web wise2008
DESCRIPTION
A talk given during the WebWise 2008 conference in Miami, FL in a session called "The Power of Discovery". This talk covers recent developments in the steve.museum social tagging research project.TRANSCRIPT
www.steve.museum [email protected]
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 OfficerIndianapolis Museum of [email protected]
www.steve.museum [email protected]
Who is Steve?
• Steve is collaboration of museums
• Steve is exploring the effectiveness of social tagging for accessing and documenting museum collections
www.steve.museum [email protected]
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?
www.steve.museum [email protected]
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
www.steve.museum [email protected]
A Few Statistics
• 2 Deployments of steve• Multi-institutional• Single Institutional
• Hosted by The Metropolitan Museum of Art
www.steve.museum [email protected]
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
www.steve.museum [email protected]
Data Collection Overview
• Single Institution Deployment• Recruited specifically by MMA
• 850 registered users
• 252 Works of Art
• 51,477 Tags assigned!!!
www.steve.museum [email protected]
Overview of Experiments
1. Tell Me What I’m Seeing.- Meta-Data vs. No Meta-Data
www.steve.museum [email protected]
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
www.steve.museum [email protected]
Overview of Experiments
1a. Getting in the Groove- Sets vs. No Sets
www.steve.museum [email protected]
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
www.steve.museum [email protected]
Overview of Experiments
2. What Other People Say?- Tags vs. No Tags
www.steve.museum [email protected]
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
www.steve.museum [email protected]
Overview of Experiments
3. It’s My Turn to Pick!- Pick by Image and Pick by Tag
www.steve.museum [email protected]
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
www.steve.museum [email protected]
Overview of Experiments
4. Sharing is Good…- Facebook and Email Integration
www.steve.museum [email protected]
Term Review
• Term by Term classification by Institutions.
• Useful for mapping the quality and character of terms as judged by the institution.
www.steve.museum [email protected]
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
www.steve.museum [email protected]
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?
www.steve.museum [email protected]
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
www.steve.museum [email protected]
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
www.steve.museum [email protected]
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?
www.steve.museum [email protected]
Get to know Steve
• Visit the Steve websitehttp://www.steve.museum
• Join the Steve mailing [email protected]
• Help a guy out! Do some tagging!http://tagger.steve.museum http://apps.facebook.com/stevemuseum