2007 KMWorld Presentation on Augmented Social Cognition Research at PARC

Web 2.0 in the Enterprise
Improving collaboration through research insights
in coordination costs

KMWorld & Intranets 2007
November 7, 2007

Ed H. Chi, Ph.D.
Manager, Augmented Social Cognition Area
echi@parc.com
Lawrence C. Lee
Director of Business Development
lawrence.lee@parc.com
Intelligent Systems Laboratory
Palo Alto Research Center Inc.

PARC Overview

  Interdisciplinary research center
  Founded in 1970
  Spun out of Xerox in 2002
  Business model:
–  Contract research
–  Licensing
–  Joint ventures
–  Spinoffs

KM World & Intranets 2007 – © 2007 Palo Alto Research Center Inc. 2

Web 2.0 and Enterprise 2.0

Software as a Service (SaaS)
Remixing and Mashup of Services

Rich User Interactions
Social Networks

Network Effects and
Collective Intelligence
Disruptive
New Collaboration Environments
Potential


Benefits of Web 2.0

  Web 2.0 tools in the enterprise:
–  Blogs
–  Wikis
–  Social bookmarking
–  RSS
–  Search
–  Social networking

  Benefits
–  Simple to set up
–  Easy to use
–  Knowledge captured as side effect of communication


Costs of Web 2.0

  Benefits of Web 2.0 do not come for free
–  Interference effects
–  Interaction costs
–  Noisy folksonomies

  Research Methodology
–  Analyze activity on social systems
–  Develop models on interaction dynamics
–  Design new systems to increase benefits and reduce cost
–  Test systems in Living Laboratories


Research Foundation

  PARC’s rich history of research in information systems
–  User interface research
–  Information foraging theory
–  Sensemaking

  Augmented Social Cognition
–  Vision: Enhance the ability of a group of people to remember,
think, and reason
–  Combines cognitive science with social computing research


Outline

  Overview
–  Benefits of Web 2.0 in the Enterprise
–  Costs of Web 2.0
»  Interference effects, interaction costs, noisy folksonomies

  Three examples of PARC research and solutions
–  WikiDashboard: Providing social transparency in Wikispaces
–  SparTag.us: Lowering interaction costs in tagging systems
–  TagSearch: Reducing noise in tagged data and harnessing
tags for search


I. Interference Effects in Wikipedia
Declining percentage of activity devoted to article
editing in Wikipedia

Source: Wikipedia (Kittur, Suh, Pendelton, Chi)


Visualizing Interference and Conflict
Revert Graph for Wikipedia page: Terry Schiavo
Anonymous (vandals/
spammers)

Sympathetic to
husband

Mediators

Sympathetic to
parents

Source: Wikipedia (Kittur, Suh, Pendelton, Chi)


WikiDashboard

  Transparency of social dynamics can reduce conflict
and coordination issues
–  WikiDashboard: Social dashboard for wikis
–  Prototype system: http://wikidashboard.parc.com

  Visualization for every wiki page
showing edit history timeline and
top individual editors

  Can drill down into activity history
for specific editors and view edits
to see changes side-by-side


WikiDashboard Summary

  Key problems
–  Lack of visibility into social dynamics of wikis
–  Lack of transparency in individual activity

  PARC’s solution
–  Visualization of individual edit activity to increase
transparency, accountability, and quality of wikis
–  Connect individuals with shared interests by reviewing
activity on specific wiki topics


II. Interaction Costs and Social Systems

  Interaction costs determine

# People willing to produce for “free”
number of people who
participate
  Surplus of attention &
motivation at small
transaction costs
  Therefore…
  Important to keep
interaction costs low
Cost of participation


Social Bookmarking

  Means of storing, annotating,
and sharing links
  Typically done for oneself
  But implicit sharing promotes
transfer of expertise
  Lightweight form of enterprise
knowledge sharing
  Players:
–  Del.icio.us
–  Connotea
–  CiteUlike
–  and many enterprise social
bookmarking systems (that
operate behind the firewall)


SparTag.us

  Social bookmarking is easy to do but there is still
friction in the process
–  Tagging in new window separate from document
–  Tagging limited to page level even though you want to tag a
paragraph only
–  Tags and annotations do not remain with paragraphs if
referenced on other pages

  New approach: Make tagging and retrieval of tagged
content easier
  Lower interaction costs and increase participation


SparTag.us

  Key innovations
–  Click-to-tag
–  Paragraph-level tags
–  Social annotations
–  Tag portability

Highlights (annotations)

Paragraph-level tagging


My SparTag.us

  Web-based notebook keeps track of all clipped
paragraphs with tags and highlights


SparTag.us Summary

  Key problems
–  Friction in current tagging process
–  Tags are limited to page level only
–  Difficult to find content of interest on page later

–  In situ tagging on the same page using click-to-tag
–  Tags applied to paragraphs
–  Tags and annotations stay with paragraph, even on new
pages (e.g. block quote on a blog post)
–  My SparTag.us notebook displays all tagged and annotated
paragraphs


III. Noise in Social Tagging Systems

  Folksonomies emerge from social tagging systems

  Benefits
–  Freeform tagging instead of conforming to rigid taxonomies
–  More flexibility allows users to recall information later
–  Information discovery using tag browsing

  Weaknesses
–  Noise
–  Inconsistency
–  Difficult to leverage for search


Think of tagging as a communication…
Concepts Topics

Users TagsNoise Documents
Decoding T1…Tn Encoding

Information Theoretic Measures

  Mutual information
I(Doc; Tag)
–  Amount of information
known about documents
given knowledge of tags
  Conditional entropy
H(Doc|Tag)
I(Doc;Tag) –  Additional amount of
H(Doc|Tag) information needed to be
known in order to identify
a document given
knowledge of tags
Knowledge Knowledge
of tags of documents

I(Doc; Tag)

  Tags contain less
information about
documents over time

Source: del.icio.us (Chi & Mytkowicz)

H(Doc | Tag)

  More information
needs to be
known over time,
over and above
what is given by
tags


Rise in Average Tags per Bookmark

  Note parallel with
increasing
average number
of words in a
search query


But Social Tagging Creates Noise

•  Synonyms
•  Misspellings
•  Morphologies

People use different
tag words to express
similar concepts.


Use Semantic Analysis to Reduce Noise

Semantic Similarity Graph
Web
Tools
Reference

Guide
Howto

Tutorial
Tips
Help

Tip Tutorials

Tricks


TagSearch – A New Search Engine

  New approach: harness human intelligence encoded
in tags on documents and web pages to improve
search precision


TagSearch Summary

  Key problems
–  Noisy tag data
–  Users do not want to conform to rigid taxonomies
–  Too many irrelevant results in enterprise search

–  Let users tag freeform but use data mining techniques to
reduce noise
–  Harness normalized tag data in new search algorithm
–  Result: High precision enterprise search engine trained on
content tagged by enterprise community


Conclusion

  Effective deployment of Web 2.0 tools requires
understanding of both costs and benefits.
–  Increasing transparency results in higher quality
–  Lowering interaction costs results in higher participation
–  Decreasing noise results in greater search precision

  PARC works with enterprises to solve specific
problems and deploy customized versions of its suite
of Web 2.0 technologies.


Contacts

  Stop by our booth at the Expo - Booth 313
  Our contact information:

Ed H. Chi, Ph.D.
Manager, Augmented Social Cognition Area
echi@parc.com
650.812.4312

Lawrence C. Lee
Director of Business Development
lawrence.lee@parc.com
650.812.4756


2007 KMWorld Presentation on Augmented Social Cognition Research at PARC

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