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.

翻译：阿芙罗节拍音乐在流媒体平台上竞争关注度，榜单可见性同时影响其收入和文化影响力。本文利用2024年尼日利亚Spotify每日Top 200数据，研究合作是否有助于歌曲在榜单上停留更长时间。每条音轨通过其在年度内进入Top 200的天数及其在尼日利亚的年播放总量进行汇总。研究采用贝叶斯负二项回归模型，以榜单停留天数为结果变量，以合作状态（单人作品与多艺术家合作）及对数化总播放量为预测变量。该方法适用于过度离散的计数数据，并能在控制整体流行度的前提下解析合作效应的影响。后验推断通过马尔可夫链蒙特卡罗方法实现，结果采用率比、后验概率及预测检验进行评估。研究结果表明，在控制总播放量后，合作曲目在榜单上的停留天数往往略少于同等级别的单人作品。