Exploring Geospatial Music Listening Patterns in Microblog Data

  • David Hauger
  • Markus SchedlEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8382)


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”.


Microblogs Geospatial music taste Music listening patterns 



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


  1. 1.
  2. 2. Accessed August 2012
  3. 3. Accessed August 2012
  4. 4.
  5. 5. Accessed August 2012
  6. 6. Accessed August 2012
  7. 7. Accessed August 2012
  8. 8. Accessed August 2012
  9. 9.
    Armentano, M., Godoy, D., Amandi, A.: Recommending information sources to information seekers in twitter. In: International Workshop on Social Web Mining, Co-located with IJCAI 2011 (2011)Google Scholar
  10. 10.
    Baeza-Yates, R., Ribeiro-Neto, B.: Modern Information Retrieval. Addison Wesley, New York (1999)Google Scholar
  11. 11.
    Bollen, J., Mao, H., Zeng, X.: Twitter mood predicts the stock market. J. Comput. Sci. 2(1), 1–8 (2011)CrossRefGoogle Scholar
  12. 12.
    Byklum, D.: Geography and music: making the connection. J. Geogr. 93(6), 274–278 (1994)CrossRefGoogle Scholar
  13. 13.
    De Longueville, B., Smith, R.S., Luraschi, G.: “OMG, from here, I can see the flames!”: a use case of mining location based social networks to acquire spatio-temporal data on forest fires. In: Proceedings of the 2009 International Workshop on Location Based Social Networks, LBSN ’09, pp. 73–80. ACM, New York (2009)Google Scholar
  14. 14.
    Duan, Y., Jiang, L., Qin, T., Zhou, M., Shum, H.-Y.: An empirical study on learning to rank of tweets. In: Huang, C.-R., Jurafsky, D. (eds.) Proceedings of the 23rd International Conference on Computational Linguistics (COLING 2010), pp. 295–303. Tsinghua University Press, August 2010Google Scholar
  15. 15.
    Govaerts, S., Duval, E.: A web-based approach to determine the origin of an artist. In: Hirata, K., Tzanetakis, G., Yoshii, K. (eds.) Proceedings of the 10th International Society for Music Information Retrieval Conference (ISMIR 2010), pp. 261–266. International Society for Music Information Retrieval (2009)Google Scholar
  16. 16.
    Java, A., Song, X., Finin, T., Tseng, B.: Why we twitter: understanding microblogging usage and communities. In: Proceedings of WebKDD and SNA-KDD, San Jose, CA, USA, August 2007Google Scholar
  17. 17.
    Kwak, H., Lee, C., Park, H., Moon, S.: What is Twitter, a social network or a news media? In: Proceedings of the 19th International Conference on World Wide Web, WWW ’10, pp. 591–600. ACM, New York (2010)Google Scholar
  18. 18.
    Lee, C.-H., Yang, H.-C., Chien, T.-F., Wen, W.-S.: A novel approach for event detection by mining spatio-temporal information on microblogs. In: International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2011), pp. 254–259, July 2011Google Scholar
  19. 19.
    Lee, D.D., Seung, H.S.: Learning the parts of objects by non-negative matrix factorization. Nature 401(6755), 788–791 (1999)CrossRefGoogle Scholar
  20. 20.
    Oulasvirta, A., Lehtonen, E., Kurvinen, E., Raento, M.: Making the ordinary visible in microblogs. Pers. Ubiquit. Comput. 14(3), 237–249 (2010)CrossRefGoogle Scholar
  21. 21.
    Park, S., Kim, S., Lee, S., Yeo, W.S.: Online map interface for creative and interactive MusicMaking. In: Proceedings of the 2010 Conference on New Interfaces for Musical Expression (NIME 2010), Sydney, Australia, pp. 331–334 (2010)Google Scholar
  22. 22.
    Paul, M.J., Dredze, M.: You are what you tweet: analyzing twitter for public health. Artif. Intell. 38, 265–272 (2011)Google Scholar
  23. 23.
    Raimond, Y., Sutton, C., Sandler, M.: Automatic interlinking of music datasets on the semantic web. In: Linked Data on the Web (LDOW2008) (2008)Google Scholar
  24. 24.
    Romero, D.M., Meeder, B., Kleinberg, J.: Differences in the mechanics of information diffusion across topics: idioms, political hashtags, and complex contagion on twitter. In: Proceedings of the 20th International Conference on World Wide Web (WWW 2011), pp. 695–704. ACM, New York (2011)Google Scholar
  25. 25.
    Sakaki, T., Okazaki, M., Matsuo, Y.: Earthquake shakes twitter users: real-time event detection by social sensors. In: Proceedings of the 19th International Conference on World Wide Web (WWW 2010), May 2010Google Scholar
  26. 26.
    Schedl, M.: Analyzing the potential of microblogs for spatio-temporal popularity estimation of music artists. In: Proceedings of IJCAI: International Workshop on Social Web Mining, Barcelona, Spain, July 2011Google Scholar
  27. 27.
    Schedl, M., Hauger, D.: Mining microblogs to infer music artist similarity and cultural listening patterns. In: Proceedings of the 21st International World Wide Web Conference (WWW 2012): 4th International Workshop on Advances in Music Information Research: “The Web of Music” (AdMIRe 2012), Lyon, France (2012)Google Scholar
  28. 28.
    Schedl, M., Pohle, T., Koenigstein, N., Knees, P.: What’s Hot? estimating country-specific artist popularity. In: Proceedings of the 11th Internat, Society for Music Information Retrieval Conference (ISMIR 2010), Utrecht, Netherlands, August 2010Google Scholar
  29. 29.
    Sharifi, B., Hutton, M.-A., Kalita, J.: Summarizing microblogs automatically. In: Proceedings of NAACL HLT, June 2010Google Scholar
  30. 30.
    Steeg, G.V.: Information theoretic tools for social media. In: Making Sense of Microposts (#MSM2012), p. 1 (2012)Google Scholar
  31. 31.
    Teevan, J., Ramage, D., Morris, M.R.: #TwitterSearch: a comparison of microblog search and web search. In: Proceedings of the 4th ACM International Conference on Web Search and Data Mining (WSDM’11), Hong Kong, China, February 2011Google Scholar
  32. 32.
    Weng, J., Lim, E.-P., Jiang, J., He, Q.: TwitterRank: finding topic-sensitive influential twitterers. In: Proceedings of the Third ACM International Conference on Web Search and Data Mining, WSDM ’10, pp. 261–270. ACM, New York (2010)Google Scholar
  33. 33.
    Wu, S., Hofman, J.M., Mason, W.A., Watts, D.J.: Who says what to whom on twitter. In: Proceedings of the 20th International Conference on World Wide Web (WWW 2011), pp. 705–714. ACM, New York (2011)Google Scholar
  34. 34.
    Zangerle, E., Gassler, W., Specht, G.: Exploiting twitter’s collective knowledge for music recommendations. In: Making Sense of Microposts (#MSM2012), pp. 14–17 (2012)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Department of Computational PerceptionJohannes Kepler UniversityLinzAustria

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