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Robert Stein Chief Information Officer Indianapolis Museum of Art [email protected] @rjstein http://www.imamuseum.org Visitors As Data Creating a Reinforcing Relationship with User Engagement

Visitors As Data

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Page 1: Visitors As Data

Robert SteinChief Information OfficerIndianapolis Museum of [email protected]@rjsteinhttp://www.imamuseum.org

Visitors As Data

Creating a Reinforcing Relationship with User

Engagement

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VISITORSAREROBOTS

source ~donsolo

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Visitor Inclusion

• No offense to Bruce, but who doesn’t want this?

VISITORSARE DATA

source ~victoriapeckham

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Modes of Visitor Data

PASSIVE

ACTIVE

AGGRESSIVE

VISITOR’S ACTION

NONE

INTERNAL

COORDINATED

MUSEUM’S RESPONSE

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Passive Data Generation

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How Can We Get Here?

PASSIVE

ACTIVE

AGGRESSIVE

VISITOR’S ACTION

NONE

INTERNAL

COORDINATED

MUSEUM’S RESPONSE

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Visitors As Data

Visitors Havethe Brain

Power WeWant

Credit: Benedict Campbell

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Unfortunately, visitors aren’tclones we can direct to

do our bidding

source ~donsolo

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How can visitors take part in powering their ownexperience?

source ~ mindcaster-ezzolicious

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Can we create a virtuous circle with visitors that clearly expresses the value and impact of their participation?

VISITOR ENGAGEMENT

MUSEUMIMPACT

source ~m-louis

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Social Tagging

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www.steve.museum [email protected]

A Few Highlights

88% of tags were usefulIf you found this work using this term would you be surprised?

Museum professionalsfound most tags useful

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www.steve.museum [email protected]

A Few Highlights

Tags are different than museum documentation:

86% of all tags not found in label copy

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www.steve.museum [email protected]

A Few Highlights

Tags are almost always useful when they are assigned two or more times

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Pretty Cool Tools

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You want me to do

what?

source ~donsolo

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Silly Museum… Robots are Friends

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Do you really have a tour

called WTF?

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Crowdsourced cropping from the V&A: http://collections.vam.ac.uk/crowdsourcing

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This is Getting Easier

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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 New

Interfaces– Facilitate Cross-Collection

Search / Browsing

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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

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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

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Some are Easy to Tag

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Some are not

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Some are really hard…

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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.

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Mechanical Turk Demographics

Source:Panos Ipeirotis - http://behind-the-enemy-lines.blogspot.com/2008/03/mechanical-turk-demographics.html

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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

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So, 300,000 tags…

can’t we just make a

Wordle

outta that?

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TagCow

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So how do we deal with

this stuff anyway?

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• 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.

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Can we use keywords from text as context for tags?

Can Tags help to disambiguate keywords from text?

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Heirarchy for Tags

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Heirarchy for Tags

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Finding a Needle in the Haystack

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Trust Networks for Weighting

D

B

E

C

AA Trusts B

B DOES NOT TRUST E

B Trusts CB Trusts D

D Trusts

C A INFERS Trust in B

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ThankYou!