This document summarizes Ed Chi's keynote presentation on augmented social cognition at the Hypertext 2010 workshop. Chi discusses characterizing social systems using analytics, modeling social interactions and dynamics, prototyping tools to increase benefits or reduce costs, and evaluating prototypes with real users. He provides examples of models for information diffusion and Wikipedia growth. Chi also covers challenges in identifying relevant models from literature and techniques for addressing noise in social tagging like synonyms, misspellings and morphologies.
Ed Chi Explores Augmented Social Cognition and Modeling Social Systems
1. Ed
H.
Chi,
Principal
Scientist
and
Area
Manager
Augmented
Social
Cognition
Area
Palo
Alto
Research
Center
Hypertext 2010 Keynote at MSM
2010-06-13 Workshop
1
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2. Cognition:
the
ability
to
remember,
think,
and
reason;
the
faculty
of
knowing.
Social
Cognition:
the
ability
of
a
group
to
remember,
think,
and
reason;
the
construction
of
knowledge
structures
by
a
group.
– (not
quite
the
same
as
in
the
branch
of
psychology
that
studies
the
cognitive
processes
involved
in
social
interaction,
though
included)
Augmented
Social
Cognition:
Supported
by
systems,
the
enhancement
of
the
ability
of
a
group
to
remember,
think,
and
reason;
the
system-‐supported
construction
of
knowledge
structures
by
a
group.
Citation:
Chi,
IEEE
Computer,
Sept
2008
Hypertext 2010 Keynote at MSM
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3. Characteriza*on
Models
Evalua*ons
Prototypes
Characterize
activity
on
social
systems
with
analytics
Model
interaction
social
and
community
dynamics
and
variables
Prototype
tools
to
increase
benefits
or
reduce
cost
Evaluate
prototypes
via
Living
Laboratories
with
real
users
Hypertext 2010 Keynote at MSM
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4. All
models
are
wrong!
– Some
are
more
wrong
than
others!
So
what
are
theories
and
models
good
for?
A
summary
of
what
we
think
is
happening
– Ways
to
describe
and
explain
what
we
have
learned
– Predicts
user
and
group
behavior
– Helps
generate
new
novel
tools
and
systems
Hypertext 2010 Keynote at MSM
2010-06-13 Workshop 4
5. For
example,
for
information
diffusion,
it’s
theory
of
influentials
[Gladwell,
etc.]
– reach
a
small
group
of
influential
people,
and
you’ll
reach
everyone
else
Figure From: Kleinberg, ICWSM2009
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6. From: Sun et al, ICWSM2009
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7. Descriptive:
clarify
terms,
key
concepts
Explanatory:
reveal
relationships
and
processes
Predictive:
about
performance
and
situations
Prescriptive:
convey
guidance
for
decision
making
in
design
by
recording
best
practice
Generative:
enable
practitioners
to
create,
invent
or
discover
something
new
7
8. A
tough
task
to
identify
models
from
the
literature,
since
it
is
so
spread
out
in
various
publications
Just
a
few
examples
from
our
group.
UIST 2004 8
19. Preferential
Attachment:
Edits
beget
edits
– more
number
of
previous
edits,
more
number
of
new
edits
Growth rate depends on:
N = current population
r = growth rate of the population
N(t) = N 0 ⋅ e rt
dN
= r⋅ N
dt
Growth rate Current
of population €
population
€
20. Ecological
population
growth
model
– Also
depend
on
environmental
conditions
– K,
carrying
capacity
(due
to
resource
limitation)
dN N
= rN(1− )
dt K
€
23. Biological
system
– Competition
increases
as
population
hit
the
limits
of
the
ecology
– Advantage
go
to
members
of
the
population
that
have
competitive
dominance
over
others
Analogy
– Limited
opportunities
to
make
novel
contributions
– Increased
patterns
of
conflict
and
dominance
24. r-‐Strategist
– Growth
or
exploitation
dN N
– Less-‐crowded
niches
/
produce
many
= rN(1− )
offspring
dt K
K-‐Strategist
– Conservation
[Gunderson & Holling 2001]
– Strong
competitors
in
crowded
niches
/
invest
more
heavily
in
fewer
offspring
€
26. Social Tagging Creates Noise
• Synonyms
• Misspellings
• Morphologies
People use different tag
words to express similar
concepts.
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27. Encoding
Retrieval
“video
people
talks
technology”
h:p://www.ted.com/index.php/speakers
h:p://edge.org
“science
research
cogni*on”
Hypertext 2010 Keynote at MSM
27
2010-06-13 Workshop 27
33. Joint
work
with
Rowan
Nairn,
Lawrence
Lee
Kammerer,
Y.,
Nairn,
R.,
Pirolli,
P.,
and
Chi,
E.
H.
2009.
Signpost
from
the
masses:
learning
effects
in
an
exploratory
social
tag
search
browser.
In
Proceedings
of
the
27th
international
Conference
on
Human
Factors
in
Computing
Systems
(Boston,
MA,
USA,
April
04
-‐
09,
2009).
CHI
'09.
ACM,
New
York,
NY,
625-‐634.
Hypertext 2010 Keynote at MSM
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34. Semantic Similarity Graph
Web
Tools
Reference
Guide
Howto
Tutorial
Tips
Help
Tip Tutorials
Tricks
Hypertext 2010 Keynote at MSM
2010-06-13 Workshop 34
35. Tags URLs
P(URL|Tag)
P(Tag|URL)
Spreading
Activation
in
a
bi-‐graph
Computation
over
a
very
large
data
set
– 150
Million+
bookmarks
Hypertext 2010 Keynote at MSM
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36. Database Lucene
• Delicious • P(URL|Tag) • Serve up search
• Ma.gnolia • P(Tag|URL) results
• Tuples of • Pre-computed
• Other social cues bookmarks • Bayesian Network patterns in a fast • Well defined APIs
• [User, URL, Tags, Inference index
Time]
Crawling MapReduce Web Server
Web
Server
UI Search
Frontend Results
• MapReduce:
months
of
computa*on
to
a
single
day
• Development
of
novel
scoring
func*on
Hypertext 2010 Keynote at MSM
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40. Dellarocas, MIT Sloan Management Review
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41. (1)
Generate
new
tools
and
systems,
new
techniques
(2)
Generate
data
that
looks
like
real
behavioral
data
Hypertext 2010 Keynote at MSM
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42. Poor heuristic
Good heuristic
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43. Solo
Cooperative (“good hints”)
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44. Appropriate
for
the
occasion
Hypertext 2010 Keynote at MSM
2010-06-13 Workshop 44
45. externally-motivated self-motivated framing
the context
Before Search
searchers searchers
31% 69%
Social Interactions
GATHER REQUIREMENTS refining
the
requirements
FORMULATE REPRESENTATION
28% 13% 59%
During Search
navigational transactional informational
FORAGING
step A step A search
process
step B step B
“evidence file”
TRANSACTION SENSEMAKING
search product /end product
After Search
28% 72%
DO NOTHING TAKE ACTION
ORGANIZE DISTRIBUTE
to self 15% to proximate 87% to public 2%
others others
46. framing
Before Search
externally-motivated self-motivated
searchers searchers the context
31% 69%
Social Interactions
GATHER REQUIREMENTS refining
the
requirements
FORMULATE REPRESENTATION
28% 13% 59%
During Search
navigational transactional informational
FORAGING
step A step A search
process
step B step B
“evidence file”
TRANSACTION SENSEMAKING
search product /end product
After Search
28% 72%
DO NOTHING TAKE ACTION
ORGANIZE DISTRIBUTE
to self 15% to proximate 87% to public 2%
others others
47. externally-motivated self-motivated framing
the context
Before Search
searchers searchers
31% 69%
Social Interactions
GATHER REQUIREMENTS refining
the
requirements
FORMULATE REPRESENTATION
28% 13% 59%
During Search
navigational transactional informational
FORAGING
step A step A search
process
step B step B
“evidence file”
TRANSACTION SENSEMAKING
search product /end product
After Search
28% 72%
DO NOTHING TAKE ACTION
ORGANIZE DISTRIBUTE
to self 15% to proximate 87% to public 2%
others others
48. externally-motivated self-motivated framing
the context
Before Search
searchers searchers
31% 69%
Social Interactions
GATHER REQUIREMENTS refining
the
requirements
FORMULATE REPRESENTATION
28% 13% 59%
During Search
navigational transactional informational
FORAGING
step A step A search
process
step B step B
“evidence file”
TRANSACTION SENSEMAKING
search product /end product
28% 72%
After Search
DO NOTHING TAKE ACTION
ORGANIZE DISTRIBUTE
to self 15% to proximate 87% to public 2%
others others
49. externally-motivated self-motivated framing
the context
Before Search
searchers searchers
31% 69%
43% users engaged in pre-search social Social Interactions
interactions.
GATHER REQUIREMENTS refining
the
reasons for interacting: to get advice, guidelines, feedback,
FORMULATE REPRESENTATION
requirements
or search tips
28% 13% 59%
During Search
navigational transactional informational
FORAGING
step A search
step A
3 types of search: informational search provides a
150 reports of unique search experiences
compelling caseBfor social search support.
mapped to a canonical model of social search.
step B step
process
“evidence file”
TRANSACTION SENSEMAKING
search product /end product
After Search
28% 72%
DO NOTHING TAKE ACTION
59% users engaged in post-search sharing.
ORGANIZE DISTRIBUTE
reasons for interacting: thought others might be interested,
to get feedback, out of obligation
to self 15% to proximate 87% to public 2%
others others
50. externally-motivated self-motivated framing
the context
Before Search
searchers searchers
• instant 31%
messaging69% to personal social
(IM) Social Interactions
connections near the search box
refining
GATHER REQUIREMENTS
the
requirements
FORMULATE REPRESENTATION
28% 13% 59%
During Search
navigational transactional informational
• step A clouds from domain FORAGING
tag step A experts
search
• step B users’ search trails process feedback)
other (for
step B
• related search terms (for feedback) Similar to: Glance; Smyth"
“evidence file”
TRANSACTION SENSEMAKING
search product /end product
After Search
28% 72%
DO NOTHING TAKE ACTION
• sharing tools built-in to (search) site Spartag.us"
• collective tag clouds (for feedback)
ORGANIZE DISTRIBUTE
Mr. Taggy"
to self 15% to proximate 87% to public 2%
others others
52. Research
Vision:
Understand
how
social
computing
systems
can
enhance
the
ability
of
a
group
of
people
to
remember,
think,
and
reason.
Living
Laboratory:
Create
applications
that
harness
collective
intelligence
to
improve
knowledge
capture,
transfer,
and
discovery.
http://asc-‐parc.blogspot.com
http://www.edchi.net
echi@parc.com
Hypertext 2010 Keynote at MSM
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Image from: http://www.flickr.com/photos/ourcommon/480538715/