From Beats to Being
Using adolescents’ listening data to identify developmental trajectories
M. Lauf (TU Delft - Electrical Engineering, Mathematics and Computer Science)
R. Ungruh – Mentor (TU Delft - Web Information Systems)
Maria Soledad Pera – Graduation committee member (Boise State University)
Masoud Mansoury – Graduation committee member (Eindhoven University of Technology)
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Abstract
Adolescence is a pivotal period of identity formation, significantly influenced by media consumption, particularly music. While recommender sytems (RS) play a central role in shaping adolescents' music exposure, they are typically designed without considering developmental needs. We do not yet understand how identity development manifests in behavioral data over time. And without this understanding, we cannot design systems that meaningfully support it. This study addresses this critical gap by applying James Marcia's established Identity Status Theory to real-world listening behavior to get insight into how adolescents traverse and transition betweeen distinct developmental phases.
Utilizing the LFM2-b dataset, we operationalize James Marcia's dimensions of exploration and commitment through Shannon Entropy and Jensen-Shannon Divergence of genre distributions, respectively, to identify and characterize distinct behavioral phases (Diffusion, Foreclosure, Moratorium, Achievement).
Our results show that while most users briefly enter all four phases, sustained engagement is much rarer, particularly for Foreclosure and Moratorium, which appear less stable. In contrast, Diffusion and Achievement are both more frequently sustained, with Achievement emerging as the most common final phase (40.78%). A strong directional transition was observed from Moratorium to Achievement (0.67 probability), while reciprocal transitions between Diffusion and Foreclosure highlight re-evaluation among users. These patterns support the dynamic and recursive nature of adolescent identity development. By linking digital behavior to psychological theory, this work offers both developmental insights and practical implications. It lays the foundation for designing recommender systems that are not only personalized but also support healthy identity formation.