Fans of Violent Music: The Role of Passion in Positive and Negative Emotional Experience
Bibliographic record
Abstract
Extreme metal and rap music with violent themes are sometimes blamed for eliciting antisocial behaviours, but growing evidence suggests that music with violent themes can have positive emotional, cognitive, and social consequences for fans. We addressed this apparent paradox by comparing how fans of violent and non-violent music respond emotionally to music. We also characterised the psychosocial functions of music for fans of violent and non-violent music, and their passion for music. Fans of violent extreme metal ( n=46), violent rap ( n=49), and non-violent classical music ( n=50) responded to questionnaires evaluating the cognitive (self-reflection, self-regulation) and social (social bonding) functions of their preferred music and the nature of their passion for it. They then listened to four one-minute excerpts of music and rated ten emotional descriptors for each excerpt. The top five emotions reported by the three groups of fans were positive, with empowerment and joy the emotions rated highest. However, compared with classical music fans, fans of violent music assigned significantly lower ratings to positive emotions and higher ratings to negative emotions. Fans of violent music also utilised their preferred music for positive psychosocial functions to a similar or sometimes greater extent than classical fans. Harmonious passion for music predicted positive emotional outcomes for all three groups of fans, whereas obsessive passion predicted negative emotional outcomes. Those high in harmonious passion also tended to use music for cognitive and social functions. We propose that fans of violent music use their preferred music to induce an equal balance of positive and negative emotions.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".