  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 w\v{a}ng-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.
