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Online music streaming services are one of the important actors in music consumption for today’s consumers. In addition to widespread use of mobile devices, many changes in the patterns of music consumption are witnessed such as the purchase of single tracks instead of albums, listening to music on different platforms, and personalized music consumption options. This study aims to examine the concept of music consumption in Turkey through audio characteristics of popular songs. Top 200 popular song-lists for 6 months period are chosen as sample and audio characteristics provided by Spotify API service regarding 676 unique songs are analyzed. Following descriptive statistics of Turkey Music Market, clustering methodology is employed and three different clusters for songs are concluded. Finally, decision tree methodology is employed to classify the dataset with popularity scores and audio characteristics together, while loudness and energy characteristics are found as significant classifiers.
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