A Machine Learning Approach to Preferential Attachment and Status Advantage in a Hip-Hop Collaboration Network

Status is central to understanding collaborative behavior, yet it is often difficult to measure in cultural fields where perceived standings are only partially observable. This study develops a scalable supervised machine learning approach to infer directed deference in collaboration networks using a partially observed status hierarchy derived from a ritualized site of status conferral (a televised competition series). Drawing on a longitudinal “featuring” network of more than 3,000 South Korean hip-hop artists, we train a classifier to learn how differences in status-relevant characteristics map onto observed deference patterns and then use it to estimate preferential attachment across all collaboration dyads. The resulting measure aligns closely with external expert assessments of artists’ relative standing. Applying this metric to streaming performance data, we show that collaboration improves listener engagement and that its effect varies nonlinearly with status distance: artists benefit both from partnering with higher-status collaborators and from featuring emerging talents.