Genre-based Analysis of Social Media Data on Music Listening Behavior

Schedl, Markus; Tkalčič, Marko

doi:10.1145/2661714.2661717

Cited by 15 publications

(11 citation statements)

References 7 publications

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“…Other analyses could model music discovery or preference by considering specific geographies, musical genres, or even individual users. Large-scale data have been used to address specific musical questions including the long tail in music-related microblogs (Schedl et al, 2014), social media behavior of Classical music fans (Schedl and Tkalčič, 2014), the relationship between musical taste and personality factors (Bansal and Woolhouse, 2015), and Twitter activity around a specific musical event (Iren et al, 2016). Using Shazam data in this way—to address specific musical questions—promises interesting approaches for future research endeavors.…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2017

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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.

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Section: Discussionmentioning

confidence: 99%

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

et al. 2017

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“…First of all, the usage of Last.fm data obviously introduces a community bias. For instance, studies showed that listeners of classical music are underrepresented [7], whereas fans of metal and alternative music are overrepresented on the platform [8]. The sample of people present in the dataset at hand (and in general in the LFM-1b set) does therefore not generalize to the population at large.…”

Section: Limitations and Future Workmentioning

confidence: 99%

Large-Scale Analysis of Group-Specific Music Genre Taste from Collaborative Tags

Schedl

Ferwerda

2017

2017 IEEE International Symposium on Multimedia (ISM)

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Abstract-In this paper, we describe the LFM-1b User Genre Profile dataset. It provides detailed information on musical genre preferences for more than 120,000 listeners and links to the LFM-1b dataset. We created the dataset by exploiting social tags, indexing them using two genre term sets, and aggregating the resulting annotated listening events on the user level. We foresee several applications of the dataset in music retrieval and recommendation tasks, among others to build and evaluate decent user models, to alleviate cold-start situations in music recommender systems, and to increase their performance using the additional abstraction layer of genre. We further present results of statistical analyses of the dataset, regarding genre preferences and their consistencies. We do so for the entire user population and for user groups defined by demographic similarities. Moreover, we report interesting insights about correlations between musical preferences on the genre level.

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“…In this work, we chose the music platform Last.fm to gather a real-world dataset, since it has been shown to attract users of a wide variety of music tastes [9]. In contrast, existing work commonly makes use of rather small and noisy datasets, typically gathered from Twitter and including a maximum of a few million listening events [6].…”

Section: Related Workmentioning

confidence: 99%

On the Influence of User Characteristics on Music Recommendation Algorithms

Schedl

Hauger

Farrahi

et al. 2015

Lecture Notes in Computer Science

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Abstract. We investigate a range of music recommendation algorithm combinations, score aggregation functions, normalization techniques, and late fusion techniques on approximately 200 million listening events collected through Last.fm. The overall goal is to identify superior combinations for the task of artist recommendation. Hypothesizing that user characteristics influence performance on these algorithmic combinations, we consider specific user groups determined by age, gender, country, and preferred genre. Overall, we find that the performance of music recommendation algorithms highly depends on user characteristics.

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Genre-based Analysis of Social Media Data on Music Listening Behavior

Cited by 15 publications

References 7 publications

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

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

Large-Scale Analysis of Group-Specific Music Genre Taste from Collaborative Tags

On the Influence of User Characteristics on Music Recommendation Algorithms

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