Reframing the filter bubble through diverse scale effects in online music consumption

The impact of platforms' algorithmic curation devices on users' consumption diversity has long since been the subject of debate in popular culture and academia alike. In recent years, methods to concretely appraise this debate have taken a comparative turn, with emerging research strands seeking to assess the influence of algorithmic recommendations relative to users' organic behaviour (i.e. those with a relative lack of algorithmic influence). Nevertheless, as the contribution of this work clearly shows, contradictory findings that algorithmic devices may both foster "bubbly" confinement dynamics and diverse exploration appear to stem, at least in part, from tensions surrounding information representation and scale. Taking into account various scales, affordances, and diversity measures, the contribution of this work conducts an empirical analysis of approximately 50,000 Deezer users' artist consumption histories across different affordances offering various degrees of algorithmic assistance. Transitioning through three scales of increasing aggregation-intra-sessions, inter-sessions and inter-affordances-our work underscores how shifts in perspective, particularly in information representation, can yield markedly different conclusions concerning the role of algorithmic devices in fostering diversity. However, when considered collectively, these perspectives coalesce to provide a nuanced update to the seminal filter bubble narrative: algorithmic curation devices may introduce more novelty than what users' achieve organically, yet concurrently, this novelty is more semantically confined. Consequently, our research significantly contributes towards an enriched understanding of the intricate dynamics surrounding on-platform algorithmic devices.