In this paper we leverage recent developments in the way scholars access, collect, and analyze data to reexamine consumption dynamics in popular music. Using web-based tools to construct a dataset that distills songs’ musical content into a handful of discrete attributes, we test whether and how these attributes affect a song’s position on the <i>Billboard Hot 100</i> charts. Our analysis suggests that attributes matter, beyond the effect of artist, label, and genre affiliation. We also find evidence that the relational patterns formed between attributes — what we call cultural networks — crowds songs that are too similar to their neighbors, adversely affecting their movement up the charts. These results suggest that culture possesses its own sphere of influence that is partially independent of the actors who produce and consume it.
Social Informatics, Lecture Notes in Computer Science, volume 8851, edited by