2022 EMNLP EMNLP 2022

Affective Idiosyncratic Responses to Music

Abstract

AbstractAffective responses to music are highly personal. Despite consensus that idiosyncratic factors play a key role in regulating how listeners emotionally respond to music, precisely measuring the marginal effects of these variables has proved challenging. To address this gap, we develop computational methods to measure affective responses to music from over 403M listener comments on a Chinese social music platform. Building on studies from music psychology in systematic and quasi-causal analyses, we test for musical, lyrical, contextual, demographic, and mental health effects that drive listener affective responses. Finally, motivated by the social phenomenon known as 网抑云 (wǎng-yì-yún), we identify influencing factors of platform user self-disclosures, the social support they receive, and notable differences in discloser user activity.

🌉 Interdisciplinary Bridge — Data Science & Analytics and Healthcare & Medicine and Interdisciplinary and Machine Learning
🧭 Keyword Pioneer — music psychology
🐣 Hot Topic Early Bird — affective computing
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio