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

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

24. Do you really have a tour called WTF?

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

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

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

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.

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!