Dataset Information

Characterizing Listener Engagement with Popular Songs Using Large-Scale Music Discovery Data.

ABSTRACT: Music discovery in everyday situations has been facilitated in recent years by audio content recognition services such as Shazam. The widespread use of such services has produced a wealth of user data, specifying where and when a global audience takes action to learn more about music playing around them. Here, we analyze a large collection of Shazam queries of popular songs to study the relationship between the timing of queries and corresponding musical content. Our results reveal that the distribution of queries varies over the course of a song, and that salient musical events drive an increase in queries during a song. Furthermore, we find that the distribution of queries at the time of a song's release differs from the distribution following a song's peak and subsequent decline in popularity, possibly reflecting an evolution of user intent over the "life cycle" of a song. Finally, we derive insights into the data size needed to achieve consistent query distributions for individual songs. The combined findings of this study suggest that music discovery behavior, and other facets of the human experience of music, can be studied quantitatively using large-scale industrial data.

SUBMITTER: Kaneshiro B

PROVIDER: S-EPMC5362644 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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Publications

Characterizing Listener Engagement with Popular Songs Using Large-Scale Music Discovery Data.

Kaneshiro Blair B Ruan Feng F Baker Casey W CW Berger Jonathan J

Frontiers in psychology 20170323

Music discovery in everyday situations has been facilitated in recent years by audio content recognition services such as Shazam. The widespread use of such services has produced a wealth of user data, specifying where and when a global audience takes action to learn more about music playing around them. Here, we analyze a large collection of Shazam queries of popular songs to study the relationship between the timing of queries and corresponding musical content. Our results reveal that the dist ...[more]

PMID: 28386241

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Project description:BackgroundSAM (Self-help for Anxiety Management) is a mobile phone app that provides self-help for anxiety management. Launched in 2013, the app has achieved over one million downloads on the iOS and Android platform app stores. Key features of the app are anxiety monitoring, self-help techniques, and social support via a mobile forum ("the Social Cloud"). This paper presents unique insights into eMental health app usage patterns and explores user behaviors and usage of self-help techniques.ObjectiveThe objective of our study was to investigate behavioral engagement and to establish discernible usage patterns of the app linked to the features of anxiety monitoring, ratings of self-help techniques, and social participation.MethodsWe use data mining techniques on aggregate data obtained from 105,380 registered users of the app's cloud services.ResultsEngagement generally conformed to common mobile participation patterns with an inverted pyramid or "funnel" of engagement of increasing intensity. We further identified 4 distinct groups of behavioral engagement differentiated by levels of activity in anxiety monitoring and social feature usage. Anxiety levels among all monitoring users were markedly reduced in the first few days of usage with some bounce back effect thereafter. A small group of users demonstrated long-term anxiety reduction (using a robust measure), typically monitored for 12-110 days, with 10-30 discrete updates and showed low levels of social participation.ConclusionsThe data supported our expectation of different usage patterns, given flexible user journeys, and varying commitment in an unstructured mobile phone usage setting. We nevertheless show an aggregate trend of reduction in self-reported anxiety across all minimally-engaged users, while noting that due to the anonymized dataset, we did not have information on users also enrolled in therapy or other intervention while using the app. We find several commonalities between these app-based behavioral patterns and traditional therapy engagement.

Dataset Information

Characterizing Listener Engagement with Popular Songs Using Large-Scale Music Discovery Data.

Publications

Characterizing Listener Engagement with Popular Songs Using Large-Scale Music Discovery Data.

Similar Datasets

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