Model-Driven Research in Social Computing

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
Image from: http://www.flickr.com/photos/ourcommon/480538715/

  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

2010-06-13 Workshop 2

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

2010-06-13 Workshop 3

  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

2010-06-13 Workshop 4

  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

2010-06-13 Workshop 5

From: Sun et al, ICWSM2009

2010-06-13 Workshop 6

  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

  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

2010-06-13 Workshop 9

Number of Articles (Log Scale)

http://en.wikipedia.org/wiki/Wikipedia:Modelling_Wikipedia’s_growth

*In thousands Monthly Active Editors

Monthly Edits by Editor Class (in thousands)

Monthly Ratio of Reverted Edits

2010-06-13 Workshop 18

  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

€

  Ecological
population
growth
model

–  Also
depend
on
environmental
conditions

–  K,
carrying
capacity
(due
to
resource
limitation)

dN N
= rN(1− )
dt K

€

  Follows
a
logistic
growth
curve

New Article

  Carrying
Capacity
as
a
function
of
time.

  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

  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

€

2010-06-13 Workshop 25

Social Tagging Creates Noise

•  Synonyms
•  Misspellings
•  Morphologies

People use different tag
words to express similar
concepts.

2010-06-13 Workshop 26

Encoding
Retrieval

“video

people

talks
technology”

h:p://www.ted.com/index.php/speakers

h:p://edge.org

“science

research
cogni*on”

27

2010-06-13 Workshop 27

Concepts
Topics

Users
Documents

Noise

Tags

Decoding
Encoding

T1…Tn

2010-06-13 Workshop 28

2010-06-13 Workshop 29

2010-06-13 Workshop 30

Source: Hypertext 2008 study on del.icio.us (Chi & Mytkowicz)
2010-06-13 Workshop 31

2010-06-13 Workshop 32

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.

2010-06-13 Workshop 33

Semantic Similarity Graph
Web
Tools
Reference

Guide
Howto

Tutorial
Tips
Help

Tip Tutorials

Tricks

2010-06-13 Workshop 34

Tags URLs

P(URL|Tag)

P(Tag|URL)

  Spreading
Activation
in
a
bi-‐graph

  Computation
over
a
very
large
data
set

–  150
Million+
bookmarks

2010-06-13 Workshop 35

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

2010-06-13 Workshop 36

2010-06-13 Workshop 37

2010-06-13 Workshop 38

2010-06-13 Workshop 39

Dellarocas, MIT Sloan Management Review

2010-06-13 Workshop 40

(1)
Generate
new
tools
and
systems,
new
techniques

(2)
Generate
data
that
looks
like
real
behavioral
data

2010-06-13 Workshop 41

Poor heuristic

Good heuristic

2010-06-13 Workshop 42

Solo

Cooperative (“good hints”)

2010-06-13 Workshop 43

  Appropriate
for

the
occasion

2010-06-13 Workshop 44

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

framing

Before Search
externally-motivated self-motivated
searchers searchers the context

31% 69%
Social Interactions

the
requirements

28% 13% 59%
During Search

FORAGING
process
step B step B
“evidence file”

After Search

28% 72%

ORGANIZE DISTRIBUTE

others others

the context

Before Search
searchers searchers

31% 69%
Social Interactions

the
requirements

28% 13% 59%
During Search

FORAGING
process
step B step B
“evidence file”


28% 72%
After Search


ORGANIZE DISTRIBUTE

others others

the context
Before Search
searchers searchers

31% 69%
43% users engaged in pre-search social Social Interactions
interactions.
the
reasons for interacting: to get advice, guidelines, feedback,
requirements
or search tips
28% 13% 59%
During Search

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”

After Search

28% 72%
59% users engaged in post-search sharing.
ORGANIZE DISTRIBUTE
reasons for interacting: thought others might be interested,
to get feedback, out of obligation
others others

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

28% 13% 59%
During Search

•  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”

After Search

28% 72%
•  sharing tools built-in to (search) site Spartag.us"

•  collective tag clouds (for feedback)
ORGANIZE DISTRIBUTE
Mr. Taggy"

others others

2010-06-13 Workshop 51

  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

2010-06-13 Workshop 52
Image from: http://www.flickr.com/photos/ourcommon/480538715/

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