Bayesian Negative Binomial Regression of Afrobeats Chart Persistence

TL;DR

This study uses Bayesian negative binomial regression to analyze how collaborations affect the duration of Afrobeats songs on Nigeria's Spotify charts, revealing that collaborations tend to stay slightly shorter on the chart.

Contribution

It introduces a Bayesian negative binomial model to assess collaboration effects on chart persistence, controlling for popularity, which is novel in this context.

Findings

Collaborations are associated with slightly fewer days on the chart.

The model accounts for overdispersion in count data.

Posterior analysis supports the negative impact of collaborations on chart duration.

Abstract

Afrobeats songs compete for attention on streaming platforms, where chart visibility can influence both revenue and cultural impact. This paper examines whether collaborations help songs remain on the charts longer, using daily Nigeria Spotify Top 200 data from 2024. Each track is summarized by the number of days it appears in the Top 200 during the year and its total annual streams in Nigeria. A Bayesian negative binomial regression is applied, with days on chart as the outcome and collaboration status (solo versus multi-artist) and log total streams as predictors. This approach is well suited for overdispersed count data and allows the effect of collaboration to be interpreted while controlling for overall popularity. Posterior inference is conducted using Markov chain Monte Carlo, and results are assessed using rate ratios, posterior probabilities, and predictive checks. The findings indicate that, after accounting for total streams, collaboration tracks tend to spend slightly fewer days on the chart than comparable solo tracks.