# Link Me Baby One More Time: Social Music Discovery on Spotify

arXiv: https://arxiv.org/abs/2401.08818 · HTML: https://mlsanity.com/paper/2401.08818/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 30 references that are in the map.
- First posted on arXiv 16 Jan 2024.

## Builds on (most cited first, 7 of 7)

- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Bayesian Inference of Spreading Processes on Networks](https://mlsanity.com/paper/1709.08862/) (2017)
- [Do Diffusion Protocols Govern Cascade Growth?](https://mlsanity.com/paper/1805.07368/) (2018)
- [Evidence of Complex Contagion of Information in Social Media: An Experiment Using Twitter Bots](https://mlsanity.com/paper/1703.06027/) (2017)
- [Complex Contagion of Campaign Donations](https://mlsanity.com/paper/1601.07679/) (2016)
- [Social Complex Contagion in Music Listenership: A Natural Experiment with 1.3 Million Participants](https://mlsanity.com/paper/1711.05701/) (2017)
- [Exploring Musical, Lyrical, and Network Dimensions of Music Sharing Among Depression Individuals](https://mlsanity.com/paper/2310.11557/) (2023)

## Abstract (arXiv)

We explore the social and contextual factors that influence the outcome of person-to-person music recommendations and discovery. Specifically, we use data from Spotify to investigate how a link sent from one user to another results in the receiver engaging with the music of the shared artist. We consider several factors that may influence this process, such as the strength of the sender-receiver relationship, the user's role in the Spotify social network, their music social cohesion, and how similar the new artist is to the receiver's taste. We find that the receiver of a link is more likely to engage with a new artist when (1) they have similar music taste to the sender and the shared track is a good fit for their taste, (2) they have a stronger and more intimate tie with the sender, and (3) the shared artist is popular amongst the receiver's connections. Finally, we use these findings to build a Random Forest classifier to predict whether a shared music track will result in the receiver's engagement with the shared artist. This model elucidates which type of social and contextual features are most predictive, although peak performance is achieved when a diverse set of features are included. These findings provide new insights into the multifaceted mechanisms underpinning the interplay between music discovery and social processes.