Universality of preference behaviors in online music-listener bipartite networks: A Big Data analysis (2212.13139v1)

Abstract: We investigate the formation of musical preferences of millions of users of the NetEase Cloud Music (NCM), one of the largest online music platforms in China. We combine the methods from complex networks theory and information sciences within the context of Big Data analysis to unveil statistical patterns and community structures underlying the formation and evolution of musical preference behaviors. Our analyses address the decay patterns of music influence, users' sensitivity to music, age and gender differences, and their relationship to regional economic indicators. Employing community detection in user-music bipartite networks, we identified eight major cultural communities in the population of NCM users. Female users exhibited higher within-group variability in preference behavior than males, with a major transition occurring around the age of 25. Moreveor, the musical tastes and the preference diversity measures of women were also more strongly associated with economic factors. However, in spite of the highly variable popularity of music tracks and the identified cultural and demographic differences, we observed that the evolution of musical preferences over time followed a power-law-like decaying function, and that NCM listeners showed the highest sensitivity to music released in their adolescence, peaking at the age of 13. Our findings suggest the existence of universal properties in the formation of musical tastes but also their culture-specific relationship to demographic factors, with wide-ranging implications for community detection and recommendation system design in online music platforms.