Image from: http://www.flickr.com/photos/ourcommon/480538715/
Ed H. Chi, Principal Scientist and Area Manager
Augmented Social Cognition Area Palo Alto Research Center
1 2010-06-13
Hypertext 2010 Keynote at MSM Workshop
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
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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
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Characteriza*on Models
Prototypes Evalua*ons
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
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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
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Figure From: Kleinberg, ICWSM2009
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From: Sun et al, ICWSM2009
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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
UIST 2004 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.
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Number of Articles (Log Scale)
http://en.wikipedia.org/wiki/Wikipedia:Modelling_Wikipedia’s_growth
Monthly Edits
Monthly Edits
*In thousands Monthly Active Editors
Monthly Edits by Editor Class (in thousands)
Monthly Ratio of Reverted Edits
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Preferential Attachment: Edits beget edits – more number of previous edits, more number of new edits
€
N(t) = N0 ⋅ ert
€
dNdt
= r ⋅ N
Growth rate of population
Current population
Growth rate depends on: N = current population r = growth rate of the population
Ecological population growth model – Also depend on environmental conditions – K, carrying capacity (due to resource limitation)
€
dNdt
= rN(1− NK)
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 – Less-‐crowded niches / produce many
offspring
K-‐Strategist – Conservation – Strong competitors in crowded niches /
invest more heavily in fewer offspring €
dNdt
= rN(1− NK)
[Gunderson & Holling 2001]
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• Synonyms • Misspellings • Morphologies
People use different tag words to express similar concepts.
Social Tagging Creates Noise
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Encoding Retrieval
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h:p://edge.org
“science research cogni*on”
h:p://www.ted.com/index.php/speakers
“video people talks technology”
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Topics Concepts
Users Documents
Tags
T1…Tn Encoding Decoding
Noise
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Source: Hypertext 2008 study on del.icio.us (Chi & Mytkowicz)
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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.
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Guide
Web
Howto
Tips Help
Tools
Tip
Tricks
Tutorial
Tutorials
Reference
Semantic Similarity Graph
Hypertext 2010 Keynote at MSM Workshop
Spreading Activation in a bi-‐graph Computation over a very large data set
– 150 Million+ bookmarks
Tags URLs
P(URL|Tag)
P(Tag|URL)
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Web Server
Search Results
UI Frontend
• Delicious • Ma.gnolia • Other social cues
Crawling
• Tuples of bookmarks
• [User, URL, Tags, Time]
Database • P(URL|Tag) • P(Tag|URL) • Bayesian Network Inference
MapReduce
• Pre-computed patterns in a fast index
Lucene • Serve up search results
• Well defined APIs
Web Server
• MapReduce: months of computa*on to a single day
• Development of novel scoring func*on
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Dellarocas, MIT Sloan Management Review
(1) Generate new tools and systems, new techniques (2) Generate data that looks like real behavioral data
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Poor heuristic
Good heuristic
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Solo
Cooperative (“good hints”)
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Appropriate for the occasion
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“evidence file”
SENSEMAKING
process
search FORAGING
Bef
ore
Sear
ch externally-motivated
searchers
31%
framing the context
refining the requirements
FORMULATE REPRESENTATION
GATHER REQUIREMENTS
69%
13% 59% 28%
transactional
self-motivated searchers
navigational informational
Dur
ing
Sear
ch
Aft
er S
earc
h
step A
step B
TRANSACTION
step A
step B
DO NOTHING
search product /end product
ORGANIZE DISTRIBUTE
TAKE ACTION
28% 72%
Social Interactions
to public others
to proximate others
to self 15% 87% 2%
“evidence file”
SENSEMAKING
process
search FORAGING
externally-motivated searchers
31%
framing the context
refining the requirements
FORMULATE REPRESENTATION
GATHER REQUIREMENTS
69%
13% 59% 28%
transactional
self-motivated searchers
navigational informational
Dur
ing
Sear
ch
Aft
er S
earc
h
step A
step B
TRANSACTION
step A
step B
DO NOTHING
search product /end product
ORGANIZE DISTRIBUTE
TAKE ACTION
28% 72%
Social Interactions
to public others
to proximate others
to self 15% 87% 2%
Bef
ore
Sear
ch
“evidence file”
SENSEMAKING
process
search FORAGING
Bef
ore
Sear
ch externally-motivated
searchers
31%
framing the context
refining the requirements
FORMULATE REPRESENTATION
GATHER REQUIREMENTS
69%
13% 59% 28%
transactional
self-motivated searchers
navigational informational
Dur
ing
Sear
ch
Aft
er S
earc
h
step A
step B
TRANSACTION
step A
step B
DO NOTHING
search product /end product
ORGANIZE DISTRIBUTE
TAKE ACTION
28% 72%
Social Interactions
to public others
to proximate others
to self 15% 87% 2%
“evidence file”
SENSEMAKING
process
search FORAGING
Bef
ore
Sear
ch externally-motivated
searchers
31%
framing the context
refining the requirements
FORMULATE REPRESENTATION
GATHER REQUIREMENTS
69%
13% 59% 28%
transactional
self-motivated searchers
navigational informational
Dur
ing
Sear
ch
Aft
er S
earc
h
step A
step B
TRANSACTION
step A
step B
DO NOTHING
search product /end product
ORGANIZE DISTRIBUTE
TAKE ACTION
28% 72%
Social Interactions
to public others
to proximate others
to self 15% 87% 2%
“evidence file”
SENSEMAKING
process
search FORAGING
externally-motivated searchers
31%
framing the context
refining the requirements
FORMULATE REPRESENTATION
GATHER REQUIREMENTS
69%
13% 59% 28%
transactional
self-motivated searchers
navigational informational
step A
step B
TRANSACTION
step A
step B
DO NOTHING
search product /end product
ORGANIZE DISTRIBUTE
TAKE ACTION
28% 72%
Social Interactions
to public others
to proximate others
to self 15% 87% 2%
43% users engaged in pre-search social interactions.
150 reports of unique search experiences mapped to a canonical model of social search.
59% users engaged in post-search sharing.
Bef
ore
Sear
ch
Dur
ing
Sear
ch
Aft
er S
earc
h
3 types of search: informational search provides a compelling case for social search support.
reasons for interacting: thought others might be interested, to get feedback, out of obligation
reasons for interacting: to get advice, guidelines, feedback, or search tips
“evidence file”
SENSEMAKING
process
search FORAGING
externally-motivated searchers
31%
framing the context
refining the requirements
FORMULATE REPRESENTATION
GATHER REQUIREMENTS
69%
13% 59% 28%
transactional
self-motivated searchers
navigational informational
step A
step B
TRANSACTION
step A
step B
DO NOTHING
search product /end product
ORGANIZE DISTRIBUTE
TAKE ACTION
28% 72%
Social Interactions
to public others
to proximate others
to self 15% 87% 2%
• instant messaging (IM) to personal social connections near the search box
Bef
ore
Sear
ch
Dur
ing
Sear
ch
Aft
er S
earc
h
• tag clouds from domain experts • other users’ search trails (for feedback) • related search terms (for feedback)
Similar to: Glance; Smyth"
• sharing tools built-in to (search) site • collective tag clouds (for feedback)
Spartag.us"
Mr. Taggy"
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Image from: http://www.flickr.com/photos/ourcommon/480538715/
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 [email protected]
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