Follow the guides: disentangling human and algorithmic curation in online music consumption

TL;DR

This study analyzes how different user attitudes towards guidance influence diversity in music streaming, revealing that recommendation effects vary by user type and content, challenging the idea of uniform filter bubbles.

Contribution

It introduces a user categorization based on access modes and examines how algorithmic and editorial recommendations differently impact content diversity across user types.

Findings

User attitudes significantly influence consumption diversity.

Recommendation effects on diversity depend on user categories.

Radio programming often favors less popular artists despite higher repetition.

Abstract

The role of recommendation systems in the diversity of content consumption on platforms is a much-debated issue. The quantitative state of the art often overlooks the existence of individual attitudes toward guidance, and eventually of different categories of users in this regard. Focusing on the case of music streaming, we analyze the complete listening history of about 9k users over one year and demonstrate that there is no blanket answer to the intertwinement of recommendation use and consumption diversity: it depends on users. First we compute for each user the relative importance of different access modes within their listening history, introducing a trichotomy distinguishing so-called `organic' use from algorithmic and editorial guidance. We thereby identify four categories of users. We then focus on two scales related to content diversity, both in terms of dispersion -- how much users consume the same content repeatedly -- and popularity -- how popular is the content they consume. We show that the two types of recommendation offered by music platforms -- algorithmic and editorial -- may drive the consumption of more or less diverse content in opposite directions, depending also strongly on the type of users. Finally, we compare users' streaming histories with the music programming of a selection of popular French radio stations during the same period. While radio programs are usually more tilted toward repetition than users' listening histories, they often program more songs from less popular artists. On the whole, our results highlight the nontrivial effects of platform-mediated recommendation on consumption, and lead us to speak of `filter niches' rather than `filter bubbles'. They hint at further ramifications for the study and design of recommendation systems.