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International Symposium on Formal Methods

FM 2019: Formal Methods. FM 2019 International Workshops pp 146–161Cite as

“Know Thyself” How Personal Music Tastes Shape the Last.Fm Online Social Network

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Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 12232))

Abstract

As Nietzsche once wrote “Without music, life would be a mistake” (Twilight of the Idols, 1889.). The music we listen to reflects our personality, our way to approach life. In order to enforce self-awareness, we devised a Personal Listening Data Model that allows for capturing individual music preferences and patterns of music consumption. We applied our model to 30k users of Last.Fm for which we collected both friendship ties and multiple listening. Starting from such rich data we performed an analysis whose final aim was twofold: (i) capture, and characterize, the individual dimension of music consumption in order to identify clusters of like-minded Last.Fm users; (ii) analyze if, and how, such clusters relate to the social structure expressed by the users in the service. Do there exist individuals having similar Personal Listening Data Models? If so, are they directly connected in the social graph or belong to the same community?.

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Notes

  1. 1.

    The choice of describing a listening with these attributes is related to the case study. Additional attributes can be used when available from the data. We highlight that listening means that the song was played and not necessarily entirely listened.

  2. 2.

    http://www.last.fm/api/, retrieval date 2016-04-04.

  3. 3.

    The code, along with the ids of seed users used in this study, is available at https://github.com/GiulioRossetti/LastfmProfiler. The complete dataset is not released to comply with Last.fm TOS.

  4. 4.

    The p-value is zero (or smaller than 0.000001) for all the correlations.

  5. 5.

    The analysis of \(b_u\) have similar results (not reported due to lack of space).

  6. 6.

    The Pearson correlations ranges in [0.96, 0.99], \(\text {p-value} \ll 1.0e^{-60}\).

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Acknowledgment

This work is partially supported by the European Community H2020 programme under the funding schemes: INFRAIA-1-2014-2015: Research Infrastructures G.A. 654024 SoBigData (http://www.sobigdata.eu), G.A. 78835 Pro-Res (http://prores-project.eu/), and G.A. 825619 AI4EU (https://www.ai4eu.eu/), and G.A. 780754 Track & Know (https://trackandknowproject.eu/).

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Authors and Affiliations

  1. ISTI-CNR, Via G. Moruzzi, Pisa, Italy

    Riccardo Guidotti & Giulio Rossetti

  2. University of Pisa, Largo B. Pontecorvo, Pisa, Italy

    Riccardo Guidotti

Authors
  1. Riccardo Guidotti
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  2. Giulio Rossetti
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Correspondence to Riccardo Guidotti .

Editor information

Editors and Affiliations

  1. McMaster University, Hamilton, ON, Canada

    Emil Sekerinski

  2. University of Porto, Porto, Portugal

    Nelma Moreira

  3. University of Minho, Braga, Portugal

    José N. Oliveira

  4. Argo Ai, Munich, Germany

    Daniel Ratiu

  5. University of Pisa, Pisa, Italy

    Riccardo Guidotti

  6. University of Liverpool, Liverpool, UK

    Marie Farrell

  7. University of Liverpool, Liverpool, UK

    Matt Luckcuck

  8. University of Exeter, Exeter, UK

    Diego Marmsoler

  9. University of Minho, Braga, Portugal

    José Campos

  10. University of Newcastle, Newcastle upon Tyne, UK

    Troy Astarte

  11. Claude Bernard University, Lyon, France

    Laure Gonnord

  12. Nazarbayev University, Nur-Sultan, Kazakhstan

    Antonio Cerone

  13. University of Surrey, Guildford, UK

    Luis Couto

  14. University of Surrey, Guildford, UK

    Brijesh Dongol

  15. University of Giessen, Giessen, Germany

    Martin Kutrib

  16. University of Lisbon, Lisbon, Portugal

    Pedro Monteiro

  17. Airbus Operations S.A.S., Toulouse, France

    David Delmas

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Guidotti, R., Rossetti, G. (2020). “Know Thyself” How Personal Music Tastes Shape the Last.Fm Online Social Network. In: Sekerinski, E., et al. Formal Methods. FM 2019 International Workshops. FM 2019. Lecture Notes in Computer Science(), vol 12232. Springer, Cham. https://doi.org/10.1007/978-3-030-54994-7_11

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  • DOI: https://doi.org/10.1007/978-3-030-54994-7_11

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