Web Wise2008

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    Web Wise2008 - Presentation Transcript

    1. Listening to our Visitors Steve.museum and the impact of social tagging for access to online collections. WebWise 2008, 10:45 am – 12:15 pm 05/06/08 Robert Stein, Chief Information Officer Indianapolis Museum of Art [email_address]
    2. Who is Steve?
      • Steve is collaboration of museums
      • Steve is exploring the effectiveness of social tagging for accessing and documenting museum collections
    3. Why Study Social Tagging?
      • Can tagging help me find art easier?
      • Do visitors give museums new and valuable information?
      • Can tagging change the way I look at art?
    4. What Steve has been up to.
      • Steve has completed the first year of a two year research grant from IMLS
      • Steve has completed three phases of data collection experiments and is currently entering its fourth phase
      • Experiments focus on understanding the behavior of web visitors who tag art
    5. A Few Statistics
      • 2 Deployments of steve
        • Multi-institutional
        • Single Institutional
          • Hosted by The Metropolitan Museum of Art
    6. Data Collection Overview
      • Multi-Institutional Deployment
        • Please visit http://tagger.steve.museum
        • 4103 users (784 registered – 3352 anonymous)
        • 1784 Works of Art
        • 35,776 Tags assigned
    7. Data Collection Overview
      • Single Institution Deployment
        • Recruited specifically by MMA
        • 850 registered users
        • 252 Works of Art
        • 51,477 Tags assigned!!!
    8. Steve Reporting Tool XML Schema for Reports
    9. Overview of Experiments
      • 1. Tell Me What I’m Seeing. - Meta-Data vs. No Meta-Data
    10. Meta-data or Not…
    11. Preliminary Insights
      • More Tags without Metadata
        • “ Taggers who do not see metadata seem to supply more tags. There were an average of 4.5 terms supplied when metadata was shown compared to 5.75 when only an image was shown without any description” (Trant 2007)
        • 28% increase in tagging
    12. Overview of Experiments
      • 1a. Getting in the Groove
      • - Sets vs. No Sets
    13. Sets or Not…
    14. Preliminary Insights
      • More Tags with Sets
        • “ In TermSet 1 the average number of tags per work was 4.6 for users who saw random works, and 5.8 for users who saw sets.” (Trant 2007)
        • 26% increase
    15. Overview of Experiments
      • 2. What Other People Say? - Tags vs. No Tags
    16. Tags +/- Meta-data
    17. Preliminary Insights
      • More Tags with Tags
        • In TermSet 2 the average number of tags per work was 7.1 for users who were shown tags from others versus 5.7 tags per work for users who were not shown other’s tags.
        • 24.5% increase
    18. Overview of Experiments
      • 3. It’s My Turn to Pick! - Pick by Image and Pick by Tag
    19. Pick Images to Tag
    20. Make a Set from Tags
    21. Some Very Early Thoughts
      • Just finished the data collection for this experiment
      • Anecdotal Observations
        • Session length appears shorter
        • Terms per work down
        • Works tagged per session down
    22. DEMO: Steve Tagger
    23. Overview of Experiments
      • 4. Sharing is Good… - Facebook and Email Integration
    24. Send to Facebook Friends
    25. Email to a Friend
    26. Facebook Profile Pages
    27. Facebook App Pages
    28. DEMO: Steve Facebook Integration
    29. Term Review
      • Term by Term classification by Institutions.
      • Useful for mapping the quality and character of terms as judged by the institution.
    30. Term Review
      • Classified as:
      • Useful / Not Useful for describing or finding the specific work of art.
      • Positive or Negative Opinions of the art
      • Misperception of the work
      • Foreign Language Term
      • Misspelling
      • Very Personal Meaning
    31. Term Review
    32. Term Review
    33. Term Review
    34. Finding Matches
      • How can we tell if these are new words or not?
        • Direct match against museum object metadata (i.e. artist, title, materials, etc…)
        • Direct match against thesauri (i.e. AAT, ULAN, TGN)
        • How about terms that aren’t direct matches?
    35. Finding Matches
      • Use WordNet to facilitate mapping classes of terms to AAT facets
      • Attempt to find a distribution of terms as they relate to concepts in AAT (or NOT)
        • Attributes and Properties, Built Environment, Color, Furnishings and Equipment, Materials, People, Physical and Mental Activities, Processes and Techniques, Styles and Periods, Visual and Verbal Communications
    36. Steve in the Wild
      • Steve is Open Source Software and available from: http://sourceforge.net/projects/steve-museum
      • The Steve software platform has been built in such a way that other institutions can use tagging for their own websites
      • The Steve team is eager to see social tagging adopted widely among museums
    37. Indianapolis Museum of Art
    38. Indianapolis Museum of Art
    39. Indianapolis Museum of Art
    40. ArtsConnectEd2
    41. ArtsConnectEd2
    42. Where’s Steve Going?
      • Make Steve Easy for others to deploy
      • Investigate what it means to do In Gallery tagging.
      • How does enthusiast tagging play a role in museums?
    43. Get to know Steve
      • Visit the Steve website http://www.steve.museum
      • Join the Steve mailing list [email_address]
      • Help a guy out! Do some tagging! http://tagger.steve.museum http://apps.facebook.com/stevemuseum

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