emotional Expression and Mental Health Support in BTS Fandom Communities: A Natural Language Processing Study on YouTube Comments (Preprint)
Bibliographic record
Abstract
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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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".