Noshir Contractor

1
From Disasters to WoW How the Science of
Networks can inform and be informed by the
Grid and Web 2.0
Noshir Contractor Professor, Departments of
Speech Communication Psychology Director, Age
of Networks Initiative, Center for Advanced
Study Director, Science of Networks in
Communities - National Center for Supercomputing
Applications University of Illinois at
Urbana-Champaign nosh_at_uiuc.edu
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• Turn on power set MODE with MODE button. You
  can confirm the MODE you chose as the red
  indicator blinks.
• Lamp blinks when (someone with) a Lovegety for
  the opposite sex set under the same MODE as yours
  comes near.
• FIND lamp blinks when (someone with) a Lovegety
  for the opposite sex set under different mode
  from yours comes near. May try the other MODES to
  GET tuned with (him/her) if you like.

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Key Takeaways

• Desire for social networking tools to assist
  multidisciplinary, interdisciplinary, and
  transdisciplinary (MIT) collaboration
• Development of multi-theoretical multilevel
  (MTML) understandings of why we create, maintain,
  dissolve and reconstitute social network links.
• Development of tools and algorithms to
  Discover, Diagnose, and Design (3D) more
  effective social networks
• Opportunity to harvest empirical
  multi-dimensional network data from recent
  efforts on the Grid and Web 2.0.
• Opportunity to enable more effective social
  networking within the Grid
• Challenge to develop MTML 2.0 to explain creation
  of links in multidimensional networks

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Aphorisms about Networks

• Social Networks
• Its not what you know, its who you know.
• Cognitive Social Networks
• Its not who you know, its who they think you
  know.
• Knowledge Networks
• Its not who you know, its what they think you
  know.

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Cognitive Knowledge Networks
Source Newsweek, December 2000
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INTERACTION NETWORKS
Non Human Agent to Non Human Agent Communication
Non Human Agent (webbots, avatars, databases,
push technologies) To Human Agent
Publishing to knowledge repository
Retrieving from knowledge repository
Human Agent to Human Agent Communication
Source Contractor, 2001
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COGNITIVE KNOWLEDGE NETWORKS
Non Human Agents Perception of Resources in a
Non Human Agent
Human Agents Perception of Provision of
Resources in a Non Human Agent
Non Human Agents Perception of what a Human
Agent knows

Human Agents Perception of What Another Human
Agent Knows
Why Tivo thinks I am gay and Amazon thinks I
am pregnant .
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Human to Human Interactions and Perceptions
Human to Non Human Interactions and Perceptions
Non Human to Human Interactions and Perceptions
Non Human to Non Human Interactions and
Perceptions
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WHY DO WE CREATE, MAINTAIN, DISSOLVE, AND
RECONSTITUTE OUR COMMUNICATION AND KNOWLEDGE
NETWORKS?
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Social DriversWhy do we create and sustain
networks?

• Theories of self-interest
• Theories of social and resource exchange
• Theories of mutual interest and collective action

• Theories of contagion
• Theories of balance
• Theories of homophily
• Theories of proximity
• Theories of co-evolution

Sources Contractor, N. S., Wasserman, S.
Faust, K. (2006). Testing multi-theoretical
multilevel hypotheses about organizational
networks An analytic framework and empirical
example. Academy of Management Review. Monge, P.
R. Contractor, N. S. (2003). Theories of
Communication Networks. New York Oxford
University Press.
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Structural signatures of Social Drivers
Theories of Self interest
Theories of Exchange
Theories of Balance
Theories of Collective Action
Theories of Homophily
Theories of Cognition
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Co-evolution of knowledge networks and 21st
century organizational forms

• NSF KDI Initiative 1999-04. PI Noshir
  Contractor, University of Illinois.
• Co-P.I.s Monge, Fulk, Bar (USC), Levitt, Kunz
  (Stanford), Carley (CMU), Wasserman (Indiana),
  Hollingshead (Illinois).
• Three dozen industry partners (global, profit,
  non-profit)
• Boeing, 3M, NASA, Fiat, U.S. Army, American Bar
  Association, European Union Project Team, Pew
  Internet Project, etc.

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• Public Goods / Transactive Memory
• Allocation to the Intranet
• Retrieval from the Intranet
• Perceived Quality and Quantity of Contribution to
  the Intranet

• Transactive Memory
• Perception of Others Knowledge
• Communication to Allocate Information

Motivations for search In Knowledge Nets

• Inertia Components
• Collaboration
• Co-authorship
• Communication

Social Exchange - Retrieval by coworkers on
other topics
Proximity -Work in the same location
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Motivation for Search in Knowledge Networks Using
Exponential Random Graph Modeling Techniques
1. Social Communication 0.144 2. Perception
of Knowledge Communication to
Allocate 0.995 3. Perception of Knowledge
Provision 0.972 4. Perception of Knowledge,
Social Exchange, Social Communication 0.851
5. Perception of Knowledge, Proximity,
Social Communication 0.882
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A contextual meta-theory ofsocial drivers for
creating and sustaining networks
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Projects Investigating Social Drivers for
Search/Diffusion in Networks
Business Applications PackEdge Community of
Practice (PG) Vodafone-Ericsson Club
for virtual supply chain management (Vodafone)
Science Applications CLEANER Collaborative
Large Engineering Analysis Network for
Environmental Research (NSF) CP2R
Collaboration for Preparedness, Response
Recovery (NSF) TSEEN Tobacco Surveillance
Evaluation Epidemiology Network (NSF, NIH,
CDC)
Core Research Social Drivers for Creating
Sustaining Communities
Societal Justice Applications Cultural
Networks Assets In Immigrant Communities
(Rockefeller Program on Culture
Creativity) Economic Resilience NGO Community
(Rockefeller Program on Working Communities)
Entertainment Applications World of Warcraft
(NSF) Everquest (NSF, Sony Online
Entertainment)
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Contextualizing Goals of Communities
Challenges of empirically testing, extending, and
exploring theories about networks until now
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Enter Cyberinfrastructure Web 2.0
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Components of Cyberinfrastructure

• High Performance Computing services
• Grid computing services
• Data, Data Analysis, Visualization
• Data Services (access to static, streaming, and
  sensor data)
• Workflow Visual-Analytic Tools (access to
  software tools, computing power, etc.)
• Document Libraries (documents, images, data sets,
  etc.)
• Collaboratories, Observatories, Virtual
  Organizations
• Collaboration Services (chat, video conferencing,
  blog, wiki, collaborative editing)
• Referral Services (people, data, documents,
  analytic tools)
• Customization interfaces for communities and
  sub-communities
• Education Workforce Development
• Training, Outreach, Mentoring services

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Science and Engineering Cyberinfrastructures
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Multidimensional Networks in CI
(Cyberinfrastructure) Multiple Types of Nodes and
Multiple Types of Relationships
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Its all about Relational Metadata

• Technologies that capture communities
  relational meta-data (Pingback and trackback in
  interblog networks, blogrolls, data provenance)
• Technologies to tag communities relational
  metadata (from Dublin Core taxonomies to
  folksonomies (wisdom of crowds) like
• Tagging pictures and video (Flickr, YouTube)
• Tagging blogs and news (Technorati, digg)
• Social bookmarking (del.icio.us, LookupThis,
  BlinkList)
• Social citations (CiteULike.org)
• Social libraries (discogs.com, LibraryThing.com)
• Social shopping (SwagRoll, Kaboodle,
  thethingsiwant.com)
• Social networks (FOAF, XFN, MySpace, Facebook)
• Technologies to manifest communities
  relational metadata (Tagclouds, Recommender
  systems, Rating/Reputation systems, ISIs
  HistCite, Network Visualization systems)

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Digital Harvesting of Relational Metadata
Web of Science Citation
Bios, titles descriptions
Personal Web sites Google search results
CI-KNOW Analyses and Visualizations
http//iknowinc.com/iknow/sb_digital_forum/www/ikn
ow.cgi
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Projects Investigating Social Drivers for
Communities
Science Applications CLEANER Collaborative
Large Engineering Analysis Network for
Environmental Research (NSF) CP2R
Collaboration for Preparedness, Response
Recovery (NSF) TSEEN Tobacco Surveillance
Evaluation Epidemiology Network (NSF, NIH,
CDC)
Business Applications PackEdge Community of
Practice (PG) Vodafone-Ericsson Club
for virtual supply chain management (Vodafone)
Core Research Social Drivers for Creating
Sustaining Communities
Societal Justice Applications Cultural
Networks Assets In Immigrant Communities
(Rockefeller Program on Culture
Creativity) Economic Resilience NGO Community
(Rockefeller Program on Working Communities)
Entertainment Applications World of Warcraft
(NSF) Everquest (NSF, Sony Online
Entertainment)
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CLEANER Grand Challenge

• How do we detect and predict waterborne hazards
  in real time?
• How do we predict the effects of human activities
  on the quantity, distribution, and quality of
  water?
• How do we improve water cycle engineering
  management strategies to provide water quantity
  and quality to sustain humans and ecosystems?

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Enabling Environmental Engineering Communities
with Cyberinfrastructure

• CLEANER
• Collaborative Large-scale Engineering Analysis
  Network for Environmental Research
• Human-dominated, complex environmental systems,
  e.g.,
• River basins
• Coastal margins
• What researchers requested
• Access to live and archived
• sensor data
• Customized network referrals to people,
  documents, datasets
• Analyze, visualize and compare
• data
• Organize, automate and share cyber-research
  processes

Users can simultaneously view and discuss data
and analyses
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CLEANER Community A multidimensional network
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CI-KNOW Harvesting the online communitys
relational meta-data
Network Maps
Cybercommunity Resources
Network Referrals
Cyberinfrastructure Use
Network Diagnostics
External Resources
INPUTS
PROCESSES
OUTPUTS
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CI-KNOW Harvesting the online communitys
relational meta-data
Network Maps
Cybercommunity Resources
Network Referrals
Cyberinfrastructure Use
Network Diagnostics
External Resources
INPUTS
PROCESSES
OUTPUTS
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(No Transcript)
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Cybercommunity Resources
David C. writes a message on a CLEANER forum
about hydrological monitoring systems.
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Cybercommunity Resources
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Recommending People
David C. retrieves hydrological datasets on C-U
County rivers
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(No Transcript)
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Cybercommunity Resources
David C. writes a message on a CLEANER forum
about hydrological monitoring systems.
37
Cybercommunity Resources
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Recommending People
David C. retrieves hydrological datasets on C-U
County rivers
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(No Transcript)
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CI-KNOW Cyberinfrastructure Knowledge Networks
on the Web A Recommender System for Locating
Resources in a Knowledge Network
CI-KNOW Team Noshir Contractor, Hank Green, York
Yao Steven Harper, Nat Bulkley, Andy Don
Abstract. A Knowledge Network is a
multi-dimensional network created from the
interactions and interconnections among the
people, documents, data, analytic tools, and
interactive collaboration spaces (like forums and
wikis) associated with a collaborative
environment. CI-KNOW is a suite of software
tools that leverages automated data collection
and social network theories, analysis techniques
and algorithms to infer an individuals interests
and expertise based on their interactions and
activities within a Knowledge Network. Recent
developments in social network theories and
methods provide the backbone for a modular system
that creates recommendations from relational
metadata. The CI-KNOW Recommender mines the
Knowledge Network associated with communities
use of cyberinfrastructure tools and uses
relational metadata to record connections among
entities in the Knowledge Network. A network
navigation portlet allows users to locate
colleagues, documents, data or analytic tools in
the network and to explore their networks through
a visual, step-wise process. An internal auditing
portlet offers administrators diagnostics to
assess the growth and health of the entire
Knowledge Network. The CI-KNOW recommender system
is integrated with the CLEANER portal, created by
the Environmental Cyberinfrastructure
Demonstration Project, which supports the
activities of environmental science communities
(CLEANER and CUAHSI) under the umbrella of the
WATERS network (http//cleaner.ncsa.uiuc.edu). It
is also integrated with the ToBIG portal, created
by the SONIC group to support the activities of
tobacco researchers and policy makers
(http//tobig.ncsa.uiuc.edu). CI-KNOW development
is supported primarily by grants from the
National Science Foundation and the National
Cancer Institute and the National Institutes of
Health.

• Elements of CI-KNOW
• Multi-disciplinary research and policy
  communities have indicated substantial interest
  in a network referral system that would identify
  available resources of interest to their members,
  e.g.
• Researchers with similar or complementary
  expertise
• Popular, new, or relevant data sets
• Appropriate analysis tools
• Models or model systems
• Visual analytic tools, workflows, etc
• To meet this interest, the Science of Networks in
  Communities (SONIC) groups social networking
  technology, CI-KNOW, is integrated into portals
  and other collaborative environments. CI-KNOW is
  a suite of software tools that leverages
  automated data collection and network extraction
  techniques to infer a scientists interests and
  expertise based on their interactions and
  activities within a Knowledge Network. It
  implements advanced algorithms to provide network
  referrals that take into account socio-technical
  incentives for knowledge sharing.
• At the User level, CI-KNOW provides tools for
• Knowledge network recommendations and
  visualizations
• Network neighborhood visualization and navigation
• At the Administrator level CI-KNOW provides
  portlets for
• Recommendations
• Global network visualization/navigation

A Hybrid, Modular Recommender System

• Traditional referral systems do not take into
  account (i) multiple relationships that link
  Knowledge Network entities (ii) the multiple
  socio-technical incentives that influence the
  effectiveness of a network-based recommendation.
  Recent developments in Social Network theories
  and methods provide ways to fill in this gap.
• CI-KNOW recommendations emerge from a modular
  system that allows for the combination and
  hybridization of multiple mechanisms for creating
  recommendations from structural metadata. Modules
  are based on Social Network Analysis Theory and
  can be updated at any time.
• Current and Planned Modules
• Structural similarity of users and items to
  search terms what is most alike?
• Structural closeness of users and items to search
  terms what is closest?
• Popularity of an item what is conventional?
• Structural influence Who or what are key links
  in the network?
• Innovation whats new?
• Burst activities whats hot?
• Exchange who you previously helped and now owes
  you one?
• Proximity who is geographically near you?

Knowledge Networks in CI (Cyberinfrastructure)
What is a Knowledge Network?
CI-KNOW Recommender System Tools Current and in
Development
User Modified

• A Knowledge Network is a multi-dimensional
  network created from the interactions and
  interconnections among people, documents, data,
  analytic tools, and interactive collaboration
  spaces (like forums and wikis) associated with a
  collaborative environment.
• A Knowledge Network grows organically as users
  publish, access data, use analysis tools, post to
  forums, etc.
• Linkages among items are stored as meta-data,
  which is accessed by CI-KNOW.
• The CI-KNOW recommender system mines the
  Knowledge Network associated with communities
  use of cyberinfrastructure tools like the portal,
  combining that data with information gathered
  from other sources like bibliographic databases
  and patent information.

CI-KNOW tools are being developed as independent
web applications that can appear as portlets
within a portal or collaborative environment.
An intuitive user-interface enable members of
the community to use the network referral system.
To make the system sustainable, CI-KNOW
continuously retrieves data from the community,
processes these data, and uses internal network
referral algorithms to update its network
referrals for use within the community. The
network navigation tools allow users to locate
themselves or their colleagues, documents or
workspaces in the knowledge network and to
explore their networks through a visual,
step-wise process. Users will have the option to
explore these networks in an optimal
topological space, or to have the networks
connected to geographical maps through an
approach similar to the one used by Google Maps
Users can specify preferences and settings for
how they use the CI-KNOW system and what they
share with others who use the system. Module
importance is user-determined they determine
which modules should contribute to the total
recommendation function and how much. Users can
determine what sorts of recommendation results
they will see and modify the type and quantity of
recommendations they receive. For Push
recommendations CI-KNOW relies on user
preferences and inferred search terms to provide
pro-active recommendations For Pull
recommendations users can specify preferences
and search terms for CI-KNOW in real-time, with
the capability to set default specifications
Rather than providing traditional search results
(such as documents that contain a specific
keyword), users receive recommendations to
entities (datasets, documents, people) that are
associated through transactions, interactions,
and interconnections with items that contain the
keyword.
Internal Modifications
Concerns

• The strongest recommendation in the entire
  Knowledge network is given a value of 1. Other
  recommendations are scaled by this global
  maximum.
• Structural recommendations reflect the profile
  similarity of items and take the users
  connections into account.
• Recommendation lists from multiple modules are
  merged and duplicates are removed, retaining
  highest scores
• A users recommendation history is saved and
  recommendations are generally not repeated within
  a session
• When users get recommendations for entities to
  which they do not have direct access, they are
  offered visual network paths to reach those
  entities.

• Recommendation quality and validity must be
  investigated.
• Test cases provide insight
• User feedback for live recommendations
• User tracking
• Privacy/Access/Permissions policies must be set
• Access to documents, contact information, and
  usage tracking determined by owners
• Visualization of users, documents and data in KN
  determined by owners

Automated Metadata Collection
When individuals log in and uses a collaborative
environment, their behavior is logged within a
unique session that is linked to their user ID.
Each session stores use logs as Resource
Description Framework (RDF) triples that can be
called metadata Metadata summarizes objects
(whether users or items), locates objects in
geographical space, indexes content, stores and
defines network ties among objects, and records
usage and access information. A triple is
information stored in the form
ltsubjectgtltpredicategtltobjectgt For example ltuser
1gtltis author ofgtltforum post 1gt Predicates form
the structural linkages between items in the
knowledge network. CI-KNOW harvests structural
metadata to record connections among entities in
the knowledge network
The CI-KNOW portlet window returns a folded
list of recommendations to users while they are
working inside the portal. The recommendation
itself is an active link to the contact
information for the user or to the particular
forum post, document, or data that is recommended.
Clicking on the Why button in the portlet
provides more detail about why a user has been
given that particular recommendation and provides
a text description and a network visualization of
the path between the user, the search term and
the recommendation.

• Metadata are classified according to the context
  from which the data are drawn and the primary
  descriptive characteristic that the data possess.
  
• Data contexts from collaborative environments
  include, but arent limited to
• user profiles
• chat environments
• forums and solution centers
• wiki and collaborative editing spaces
• individual notebooks
• data repositories
• analysis tool areas
• libraries of relevant documents
• external links to data, tools and documents
• data mined from bibliographic data bases or other
  sources

CI-KNOW Tools in Development

• Administrative subsystem for searching the KN and
  reporting of a standard set of network
  diagnostics for knowledge portfolio management

With one click, the user can show all the
shortest paths, hide node labels, and access more
tools for Knowledge Network exploration
The Tell Me Why page presents information about
the shortest path between the user, the search
term and the recommendation.
Instantiations of CI-KNOW
CI-KNOW Metadata Taxonomies
The Tell Me Why visualization shows the
shortest path between the user, the search term
and the recommendation.
CI-KNOW Technical Specifications
How CI-KNOW Searching Works

• A user enters a word or phrase into the
  Recommendation search box. Those items that
  contain the word or phrase become the seeds for
  CI-KNOW
• CI-KNOW uses relational metadata to crawl outward
  from the seeds, recording which entities in the
  knowledge network are connected directly or
  indirectly to these seeds
• Recommendations are based on algorithms that
  take into account the structural paths to
  relevant entities as well as the social
  motivations and incentives for knowledge sharing

CyberIntegrator

• Java-based
• Run on a dedicated Apache Tomcat server
• Platform independent
• Uses a web services/API paradigm
• Calls lt.25 second for current system settings,
  calculation dependent (based on table lookup and
  sorting)
• Scalable to 10 million links and 1 million nodes
  with no increase in call time

Waters Network Portal
Funding for CI-KNOW development comes from NSF
grants BES-0414259, BES-0533513, IIS-0535214,
SBE-0555115, and SCI-0525308 and Office of Naval
Research grant N00014-04-1-0437.
ToBIG Portal
41
Projects Investigating Social Drivers for
Communities
Science Applications CLEANER Collaborative
Large Engineering Analysis Network for
Environmental Research (NSF) CP2R
Collaboration for Preparedness, Response
Recovery (NSF) TSEEN Tobacco Surveillance
Evaluation Epidemiology Network (NSF, NIH,
CDC)
Business Applications PackEdge Community of
Practice (PG) Vodafone-Ericsson Club
for virtual supply chain management (Vodafone)
Core Research Social Drivers for Creating
Sustaining Communities
Societal Justice Applications Cultural
Networks Assets In Immigrant Communities
(Rockefeller Program on Culture
Creativity) Economic Resilience NGO Community
(Rockefeller Program on Working Communities)
Entertainment Applications World of Warcraft
(NSF) Everquest (NSF, Sony Online
Entertainment)
42
3D Strategy for Enhancing CoP Networks

• Discovery Effectively and efficiently foster
  network links from people to other people,
  knowledge, and artifacts (data sets/streams,
  analytic tools, visualization tools, documents,
  etc.). If only we knew what we knew.
• Diagnosis Assess the health of CoPs internal
  and external networks - in terms of scanning,
  absorptive capacity, diffusion, robustness, and
  vulnerability to external environment
• Design or re-wire networks using social and
  organizational incentives (based on social
  network research) and network referral systems to
  enhance evolving and mature communities.

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Mapping Flows in the PackEdge CoP Network
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PackEdge CoP Vital Statistics

• Exploration
• Scanning Access to expertise external to CoP
• Absorption Ability to absorb expertise external
  to CoP
• Vulnerability Brokered by members external to
  CoP
• Exploitation
• Diffusion Ability to diffuse expertise
  throughout CoP
• Robustness Not relying on few critical CoP
  members to keep things together

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Pre-wired PackEdge CoP Network
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Re-wired PackEdge CoP Network
47
Wiring the PackEdge CoP Network for Success

• Increase the likelihood to give and get
  information to the right target and source
  respectively
• Benefits for CoP
• Increase absorptive capacity from 45.3 to 53.4
• Reduce number of steps for diffusion from 4.3 to
  2.6
• Costs for CoP
• Increase communication links of network leaders
  from 28 to 38 ( 150 new links).
• Increase criticality of network leaders from 26.7
  to 48.5

48
Projects Investigating Social Drivers for
Communities
Business Applications PackEdge Community of
Practice (PG) Vodafone-Ericsson Club
for virtual supply chain management (Vodafone)
Science Applications CLEANER Collaborative
Large Engineering Analysis Network for
Environmental Research (NSF) Collaboration
for Preparedness, Response Recovery
(NSF) TSEEN Tobacco Surveillance Evaluation
Epidemiology Network (NSF, NIH, CDC)
Core Research Social Drivers for Creating
Sustaining Communities
Societal Justice Applications Cultural
Networks Assets In Immigrant Communities
(Rockefeller Program on Culture
Creativity) Economic Resilience NGO Community
(Rockefeller Program on Working Communities)
Entertainment Applications World of Warcraft
(NSF) Everquest (NSF, Sony Online
Entertainment)
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WoW Massively Multiplayer Online Role Playing
Game
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Rise of WoW
Source http//www.mmogchart.com/
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Goals of WoW Community

• Teams perform diverse quests within the game
  environment, typically varying in length from one
  hour to several days, with the goal of achieving
  an objective, gaining resources, and increasing
  experience.
• Exploiting, Bonding Swarming

52
Contextualizing Goals of WoW
53
Mapping Goals to Theories WoW Gaming Community
54
Data Collection

• Data were collected from all 184 individuals who
  belonged to 16 guilds at 3 points in time.
• T1 initial contact and survey administration
• T2 two weeks after initial survey administration
• T3 four weeks after initial survey
  administration
• Demographic information
• Gender
• Female members (21.7)
• Male members (78.3)
• Ethnicity
• Caucasian (79.3)
• Asian/ Pacific Islander (15.2)
• African American (2.2)
• Hispanic/Latino (1.1)
• Native American (1.1)

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Information Retrieval - Time One
56
Information Retrieval - Time Two
57
Information Retrieval -Time Three
58
Density of Communication Ties Decreases over Time
59
Unraveling the Structural Signatures

• Incentive for creating a WoW link with someone
• -1.08 (cost of creating a link)
  Self-interest
• 0.29 (benefit of reciprocating) Exchange
• 3.07 (benefit for being a friend of a friend)
• Balance
• 0.04 (benefit of connecting to an expert)
• Cognition

All coefficients significant at 0.05 level
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Summary

• Theories about the social motivations for
  creating, maintaining, dissolving and re-creating
  social network ties in multidimensional networks
• Development of cyberinfrastructure/Web 2.0
  provide the technological capability to capture
  relational metadata needed to more effectively
  understand (and enable) communities.
• Computational modeling techniques to model
  network dynamics in large-scale multi-agent
  systems
• Exponential random graph modeling techniques to
  empirically validate the local structural
  signatures that explain emergent global network
  properties

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SONIC Research Team Members
Nat Bulkley Postdoctoral Research Associate NCSA,
UIUC
Andy Don Research Programmer NCSA, UIUC
Steven Harper Postdoctoral Research
Associate NCSA, UIUC
Hank Green Research Scientist NCSA, UIUC
Chunke Su Graduate Research Assistant Speech
Communication, UIUC
Mengxiao Zhu Graduate Research Assistant Speech
Communication, UIUC
York Yao Research Programmer NCSA, UIUC
Diana Jimeno-Ingrum Graduate Research
Assistant Labor Industrial Relations, UIUC
Annie Wang Graduate Research Assistant Speech
Communication, UIUC
62
Acknowledgements