A Search Engine Log Analysis of Music-Related Web Searching

  • Sally Jo Cunningham
  • David Bainbridge
Part of the Studies in Computational Intelligence book series (SCI, volume 283)


We explore music search behavior by identifying music-related queries in a large (over 20 million queries) search engine log, gathered over three months in 2006. Music searching is a significant information behavior: approximately 15% of users conduct at least one music search in the time period studied, and approximately 1.35% of search activities are connected to music. We describe the structural characteristics of music searches—query length and frequency for result selection—and also summarize the most frequently occurring search terms and destinations. The findings are compared to earlier studies of general search engine behavior and to qualitative studies of natural language music information needs statements. The results suggest the need for specialized music search facilities and provide implications for the design of a music information retrieval system.


query analysis music searching search engine logs 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Halvey, M., Keane, M.T.: Analysis of online video search and sharing. In: Proceedings of Hypertext 2007, Manchester, UK, September 2007, pp. 217–226 (2007)Google Scholar
  2. 2.
    Tjondronegoro, D., Spink, A.: Multimedia Web searching on a meta-search engine. In: Twelfth Australasian Document Computing Symposium, Melbourne Zoo, Australia, 10 December (2007)Google Scholar
  3. 3.
    Pass, G., Chowdhury, A., Torgeson, C.: A Picture of Search. In: 1st International Conference on Scalable Information Systems, Hong Kong (June 2006)Google Scholar
  4. 4.
    Silverstein, C., Henzinger, M., Moricz, M.: Analysis of a very large Web search engine query log. SIGIR Forum 33(1), 6–12 (1999)CrossRefGoogle Scholar
  5. 5.
    Jansen, B.J., Spink, A., Saracevic, T.: Real life, real users, and real needs: a study and analysis of user queries on the Web. Information Processing & Management 36, 207–227 (2000)CrossRefGoogle Scholar
  6. 6.
    Beitzel, S.M., Jensen, E.C., Chowdhury, A., Grossman, D., Frieder, O.: Hourly analysis of a very large topically categorized Web query log. In: Proceedings SIGIR 2004, Sheffield (UK), July 2004, pp. 321–328 (2004)Google Scholar
  7. 7.
    Spink, A.: Web searching for sexual information: an exploratory study. Information Processing & Management 40, 113–123 (2004)CrossRefGoogle Scholar
  8. 8.
    Cunningham, S.J., Nichols, D.M.: How people find videos. In: Proc. 8th ACM/IEEE-CS Joint Conference on Digital Libraries (JCDL), Pittsburgh, USA, June 2008, pp. 201–210 (2008)Google Scholar
  9. 9.
    Bainbridge, D., Cunningham, S.J., Downie, J.S.: How people describe their music information needs: a grounded theory analysis of music queries. In: Proceedings of the International Symposium on Music Information Retrieval (ISMIR 2003), Baltimore, October 2003, pp. 221–222 (2003)Google Scholar
  10. 10.
    Lee, J., Downie, J.S., Cunningham, S.J.: Challenges in cross-cultural/multilingual music information seeking. In: Proceedings of ISMIR 2005 Sixth International Conference On Music Information Retrieval, London, September 2005, pp. 1–7 (2005)Google Scholar
  11. 11.
    Cunningham, S.J., Laing, S.: An analysis of lyrics questions on Yahoo! Answers: Implications for lyric / music retrieval systems. In: Proceedings of the 11th Australasian Document Computing Symposium (December 2009)Google Scholar
  12. 12.
    Koshman, S., Spink, A., Jansen, B.J.: Web searching on the Vivisimo search engine. Journal of the American Society for Information Science and Technology 57(14), 1875–1887 (2006)CrossRefGoogle Scholar
  13. 13.
    Downie, J.S.: Music Information Retrieval. Annual Review of Information Science & Technology 37(1), 295–340 (2003)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Sally Jo Cunningham
    • 1
  • David Bainbridge
    • 1
  1. 1.Department of Computer ScienceUniversity of WaikatoHamiltonNew Zealand

Personalised recommendations