Expertise and Dynamics within Crowdsourced Musical Knowledge Curation: A Case Study of the Genius Platform

TL;DR

This study analyzes the temporal dynamics and expertise development in Genius, a crowdsourced platform for song lyric annotations, revealing unique contribution patterns and proposing a model for early expertise prediction.

Contribution

It introduces a novel analysis of user contribution dynamics and develops a model for early prediction of expertise in crowdsourced musical knowledge curation.

Findings

Expertise follows a 'U shape' with early and late contributors being experts.

Early contribution traits can predict user expertise.

Contribution patterns differ significantly from other crowdsourcing platforms.

Abstract

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.