Article

Genre-based Analysis of Social Media Data on Music Listening Behavior [ Are Fans of Classical Music Really Averse to Social Media ?]

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Abstract

It is frequently presumed that lovers of Classical music are not present in social media. In this paper, we investigate whether this statement can be empirically verified. To this end, we compare two social media platforms - Last.fm and Twitter - and perform a study on musical preference of their respective users. We investigate two research hypotheses: (i) Classical music fan are more reluctant to use social media to indicate their listing habits than listeners of other genres and (ii) there are correlations between the use of Last.fm and Twitter to indicate music listening behavior. Both hypotheses are verified and substantial differences could be made out for Twitter users. The results of these investigations will help improve music recommendation systems for listeners with non-mainstream music taste.

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... Classical music consumption, especially live performances, did not change much over the recent decades, despite the development of new technologies. The share of consumption on mobile devices and the buzz on social media is very lim- ited [14]. In order to provide the listeners of classical music an with an enhanced experience that new technologies can offer (such as additional information and personalization) and lower the barriers for engagement with classical music [9], we performed a series of studies and implemented a set 1 http://phenicx.upf.edu ...
... In order to implement personalized applications, such as adaptation of user interfaces [10], for end users the system should be able to acquire the user preferences. Implicit acquisition of such data in the domain of classical music has shown to be hard as users hardly leave any traces on their preferences about classical music on social media [14] . However , related work suggests that preferences are related to two proxy constructs, personality and music sophistication. ...
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... Chen and Shen [2] propose a recommendation approach that integrates user location, listening history, music descriptors, and global music popularity trends inferred from microblogs. 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]. ...
... Age: Listeners from 8 possible age groups are considered. These ranges are67891011121314151617,18192021,22232425,2627282930,31323334353637383940,41424344454647484950,51525354555657585960,: US age [Start-End]. Gender: A listener's gender is considered (i.e. ...
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... users in our dataset toward certain genres differ from the genre preferences of the population at large. For instance, we found that rap and R&B as well as classical music is substantially underrepresented in Last.fm listening data (Schedl and Tkalcic, 2014), which we use in the present study. To some extent, these limitations related to the dataset could be alleviated in the future by performing further data cleansing and preprocessing steps, e.g., threshold-based filtering of exorbitant playcounts by a minority of listeners. ...
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... users in our dataset towards certain genres differ from the genre preferences of the population at large. For instance, we found that rap and R&B as well as classical music is substantially underrepresented in Last.fm listening data [68], which we use in the present study. To some extent, these limitations related to the dataset could be alleviated in the future by performing further data cleansing and preprocessing steps, e.g., threshold-based filtering of exorbitant playcounts by a minority of listeners. ...
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... Similarly, the dataset is known to be biased with respect to gender (with a high percentage of male users in the sample). Listeners of classical music tend to be underrepresented [84], whereas listeners of the genres metal and alternative tend to be overrepresented on the Last.fm platform [85]. ...
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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 , 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). ...
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N. Ardet. Teenagers, Internet and Black Metal. In Proceedings of the Conference on Interdisciplinary Musicology (CIM), Graz, Austria, April 2004.
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  • M Larson
  • A Hanjalic
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  • B Ferwerda
  • M Schedl
  • C Liem
  • M Melenhorst
  • A Odić
  • A Kosir
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  • E Zangerle
  • W Gassler
  • G Specht