Evolution of global music trends: An exploratory and predictive approach based on Spotify data

Music listening choices are considered to be a factor capable of measuring people's emotions. Thanks to the explosion of streaming music applications in recent years, it is possible to describe listening trends of the global population based on emotional features. In this paper we have analysed the most popular songs from 52 countries on Spotify through their features of danceability, positivity and intensity. This analysis allows exploring how these song features reflect mood trends along with other contextual factors that may affect the population's listening behaviour, such as the weather or the influence of the COVID-19 pandemic. Finally, we have proposed a multivariate time series model to predict the preferred type of music in those countries based on their previous music listening patterns and the contextual factors. The results show some relevant behavioural changes in these patterns due to the effect of the pandemic. Furthermore, the resulting prediction model enables forecasting the type of music listened to in three different groups of countries in the next 4 months with an error around 1%. These results may help to better understand streaming music consumption in businesses related to the music and marketing industry.

Automated summary

Music listening choices can reflect people's emotions, and this study analyzes the most popular songs from 52 countries on Spotify to explore mood trends and contextual factors. A multivariate time series model is proposed to predict preferred music types based on previous listening patterns and contextual factors, showing changes due to the pandemic. The resulting prediction model can forecast music listening preferences in different countries with a low error rate, providing insights for the music and marketing industry.