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Visitors As Data

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  • 1. Robert Stein
    Chief Information Officer
    Indianapolis Museum of Art
    rstein@imamuseum.org
    @rjstein
    http://www.imamuseum.org
    Visitors As Data
    Creating a Reinforcing Relationship with User Engagement
  • 2. VISITORS
    AREROBOTS
    source ~donsolo
  • 3. Visitor Inclusion
    No offense to Bruce, but who doesn’t want this?
    VISITORS
    ARE DATA
    source ~victoriapeckham
  • 4. Modes of Visitor Data
    MUSEUM’S RESPONSE
    VISITOR’S ACTION
    PASSIVE
    NONE
    ACTIVE
    INTERNAL
    AGGRESSIVE
    COORDINATED
  • 5. Passive Data Generation
  • 6.
  • 7.
  • 8.
  • 9.
  • 10. How Can We Get Here?
    MUSEUM’S RESPONSE
    VISITOR’S ACTION
    PASSIVE
    NONE
    ACTIVE
    INTERNAL
    AGGRESSIVE
    COORDINATED
  • 11. Visitors As Data
    Visitors Havethe BrainPower We
    Want
    Credit: Benedict Campbell
  • 12. Unfortunately, visitors aren’t
    clones we can direct to
    do our bidding
    source ~donsolo
  • 13. How can visitors take part
    in powering their own
    experience?
    source ~ mindcaster-ezzolicious
  • 14. MUSEUM
    IMPACT
    VISITOR
    ENGAGEMENT
    Can we create a virtuous circle with visitors that clearly expresses the value and impact of their participation?
    source ~m-louis
  • 15. Social Tagging
  • 16. www.steve.museum steve@steve.museum
    A Few Highlights
    Museum professionalsfound most tags useful
    88% of tags were useful
    If you found this work using this term would you be surprised?
  • 17. www.steve.museum steve@steve.museum
    A Few Highlights
    Tags are different than museum documentation:
    86% of all tags not found in label copy
  • 18. www.steve.museum steve@steve.museum
    A Few Highlights
    Tags are almost always useful when they are assigned two or more times
  • 19. Pretty Cool Tools
  • 20. You want me to dowhat?
    source ~donsolo
  • 21. Silly Museum… Robots are Friends
  • 22.
  • 23.
  • 24. Do you really have a tour called WTF?
  • 25.
  • 26.
  • 27.
  • 28.
  • 29.
  • 30.
  • 31.
  • 32. Crowdsourced cropping from the V&A: http://collections.vam.ac.uk/crowdsourcing
  • 33. Generating Lots of Data
    Seems overwhelming! V&A has 120K images here!
    30 sec/image = 120 images in 1 hr
    1000 person hours
    This would require 5,000 people to each crop just 24 image or spend approx 12 min
    This is very doable
  • 34. This is Getting Easier
  • 35. Steve in Action
    Funded in 2008 by the IMLS
    Led by the New Media Consortium in collaboration with IMA, Susan Chun and a host of museum partners
    A Few Project Goals
    Make Social Tagging Easy
    Develop Innovative NewInterfaces
    Facilitate Cross-CollectionSearch / Browsing
  • 36. Steve in Action Features
    Simple Import (CSV, CDWA, Scraping)
    Hosted and Themable Data Collection Platform
    Powerful API Access
    Cut-n-Paste Tagging Widgets for Easy Integration
  • 37.
  • 38. IMA’s Collection
    54,000 objects in collection
    2,242 objects on display (4%)
    26,268 objects with images (48%)
    Using Steve widgets to drive social tagging
  • 39.
  • 40.
  • 41. Some are Easy to Tag
  • 42. Some are not
  • 43. Some are really hard…
  • 44. Tagcow
    Use crowdsourcing to add tags / data to image collections
    Cost $0.15 - $0.20 per image
    Tagcow uses software built on Amazon’s Mechanical Turk to process 100,000’s of images per day.
  • 45. Mechanical Turk Demographics
    Source:PanosIpeirotis - http://behind-the-enemy-lines.blogspot.com/2008/03/mechanical-turk-demographics.html
  • 46. IMA and Tagcow
    IMA gave Tagcow links to about 26,000 collection objects with images
    Tagcow returned 298,668 Total Tags
    254,130 descriptive tags (28,708 distinct)
    44,538 color tags
    Term Frequency: Min (1), Max(4299), Avg(8.85)
    Document Frequency: Min (1) Max(134) Avg(9.94)
    29,174 tags with more than one word
  • 47. So, 300,000 tags…
    can’t we just make a
    Wordleoutta that?
  • 48. TagCow
  • 49.
  • 50.
  • 51. So how do we deal with
    this stuff anyway?
  • 52. Funded in 2008 by IMLS
    Led by University of Maryland in collaboration with IMA, Susan Chun and a working group of museums.
    Studying the relationships between social tags, scholarly text and resources, and the application of trust networks to improve access to museum collections.
  • 53.
  • 54. Can we use keywords from text
    as context for tags?Can Tags help to disambiguate
    keywords from text?
  • 55. Heirarchy for Tags
  • 56. Heirarchy for Tags
  • 57. Finding a Needle in the Haystack
  • 58. Trust Networks for Weighting
    A INFERS Trust in B
    D Trusts C
    C
    D
    B Trusts C
    B Trusts D
    B
    A Trusts B
    A
    E
    B DOES NOT TRUST E
  • 59. Thank
    You!

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