| dc.description.abstract | This thesis situates itself within broader discussions within media studies. Drawing on insights from scholars such as Prey, Seaver, Beedie and Eriksson et al., it explores Spotify’s Mood-playlists by constructing a framework to analyse their ‘vibes’, shedding light on the socio-technical processes and cultural dynamics and dabbling in debates surrounding cultural production, the influence of algorithms on user autonomy, and the commercialization of affective experiences in the digital marketplace. Through an in-depth analysis of playlist titles, song selection criteria, and personalised variations, leaning on the platform’s API and a K-means analysis, the research uncovers how Spotify evokes specific emotional states, contexts and atmospheres and capitalises on certain vibes by intertwining cultural references, algorithmic processes, and user preferences.
Key findings highlight the dynamic nature of playlist construction, influenced by shifting cultural landscapes and emerging trends. However, despite variations in song selection, consistent characteristics shape playlist vibes, raising questions about the algorithmic understanding of user emotions. Overall, the research underscores the significance of playlists as cultural artefacts and their role in shaping user experiences and acting as cultural tastemakers. | |
