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

Authors

  • Derek Lim Cornell University
  • Austin R. Benson Cornell University

Keywords:

Social network analysis; communities identification; expertise and authority discovery, Qualitative and quantitative studies of social media, Engagement, motivations, incentives, and gamification.

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 mainly 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 offers one possible explanation for such behavior. We also find several 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.

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Published

2021-05-22

How to Cite

Lim, D., & Benson, A. R. (2021). Expertise and Dynamics within Crowdsourced Musical Knowledge Curation: A Case Study of the Genius Platform. Proceedings of the International AAAI Conference on Web and Social Media, 15(1), 373-384. Retrieved from https://ojs.aaai.org/index.php/ICWSM/article/view/18068