# Affective Idiosyncratic Responses to Music

arXiv: https://arxiv.org/abs/2210.09396 · HTML: https://mlsanity.com/paper/2210.09396/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 124 references that are in the map.
- First posted on arXiv 17 Oct 2022.

## Builds on (most cited first, 6 of 6)

- [Causal Inference in Natural Language Processing: Estimation, Prediction, Interpretation and Beyond](https://mlsanity.com/paper/2109.00725/) (2021)
- [SMHD: A Large-Scale Resource for Exploring Online Language Usage for Multiple Mental Health Conditions](https://mlsanity.com/paper/1806.05258/) (2018)
- [On the State of Social Media Data for Mental Health Research](https://mlsanity.com/paper/2011.05233/) (2020)
- [Practical and Ethical Considerations in the Effective use of Emotion and Sentiment Lexicons](https://mlsanity.com/paper/2011.03492/) (2020)
- [Social Media Reveals Urban-Rural Differences in Stress across China](https://mlsanity.com/paper/2110.15726/) (2021)
- [Homophily of Music Listening in Online Social Networks](https://mlsanity.com/paper/1710.10642/) (2017)

## Abstract (arXiv)

Affective 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 wvang-y`i-y'un, we identify influencing factors of platform user self-disclosures, the social support they receive, and notable differences in discloser user activity.