Music listening and mood are often investigated using methodologies limited by artificial settings, response and recall biases, and a lack of consideration for individual differences. These data lack the validity, reliability, and time-sensitivity required to undertake complex analyses into any prospective relationship between music and mood. Music streaming data from platforms such as Spotify provides a naturalistic, time-stamped and almost continuous measure of real-time music listening which can be supplemented with information about musical characteristics. When combined with time-sensitive mood records, which circumvent potential recall biases to capture a real-time picture of mood, these data provide a novel method of investigating the music-mood relationship. Determining the nature of this relationship could form the foundations for music listening to be used as a proxy for wellbeing intervention and prevention strategies. This thesis will investigate the viability of the ‘Mood Music’ dataset (n=171), comprising of Spotify streaming history, Ecological Momentary Assessment, and mental health survey measures, for such an investigation. After providing an overview and evaluation of the different methods used to assess mood and music listening, it will consider the validity and reliability, dimensionality, missingness, and variability over time of these data. I will conclude, due to low missingness and high validity and reliability in the Ecological Momentary Assessment and Spotify datasets, that this dataset is viable for future causal analyses as a parallel time series dataset. This work will inform my future PhD research testing the causal relationships between music and mood, with the potential to inform intervention and prevention strategies.
The Mood Music study : Combining Spotify listening behaviour, Ecological Momentary Assessment, and mental health
Jones, M. (Author). 9 Dec 2025
Student thesis: Master's Thesis › Master of Science by Research (MScR)