Visitors As Data

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Visitors As Data

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