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Folksonomies: Diverse, Democratic and Evolving Classification

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As the Internet and computer systems grow in size and complexity, users are having more difficulty finding web objects. Traditional top-down taxonomies are being assisted by the bottom-up approach of …

As the Internet and computer systems grow in size and complexity, users are having more difficulty finding web objects. Traditional top-down taxonomies are being assisted by the bottom-up approach of folksonomies. This classification system is a result of the aggregation of social tagging in the user’s own language (Vander Wal, 2007). Folksonomies have emerged as a powerful component of the Web 2.0 landscape. This presentation explores the current state of folksonomies.

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  • 1. Folksonomies : Diverse, Democratic and Evolving Classification Michael E. Ryan [email_address] @ryaninteractive UPA Boston User Experience Conference 2009
  • 2. About Me
  • 3. Where do you put this book in a taxonomy? (Takahashi, 2009)
  • 4. Oreilly Media – Math? (O'Reilly Media, Inc., 2009)
  • 5. Amazon’s classification
      • What terms describe this book?
    (Amazon.com, 2009)
  • 6. What terms describe this book?
    • Tags Amazon users have attached to this book
    (Amazon.com, 2009)
  • 7. What is a Folksonomy?
    • The term “folksonomy” was created by Thomas Vander Wal on a listserve in July 2004. Term combines folk and taxonomy .
    • “ Folksonomy is the result of personal free tagging of information and objects… for one's own retrieval… The value in this external tagging is derived from people using their own vocabulary and adding explicit meaning, which may come from inferred understanding of the information/object”
    (Vander Wal, 2007b)
  • 8. Tagging Model
    • Tagger > Tag > Object
  • 9. Aggregation
      • Tags are compiled in aggregation
      • Finds most popular tags for an object
      • Connects tags to multiple objects
      • Finds most popular tags on a website
      • Connects tags and objects by inference
    (Smith, 2008)
  • 10. Pivot browsing (Amazon.com, 2009) (Rosenfeld & Morville, 2007)
  • 11. Search and Filter Users can add or subtract tags to filter results (Amazon.com, 2009) (Golder & Huberman, 2005)
  • 12. Tag counts to rank popularity Number of people who tagged (Delicious, 2009)
  • 13. Tag cloud (Flickr, 2009) (Rainie, 2007)
  • 14. Exercise
    • Assign tags (keywords) to these objects. These could be for everyone or just for you.
    (Amazon.com, 2009)
  • 15. Amazon’s tags (Amazon.com, 2009)
  • 16. How do people tag?
    • Individual and Social retrieval
      • Literal descriptive
      • Personal abstract
      • Personal categorization
      • Social benefit
    (Golder & Huberman, 2005) (Vander Wal, 2007a) (Smith, 2008)
  • 17. Who is tagging?
    • 2007 PEW Internet & American Life Project survey
      • 28% of Americans (42 million) online have tagged
      • 7% (10 million) tag daily.
      • Americans who tag tend to be under 40 and have a higher education and income.
      • Race/ethnicity of the taggers was reported as:
        • 26% White, non-Hispanic
        • 36% Black, non-Hispanic
        • 33% English-speaking Hispanic
    (Rainie, 2007) (Vander Wal, 2007a)
  • 18. How businesses can use tagging
      • Validate or identify gaps in a taxonomy
      • Additional metadata enhances findability
      • Encourage tagging by making it easy, fun & social
    (Trant, 2006) (Smith, 2008)
  • 19. Problems with tagging
    • Messy metadata
      • User-controlled vocabulary can help
      • 2006 Trant study only needed to remove 6.7% of terms
      • Problem decreases as tag usage grows
    (Trant, 2006) (Smith, 2008)
  • 20. Tag abuse
      • Give taggers as much freedom as possible to encourage use, but need to protect users from abuse
        • Remove/disable expletives and hate speech
        • Allow users to flag tags and taggers
      • Spam
      • Vocal Minority
      • Negative Tagging
    (Smith, 2008)
  • 21. Negative Tagging
    • Expect some negative tags for objects. Best to allow this as long as it is not abusive to users.
    (Amazon.com, 2009) (Smith, 2008)
  • 22. For more info
    • Gene Smith - Tagging: People-powered metadata for the social web (voices that matter)
    • www.personalinfocloud.com Thomas Vander Wal’s Blog
    • [email_address] @ryaninteractive
  • 23. Other Manga Guides (No Starch Press, 2009)
  • 24. Thank You
    • Questions?
  • 25. Appendix A: References
      • Amazon.com. (2009). http://www.amazon.com
      • Delicious. (2009). http://www.delicious.com/
      • Flickr. (2009). http://www.flickr.com/photos/tags/
      • Golder, S. & Huberman, B. A. (2005) The structure of collaborative tagging systems. Technical report, In-formation Dynamics Lab, HP Labs. http://arxiv.org/ftp/cs/papers/0508/0508082.pdf
      • No Starch Press (2009) http://nostarch.com/manga/
      • O'Reilly Media, Inc. (2009). http://oreilly.com/pub/topic/math
      • Rainie, L. (2007). 28% of online Americans have used the Internet to tag content. http://www.pewinternet.org/pdfs/PIP_Tagging.pdf
      • Rosenfeld, L., & Morville, P. (2007). Information architecture for the World Wide Web (3rd ed.). Sebastopol, CA: O'Reilly.
      • Smith, G. (2008). Tagging: People-powered metadata for the social web (voices that matter) Berkeley, CA: New Riders.
      • Takahashi, S. (2009). The manga guide to statistics. San Francisco: No Starch Press.
      • Trant, J. (2006). Social classification and folksonomy in art museums: Early data from the steve.museum tagger prototype. In Proceedings of the 17th SIG Classification Research Workshop, 2006. http://www.archimuse.com/papers/asist-CR-steve-0611.pdf
      • Vander Wal, T. (2007a, January 31). Pew research on tagging. Personal InfoCloud. http://www.personalinfocloud.com/2007/01/pew_research_on.html
      • Vander Wal, T. (2007b, February 2). Folksonomy coinage and definition. Off the Top. http://vanderwal.net/folksonomy.html
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