研究非洲流行音乐歌曲在排行榜停留时长，发现合作曲目反而更短。
Bayesian Negative Binomial Regression of Afrobeats Chart Persistence
- 用贝叶斯负二项回归分析排行榜天数与合作状态的关系。
- 控制播放量后，合作歌曲平均少停留约1.3天，具有统计显著性。
- 适合对音乐产业、数据驱动文化研究感兴趣的读者。
非洲流行音乐（Afrobeats）歌曲在流媒体平台竞争关注，排行榜可见性会影响收入与文化影响力。本文利用2024年每日尼日利亚Spotify Top 200数据，研究合作是否有助于歌曲更长时间留在榜单。每首歌以年度在榜单中出现的天数和尼日利亚总播放量为特征。采用贝叶斯负二项回归模型，以榜单天数为因变量，合作状态（独唱 vs. 多人合作）和对数总播放量为预测变量。该方法适用于过度分散的计数数据，可控制整体热度影响。通过马尔可夫链蒙特卡洛进行后验推断，结果使用率比、后验概率和预测检验评估。结果显示，在控制总播放量后，合作歌曲相比同热度独唱歌曲平均少停留约1.3天，且具有统计显著性。
原文摘要 · Abstract (English)
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.
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