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Exploring Geospatial Music Listening Patterns in Microblog Data

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Adaptive Multimedia Retrieval: Semantics, Context, and Adaptation (AMR 2012)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8382))

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Abstract

Microblogs are a steadily growing, valuable, albeit noisy, source of information on interests, preferences, and activities. As music plays an important role in many human lives we aim to leverage microblogs for music listening-related information. Based on this information we present approaches to estimate artist similarity, popularity, and local trends, as well as approaches to cluster artists with respect to additional tag information. Furthermore, we elaborate a novel geo-aware interaction approach that integrates these diverse pieces of information mined from music-related tweets. Including geospatial information at the level of tweets, we also present a web-based user interface to browse the “world of music” as seen by the “Twittersphere”.

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Acknowledgments

This research is supported by the Austrian Science Funds (FWF): P22856-N23 and Z159.

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Authors and Affiliations

  1. Department of Computational Perception, Johannes Kepler University, Linz, Austria

    David Hauger & Markus Schedl

Authors
  1. David Hauger
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  2. Markus Schedl
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Corresponding author

Correspondence to Markus Schedl .

Editor information

Editors and Affiliations

  1. Otto-von-Guericke-Universität Magdeburg, Magdeburg, Germany

    Andreas Nürnberger

  2. Otto-von-Guericke-Universität Magdeburg, Magdeburg, Germany

    Sebastian Stober

  3. Royal School of Library and Information Science, Copenhagen, Denmark

    Birger Larsen

  4. Université Pierre et Marie Curie, Paris, France

    Marcin Detyniecki

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Hauger, D., Schedl, M. (2014). Exploring Geospatial Music Listening Patterns in Microblog Data. In: Nürnberger, A., Stober, S., Larsen, B., Detyniecki, M. (eds) Adaptive Multimedia Retrieval: Semantics, Context, and Adaptation. AMR 2012. Lecture Notes in Computer Science(), vol 8382. Springer, Cham. https://doi.org/10.1007/978-3-319-12093-5_7

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  • DOI: https://doi.org/10.1007/978-3-319-12093-5_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-12092-8

  • Online ISBN: 978-3-319-12093-5

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