Social Discovery
in an Information Abundant World
Ben Shneiderman
ben@cs.umd.edu
@benbendc
Founding Director (1983-2000), Human-Computer Interaction Lab
Professor, Dept. of Computer Science
Member, Institute for Advanced Computer Studies
Miles Conrad Award Lecture
NFAIS, Feb. 28, 2011
Interdisciplinary research community
- Computer Science & Info Studies
- Psych, Socio, Poli Sci & MITH
(www.cs.umd.edu/hcil)
Design Issues
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Input devices & strategies
• Keyboards, pointing devices, voice
• Direct manipulation
• Menus, forms, commands
Output devices & formats
• Screens, windows, color, sound
• Text, tables, graphics
• Instructions, messages, help
Collaboration & Social Media
Help, tutorials, training
• Visualization
Search
www.awl.com/DTUI
Fifth Edition: 2010
Spotfire: Retinol’s role in embryos & vision
http://registration.spotfire.com/eval/default_edu.asp
Fact-Finding Search
Discovery Process: Task Analysis
Specific fact finding (known-item search)
On what day was Barack Obama born?
<Google succeeds>
Discovery Process: Task Analysis
Specific fact finding (known-item search)
On what day was Barack Obama born?
<Google succeeds>
Extended fact finding (vague query)
What cities did John McCain live in since he became a Senator?
Exploration of availability (vague result request)
What genealogical information on Barack Obama is at the National Archives?
Discovery Process: Task Analysis
Specific fact finding (known-item search)
On what day was Barack Obama born?
<Google succeeds>
Extended fact finding (vague query)
What cities did John McCain live in since he became a Senator?
Exploration of availability (vague result request)
What genealogical information on Barack Obama is at the National Archives?
Open-ended browsing and problem analysis (hidden assumptions)
How has John McCain’s position on the environment changed since 2001?
Mismatch with metadata (requires exhaustive search)
How has Barack Obama’s choice of clothing changed during his campaign?
Discovery Process: Task Analysis
Specific fact finding (known-item search
Extended fact finding (vague query
Exploration of availability (vague result request
Open-ended browsing and problem analysis (hidden assumptions
Mismatch with metadata (requires exhaustive search
Discovery Process: Task Analysis
Specific fact finding (known-item search
1-minute
Extended fact finding (vague query
Weeks
&Exploration of availability (vague result request
Months
Open-ended browsing and problem analysis (hidden assumptions
Mismatch with metadata (requires exhaustive search
Discovery Process: Design Challenges
Enrich query formulation
Expand result management
Enable long-term effort
Enhance collaboration
Deal with special cases:
- Legal, patent & medical searches require thoroughness
- Proving non-existence is difficult
- Outlier items can be critical
- Bridging items can be critical
NFAIS 2011: Social Discovery
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Facebook minutes exceed Google minutes
NFAIS 2011: Social Discovery
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Facebook minutes exceed Google minutes
Social referral exceeds solitary search
Social Referral Exceeds Solitary Search
Search and Rescue: How to Become Findable and Shareable in Social Media
http://searchenginewatch.com/3639969
http://ukwebfocus.wordpress.com/2010/06/09/if-social-discovery-is-beating-traditional-search/
NFAIS 2011: Social Discovery
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Facebook minutes exceed Google minutes
Social referral exceeds solitary search
Ambitious challenges generate powerful collaborations
NFAIS 2011: Social Discovery
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Facebook minutes exceed Google minutes
Social referral exceeds solitary search
Ambitious challenges generate powerful collaborations
Shift to App discovery & mobile search
NFAIS 2011: Social Discovery
•
•
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Facebook minutes exceed Google minutes
Social referral exceeds solitary search
Ambitious challenges generate powerful collaborations
Shift to App discovery & mobile search
Social Social Social Social Social Social Social Social
Social Social Social Social Social Social Social Social
Social Social Social Social Social Social Social Social
Social Social Social Social Social Social Social Social
Social Social Social Social Social Social Social Social
Social Social Social Social Social Social Social Social
Social Social Social Social Social Social Social Social
Social Social Social Social Social Social Social Social
Social Social Social Social Social Social Social Social
Discovery Process: Visual Analytics
Sense-Making Loop
• Information Seeking
• Information Foraging
• Exploration  Insight
• Present Results
Thomas & Cook, Illuminating the Path (2004)
Social Discovery: New Media Lifestyle
Prosumption Cycle
• Producers & Consumers
• User Generated Content
• Crowdsourcing
• Collective Intelligence
http://www.socialtimes.com/2008/07/new-media-lifecycle-social-discovery/
Social Bookmarking: del.icio.us & connotea
Collaboration: Tagging & voting
Treemap: Digg Overview
www.hivegroup.com
Chomp: App Discovery
Blekko:
YAHOO! Answers
Wikia Answers
Quora
Search question topics
Read questions
Read answers
from named sources
Rate & contribute
VIVOweb: Enabling Networking of Scientists
Biodiversity: Encyclopedia of Life
eol.org
NASA Clickworkers: Crowdsource Science
Citizen Science Alliance
Collaboratories: Discovery Communities
Bos et al.,
Olson, Gary M., Zimmerman, Ann, & Bos, Nathan. (2008)
Scientific Collaboration on the Internet. MIT Press.
Health, Healthcare & Wellness
Social Marketplace: Mechanical Turk
Social Challenge: Netflix Prize
Social Search/Discovery: Active & Passive
Social Challenge: Innocentive
TopCoder: Asking for Programming Help
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Linux, Mozilla, Apache,...
CrisisCamp, Sahana, Hackathons, ....
Social Discovery
Social
Discovery
Create Capacity
Seek Solution
Individual
Tag, Comment,
Rate, Review,
Summarize
Ask questions,
Offer challenge,
Request collaboration,
Seek experts
Community
Establish thesauri,
Prepare catalogs,
Aggregate knowledge,
Organize resources
Give answers,
Respond to challenge,
Discuss alternatives,
Offer advice
From Reader to Leader:
Motivating Technology-Mediated Social Participation
All
Users
Reader
Contributor
Collaborator
`
Preece & Shneiderman, AIS Trans. Human-Computer Interaction1 (1), 2009
aisel.aisnet.org/thci/vol1/iss1/5/
Leader
Motivating Readers
Usability
Sociability
Interesting & relevant content presented in
attractive, well-organized layouts
Encouragement by friends, family,
respected authorities, advertising
Frequently updated content with
highlighting to encourage return visits
Repeated visibility in online, print,
television, other media
Support for newcomers: tutorials, animated
demos, FAQs, help, mentors, contacts
Understandable norms & policies
Clear navigation paths 
sense of mastery and control
Sense of belonging: recognition of
familiar people & activities
Universal usability: novice/expert, small/large
display, slow/fast network, multilingual,
support for users with disabilities
Charismatic leaders with visionary
goals
Interface design features to support reading,
browsing, searching, sharing
Safety & privacy
Motivating Contributors
Usability
Sociability
Low threshold interfaces to encourage
small contributions (no login)
Support for legitimate peripheral
participation
High ceiling interfaces that allow large
frequent contributions
Chance to build reputation over time
while performing satisfying tasks
Visibility for users’ contributions & impact aggregated over time
Recognition for the highest quality
& quantity of contributions
Visibility of ratings & comments
Recognition of a person’s specific
expertise
Tools to undo vandalism, limit malicious
users, control pornography & libel
Policies & norms for contributions
Motivating Collaborators
Usability
Sociability
Ways to locate relevant & competent
individuals to form collaborations
Atmosphere of empathy & trust that
promotes belonging to the community &
willingness to work within groups to
produce something larger
Tools to collaborate: communicate
within groups, schedule projects,
assign tasks, share work products,
request assistance
Altruism: a desire to support the
community, desire to give back,
willingness to reciprocate
Visible recognition collaborators, e.g.
authorship, citations, links,
acknowledgements
Ways to develop a reputation for
themselves & their collaborators;
develop & maintain status within group
Ways to resolve differences
(e.g. voting), mediate disputes &
deal with unhelpful collaborators
Respect for status within the community
Motivating Leaders
Usability
Sociability
Leaders are given higher visibility &
Leadership is valued and given an
their efforts are highlighted, sometimes
honored position & expected to meet
with historical narratives, special
expectations
tributes, or rewards
Leaders are given special powers, e.g.
Respect is offered for helping others &
to promote agendas, expend resources, dealing with problems
or limit malicious users
Mentorship efforts are visibly celebrated, Mentors are cultivated & encouraged
e.g. with comments from mentees
NodeXL:
Network Overview for Discovery & Exploration in Excel
www.codeplex.com/nodexl
NodeXL:
Network Overview for Discovery & Exploration in Excel
www.codeplex.com/nodexl
NodeXL: Import Dialogs
www.codeplex.com/nodexl
Tweets at #WIN09 Conference: 2 groups
Tweets at #NFAIS11 Conference: Sunday
Size by Betweenness Centrality
Tweets at #NFAIS11 Conference: Sunday
Tweets at #NFAIS11 Conference: Monday
Size by Betweenness Centrality
Tweets at #NFAIS11 Conference: Monday
Size by Betweenness Centrality
Tweets at #NFAIS11 Conference: Monday
Size by Betweenness Centrality, Color by Cluster
WWW2010 Twitter Community
Tweets for #HCI: 2 groups
Oil Spill Twitter Community
www.codeplex.com/nodexl/
CHI2010 Twitter Community
www.codeplex.com/nodexl/
NodeXL: HIPAA, F-R layout, Size by Followers
www.codeplex.com/nodexl
NodeXL: HIPAA, H-K layout, extract smalls,
size by Tweets
www.codeplex.com/nodexl
NodeXL: HIPAA, H-K, Size by Betweenness Centrality
www.codeplex.com/nodexl
NodeXL: HIPAA, H-K, Size by Betweenness Centrality
www.codeplex.com/nodexl
NodeXL: HIPAA, H-K, Size by Betweenness Centrality
www.codeplex.com/nodexl
NodeXL: HIPAA, H-K, Size by Betweenness Centrality
www.codeplex.com/nodexl
Flickr clusters for “mouse”
Computer
Mickey
Animal
Flickr networks
Analyzing Social Media Networks with NodeXL
I. Getting Started with Analyzing Social Media Networks
1. Introduction to Social Media and Social Networks
2. Social media: New Technologies of Collaboration
3. Social Network Analysis
II. NodeXL Tutorial: Learning by Doing
4. Layout, Visual Design & Labeling
5. Calculating & Visualizing Network Metrics
6. Preparing Data & Filtering
7. Clustering &Grouping
III Social Media Network Analysis Case Studies
8. Email
9. Threaded Networks
10. Twitter
11. Facebook
12. WWW
13. Flickr
14. YouTube
15. Wiki Networks
www.elsevier.com/wps/find/bookdescription.cws_home/723354/description
Social Media Research Foundation
Researchers who want to
- create open tools
- generate & host open data
- support open scholarship
Map, measure & understand
social media
Support tool projects to
collection, analyze & visualize
social media data.
smrfoundation.org
UN Millennium Development Goals
To be achieved by 2015
• Eradicate extreme poverty and hunger
• Achieve universal primary education
• Promote gender equality and empower women
• Reduce child mortality
• Improve maternal health
• Combat HIV/AIDS, malaria and other diseases
• Ensure environmental sustainability
• Develop a global partnership for development
28th Annual Symposium
May 25-26, 2011
www.cs.umd.edu/hcil
Social Search/Discovery: Active&Passive
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Create content: scanned & born digital
• Individual & group efforts
Improve info quality
• consistency, clarity, completeness
• currency, sourced, linked
Organize data: taxonomies, tagging, rating
Common queries, results ranking, query refinement
Interpreting & organizing results
Social media has fundamentally changed the way that people discover products,
services and content. It's no longer simply about the old method of product, price and
placement, it's about relevance, value and virality. As the effectiveness of traditional
online marketing continues to decline, how can you be sure that your message is not
only absorbed, but embraced, passed along and viewed as valuable? There have
already been plenty of studies conducted that show us that word of mouth is the most
trusted source of information for internet users. It's probably fair to say that word of
mouth is the most trusted method of discovery and influence in general. I'm much
more likely to go see a movie if a good friend tells me I'll like it versus seeing an ad.
http://heavybagmedia.com/blog/2008/11/28/maximizing-social-discovery-opportunities-on-the-social-web
“Noovo is a social discovery platform that helps
users contribute, find and share highly relevant
content in the simplest possible way,”
Social Discovery
Social
Discovery
Create Capacity
Seek Solution
Individual
Tag, Comment,
Rate, Review,
Summarize
Ask questions,
Offer challenge,
Request collaboration,
Seek experts
Community
Establish thesauri,
Prepare catalogs,
Aggregate knowledge,
Organize resources
Give answers,
Discuss alternatives,
Respond to challenge,
Offer advice
Social Discovery in an Information Abundant World
Ben Shneiderman ben@cs.umd.edu Twitter @benbendc
University of Maryland, Founding Director (1983-2000), Human-Computer Interaction Lab
Professor, Department of Computer Science, Member, Institute for Advanced Computer Studies
Miles Conrad Award Lecture
NFAIS, February 28, 2011
Asking for Help
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Requests to your community
Posts to discussion groups
Questions to free answer services
Questions to paid answer services
• Mechanical Turk
• Quora
Innocentive
Forming Collaborations
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Find fellow searchers with shared interests
Hire staff to follow your leadership
Recruit domain experts to work with you
Hire team to provide diverse skills
Social Search/Discovery: Active & Passive
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Early systems: GroupSearch, CoSearch, Ariadne
Tagging: Digg, Connotea
Rating, Ranking, Reviewing
Social Search: Wikia Search
Facebook & Twitter requests
U.S. Library of Congress
• Scholars, Journalists, Citizens
• Teachers, Students
Visible Human Explorer (NLM)
• Doctors
• Surgeons
• Researchers
• Students
NASA Environmental Data
• Scientists
• Farmers
• Land planners
• Students
Bureau of the Census
• Economists, Policy
makers, Journalists
• Teachers, Students
NSF Digital Government Initiative
• Find what you need
• Understand what you Find
Census,
NCHS,
BLS, EIA,
NASS, SSA
www.ils.unc.edu/govstat/
Recovery.gov
Information Visualization
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Visual bandwidth is enormous
• Human perceptual skills are remarkable
• Trend, cluster, gap, outlier...
• Color, size, shape, proximity...
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Three challenges
• Meaningful visual displays of massive data
• Interaction: widgets & window coordination
• Process models for discovery:
ManyEyes: A web sharing platform
http://manyeyes.alphaworks.ibm.com/manyeyes