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  • 1. HT06, Position Paper, Tagging, Taxonomy, Flickr, Academic Article, ToRead , Presentation Cameron Marlow, Mor Naaman, danah boyd, Marc Davis Yahoo! Research
  • 2. What Are Tags?
    • “ A tag is a keyword or descriptive term associated with an item as means of classification by means of a folksonomy. Tags are usually chosen informally and personally by the author / creator of the item — i.e. not usually as part of some formally defined classification scheme. Tags are typically used in dynamic, flexible, automatically generated internet taxonomies for online resources such as computer files, web pages, digital images, and internet bookmarks.”
    • Wikipedia, 2006
  • 3. Del.icio.us
  • 4. Flickr
  • 5. Why Yahoo? Yahoo, circa 1996
  • 6. Why Yahoo? Yahoo, circa 2004
  • 7. Why Yahoo? +
  • 8. Motivation
    • Introduce tagging for academic audiences
    • Create a common language for current practitioners
    • Point to potential and possible directions of further research
    • Method
      • Develop a model of tagging
      • Survey existing systems and features
      • Develop taxonomy
    Tagging Model Taxonomy Prelim. Study Future Work
  • 9. Tagging Systems: Simple Model
  • 10. Tagging - Simple Model
    • Keywords
    • Describing connected resources
    • Sounds familiar?
    (Automatic resource compilation by analyzing hyperlink structure and associated text, Chakrabarti et al, 1998)
  • 11. What About Anchor Text?
    • Democratization and personalization
    • Extent and scale
    • User-inclusive model (not site- or page-based)
    • Notion of connected/related users
    • Intent of action (e.g., description vs. navigation or reference)
    • Richness of context
  • 12. Wait! Where Are We? Tagging Model Taxonomy Prelim Study Future Work
  • 13. Taxonomy
    • Create a common language
    • Point out differences and generative factors
    • Two taxonomies
      • Systems
      • Incentives (see paper)
  • 14. Systems Taxonomy
    • Who
    • How
    • What
    • Where from
    Structure and nature of resulting tags
  • 15. Tagging Rights
    • Who is allowed to tag a resource?
    Self-tagging Open Permission-based
  • 16. Tagging Support
    • Does the system “help” in tagging?
    Blind Viewable Suggested
  • 17. Tag Aggregation
    • How tags for individual resources are aggregated
    Set Bag
  • 18. Object Type
    • What is the type of resource being tagged?
    Textual Non-textual
  • 19. Object Source
    • Where the object media originates from
    User-contributed Global System
  • 20. Where are we now? Tagging Model Taxonomy Prelim. Study Future Work
  • 21. Case Study: Flickr and Del.icio.us
    • Flickr
      • Rights: Permission-based
      • Support: Blind
      • Aggregation: Set
      • Type: Non-textual
      • Source: User-contributed
    • Del.icio.us
      • Rights: Owner
      • Support: Suggested
      • Aggregation: Bag
      • Type: Textual
      • Source: Global
  • 22. Growth of tags
    • Number of distinct tags in 10 user collections, over time
    Index of photo Total number of distinct tags
  • 23. Similar to del.icio.us? 1000 500 0 0 2500 5000 Figure from Golder et al, 2005 Index of bookmark Total number of distinct tags Scales are different!
  • 24. Together Index of photo Total number of distinct tags
  • 25. Case Study: Flickr and Del.icio.us
    • Flickr
      • Rights: Permission-based
      • Support: Blind
      • Aggregation: Set
      • Type: Non-textual
      • Source: User-contributed
    • Del.icio.us
      • Rights: Owner
      • Support: Suggested
      • Aggregation: Bag
      • Type: Textual
      • Source: Global
  • 26. Case Study: Flickr
    • Nobody tags other people’s content
    • Why?
    Not collected Not identified Not prominent In user’s account As coming from the tagger In the interface, as “opinion” Not aggregated Can’t “vote” on tag/item pair
  • 27. Almost done Tagging Model Taxonomy Prelim. Study Future Work
  • 28. Future Research
    • Search / IR
      • Comparison of hypertext and tags
      • Spam detection
    • Linguistics / NLP
      • Taxonomy generation
      • Sociolinguistics
    • Collaborative Filtering
      • Identify trends (locally and globally)
      • Trust metrics
      • Identify influencers
  • 29. Thank You
    • Cameron Marlow
    • [email_address]
    • http://research.yahoo.com
    • Data?