Analyzing the Spotify Top 200 Through a Point Process Lens

TL;DR

This paper analyzes the Spotify Top 200 chart over 20 months using stochastic point process models to understand song popularity, rarity, and longevity, and clusters songs based on their streaming patterns.

Contribution

It introduces a stochastic process model for streaming counts and applies clustering to identify similar songs, offering new insights into chart dynamics.

Findings

Identified patterns in song popularity and longevity.

Developed a stochastic intensity point process model.

Clustered songs based on streaming behavior.

Abstract

Every generation throws a hero up the pop charts. For the current generation, one of the most relevant pop charts is the Spotify Top 200. Spotify is the world's largest music streaming service and the Top 200 is a daily list of the platform's 200 most streamed songs. In this paper, we analyze a data set collected from over 20 months of these rankings. Via exploratory data analysis, we investigate the popularity, rarity, and longevity of songs on the Top 200 and we construct a stochastic process model for the daily streaming counts that draws upon ideas from stochastic intensity point processes and marked point processes. Using the parameters of this model as estimated from the Top 200 data, we apply a clustering algorithm to identify songs with similar features and performance.