  Many platforms collect crowdsourced information primarily from volunteers. As
this type of knowledge curation has become widespread, contribution formats
vary substantially and are driven by diverse processes across differing
platforms. Thus, models for one platform are not necessarily applicable to
others. Here, we study the temporal dynamics of Genius, a platform primarily
designed for user-contributed annotations of song lyrics. A unique aspect of
Genius is that the annotations are extremely local -- an annotated lyric may
just be a few lines of a song -- but also highly related, e.g., by song, album,
artist, or genre. We analyze several dynamical processes associated with lyric
annotations and their edits, which differ substantially from models for other
platforms. For example, expertise on song annotations follows a "U shape" where
experts are both early and late contributors with non-experts contributing
intermediately; we develop a user utility model that captures such behavior. We
also find several contribution traits appearing early in a user's lifespan of
contributions that distinguish (eventual) experts from non-experts. Combining
our findings, we develop a model for early prediction of user expertise.
