ASC Research given at the PARC Forum on 2008-05-01
Upcoming SlideShare
Loading in...5
×
 

ASC Research given at the PARC Forum on 2008-05-01

on

  • 1,749 views

This is the slide set for introducing ASC research given at the PARC forum on May 1, 2008.

This is the slide set for introducing ASC research given at the PARC forum on May 1, 2008.

Statistics

Views

Total Views
1,749
Views on SlideShare
1,747
Embed Views
2

Actions

Likes
1
Downloads
18
Comments
0

1 Embed 2

http://www.slideshare.net 2

Accessibility

Categories

Upload Details

Uploaded via as Adobe PDF

Usage Rights

© All Rights Reserved

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Processing…
Post Comment
Edit your comment
  • PARC FORUM *this week*:Thursday May 1, 4:00 – 5:00 pm, George E. Pake Auditorium at Palo Alto Research Center (www.parc.com/directions) TITLE: \"Enhancing the Social Web through Augmented Social Cognition research\"SPEAKER: Ed Chi, PARC Augmented Social Cognition groupABSTRACT: We are experiencing the new Social Web, where people share, communicate, commiserate, and conflict with each other. As evidenced by Wikipedia and del.icio.us, Web 2.0 environments are turning people into social information foragers and sharers. Users interact to resolve conflicts and jointly make sense of topic areas from “Obama vs. Clinton” to “Islam.”PARC‘s Augmented Social Cognition researchers -- who come from cognitive psychology, computer science, HCI, sociology, and other disciplines -- focus on understanding how to “enhance a group of people’s ability to remember, think, and reason”. Through Web 2.0 systems like social tagging, blogs, Wikis, and more, we can finally study, in detail, these types of enhancements on a very large scale.In this Forum, we summarize recent PARC work and early findings on: (1) how conflict and coordination have played out in Wikipedia, and how social transparency might affect reader trust; (2) how decreasing interaction costs might change participation in social tagging systems; and (3) how computation can help organize user-generated content andmetadata.ABOUT THE SPEAKER: Ed H. Chi is a senior research scientist and area manager of PARC's Augmented Social Cognition group. His previous work includes understanding Information Scent (how users navigate and make sense of information environments like the Web), as well as developing information visualizations such as the \"Spreadsheet for Visualization\" (which allows users to explore data through a spreadsheet metaphor where each cell holds an entire data set with a full-fledged visualization). He has also worked on computational molecular biology, ubiquitous computing systems, and recommendation and personalized search engines. Ed has over 19 patents and has been conducting research on user interface software systems since 1993. He has been quoted in the Economist, Time Magazine, LA Times, Slate, and the Associated Press. Ed completed his B.S., M.S., and Ph.D. degrees from the University of Minnesota between 1992 and 1999. In his spare time, he is an avid Taekwondo black belt, photographer, and snowboarder. ***************************************************This is the final talk in our \"Going Beyond Web 2.0\" speaker series. Previous talks in this series, as well as other recent Forum talks, are available online at www.parc.com/forums.**************************************************To subscribe to future PARC Forum announcements and/or our bimonthly e-newsletter, please visit: www.parc.com/subscriptions.To unsubscribe from Forum announcements, please send an e-mail to info@parc.com specifying the e-mail address you'd like to have removed.
  • Making sense of this area
  • Voting systems: faddishness of information, social dashboardsCol info. Structures: explicit social networksCollaborative Co-creation
  • Voting systems: faddishness of information, social dashboardsCol info. Structures: explicit social networksCollaborative creation
  • This clip is from a comedy show, but it raises a serious question as well. What does happen when you have millions of people with different viewpoints all editing the same content? Well, you get a lot of conflict. I’m going to briefly go through an example of conflict that occurred on one of the most heavily edited pages in Wikipedia, which is, , you guessed it, about our own George W.
  • This main article page is all a casual browser would see when visiting the site. However, for each article in Wikipedia there is a corresponding talk page
  • ...and it’s this talk page where much of the discussion and conflict occurs. For example, here’s a conflict started by user Duke53, who believes that the age at which George W received a DUI is an important fact that belongs in the main article. Meanwhile others argue that the information is unencyclopedic, and that Duke53’s continued re-adding of it constitutes vandalism. So even this seemingly small issue has sparked a major controversy, which continues well past what you see on this page. In fact, in Wikipedia each user has their own user page, and the argument spills over to Duke53’s user talk page
  • here it turns into a discussion of the policies for conflict resolution and what is considered vandalism. Wikipedia has a large number of pages dedicated just to policies and procedures such as conflict resolution,
  • which themselves are fluid and changing over time. In fact, some of the most heated debates take place on the talk pages
  • for these policies. These policies and procedures are so important that an admin we surveyed said:
  • So what we see is that the proportion of edits going to article pages is decreasing to around 70% of all edits, meaning there is less direct work being done.
  • But vandalism still only accounts for about 1% of all edits
  • Paste controversial tag picture hereFigure depicting CRC
  • Selected a set of page metrics which we could scale to compute across large numbers of pages.
  • This graph is just running the model on the list of controversial topics, it is not x-validation. It’s R-square is actually 0.897.
  • This graph is just running the model on the list of controversial topics, it is not x-validation. It’s R-square is actually 0.897.
  • Especially interesting: unique editors DECREASE conflict. Anonymous edits are bad when on the discussion page but not the article page.Change to 1,2,3,4... and up/down arrows
  • 46
  • TALK ABOUT MODEL IMPLICATIONS FOR SEARCHHow do we evaluate a search engine?
  • Add idea of black box and tell story from the side of the box getting tags
  • 4 possibilities:
  • Vocabulary saturation!shows a marked increase in the entropy of the tag distribution H(T) up until week 75 (mid-2005) at which point the entropy measure hits a plateau. Since the total number of tags keeps increasing, tag entropy can only stay constant in the plateau by having the tag probability distribution become less uniform. What this suggests is that users are having a hard time coming up with “unique” tags. That is to say, a user is more likely to add a tag to del.icio.us that is already popular in the system, than to add a tag that is relatively obscure.
  • What’s perhaps the most telling data of all is the entropy of documents conditional on tags, H(D|T), which is increasing rapidly (see Figure 4). What this means is that, even after knowing completely the value of tags, the entropy of the document is still increasing. Conditional Entropy asks the question: “Given that I know a set of tags, how much uncertainty regarding the document set that I was referencing with those tags remains?” This measure gives us a method for analyzing how useful a set of tags is at describing a document set. The fact that this curve is strictly increasing suggests that the specificity of any given tag is decreasing. That is to say, as a navigation aid, tags are becoming harder and harder to use. We are moving closer and closer to the proverbial “needle in a haystack” where any single tag references too many documents to be considered useful.
  • Figure 6 shows the number of tags per bookmark over time. The trend is clearly increasing, complementing the increase in navigation difficulty.
  • What is the valuable problem addressed by this research program? What is the target (user, company, application, market), what is our place in the value chain, and what is the business model to bring value to the target and PARC?
  • As you can tell from my demo, what is being tagged are paragraphs. This is based on our intuition that although there are cases where it makes sense to tag the whole document, there are many other cases where the interesting nuggets of information are at the sub-document level, for example, entities, facts, concepts, and paragraphs. Our implementation focuses on paragraphs for now. The key idea is that we compute a unique fingerprint for each paragraph that we encounter. Currently, we use Secure Hash Algorithm to compute the paragraph fingerprint. We are exploring other ways in the future. This simple idea of paragraph fingerprint has also been picked up by other projects in UbiDocs.
  • Here is an example of duplicate content. Here we have a story at Forbes.com which is about the recent tragedy happening in Minnesota and I annotated part of the story. Here on a different web site, the same story appears and my annotations show up too.
  • As I browse the web and annotate the pages, one of the things that SparTag.us automatically created for me is a notebook which contains all the paragraphs that I have annotated. Here it shows when I annotated this paragraph. Here is an option that allows me to make my annotations on this paragraph become private. Here are the URLs that I have visited and contain this paragraph. And I can search my notebook against the tags that I specified, the text that I highlighted, the text of the paragraphs that I annotated, or the URLs. By the way, this last one was suggested by Prateek who was a subject in our last user study. And here is a tag cloud which is really a representation of what kind of keywords I have using as tags.
  • The way that we support social sharing is through a simple user interface like this. Here I designate myself as a fan of Ed, which means that I can see his annotations. When I go to this web page, I see that Ed has been here before and decided to leave some annotations. Of course, I can highlight or tag this paragraph too. Now, if I don’t want to be Ed’s fan anymore, I can remove his name from my friend list. And his annotations disappear too. And because this is done in AJAX, there is no need to reload the page.73
  • A nice thing about SparTag.us is that when you come to a web page, it sort of tells you what may be interesting to pay attention to. Here it reminds me that these are two paragraphs that I have annotated. Here I see that Ed has annotated this paragraph.
  • There are really two facets of tagging. The first is encoding: when you encounter a document, have read or skimmed it and have to generate a few words that describe it. The second side of tagging is retrieval: you find a new document that has several tags attached to it, and you read those tags and the document. The tags may give you an idea about what the document is about.I am going to come back to this distinction later.
  • Posing the right questions is half of the work.

ASC Research given at the PARC Forum on 2008-05-01 ASC Research given at the PARC Forum on 2008-05-01 Presentation Transcript

  • Ed
H.
Chi
 Augmented
Social
Cognition
Area
 Palo
Alto
Research
Center
 Peter
Pirolli,
Lichan
Hong,
Bongwon
Suh,
Les
Nelson,
Rowan
Nairn
 Alumni:
Raluca
Budiu,
Bryan
Pendleton,
Niki
Kittur,
Todd
Mytkowicz
 Image from: http://www.flickr.com/photos/ourcommon/480538715/ 2008-05-01 Ed H. Chi ASC Overview 1
  • 2008-05-01 Ed H. Chi ASC Overview 2
  • 2008-05-01 Ed H. Chi ASC Overview 3
  • 2008-05-01 Ed H. Chi ASC Overview 4
  • And
how
are
they
related?
 2008-05-01 Ed H. Chi ASC Overview 5
  • 12 years of work in foraging and sensemaking Information
Scent
   –  WUFIS
/
IUNIS
(Basic
scent
modeling
algorithms)
 [CHI2000,2001]
 –  Bloodhound
(Simulation
of
web
navigation)
[CHI2003]
 –  LumberJack
(Log
analysis
of
user
needs)
[CHI2002]
 Foraging
   –  ScentTrails
[TOCHI2003]
 –  ScentIndex
[CHI2004]
 –  ScentHighlight
[IUI2005]
 –  Visual
foraging
of
highlighted
text
[to
appear,
HCII]
 –  Proximal
Search
[to
be
published]
 Sensemaking
   –  Visualization
of
Web
Ecologies
[CHI98]
 –  Visualization
Spreadsheets
[Infovis97,
Infovis99]
 2008-05-01 Ed H. Chi ASC Overview 6
  • 2008-05-01 Ed H. Chi ASC Overview 7
  • Source: Starship Exeter Lessig http://en.wikipedia.org/wiki/Star_Trek_fan_productions 2008-05-01 Ed H. Chi ASC Overview 8
  • 2008-05-01 Ed H. Chi ASC Overview 9
  • 2008-05-01 Ed H. Chi ASC Overview 10
  • Groups
utilize
systems
to
   make
sense
and
share
 complex
topics
and
 materials.
 Wikipedia
(social
status)
   Slashdot
(karma
points)
   WikiHow.com
   Lostpedia.com
   2008-05-01 Ed H. Chi ASC Overview 11
  • Systems
that
evolve
structures
   that
can
be
used
to
organize
 information.
 Del.icio.us

   Flickr

   YouTube

   Friendster
   2008-05-01 Ed H. Chi ASC Overview 12
  • Counting
votes
   –  A
way
to
increase
signal‐to‐noise
ratio
 –  Information
faddishness
 Examples:
   –  Digg.com
 –  Most
bookmarked
items
on
del.icio.us
 –  Estimating
the
weight
of
an
ox
or
 temperature
of
a
room
 –  The
true
value
of
a
stock
 –  PageRank
or
Hub
/
Authority
algorithms
 2008-05-01 Ed H. Chi ASC Overview 13
  • Col. Information Collaborative Voting systems Structures Co-Creation Digg.com eHow.com Wikipedia IBM dogear PageRank Slashdot Naver Del.icio.us Flickr Heavier collaboration 2008-05-01 Ed H. Chi ASC Overview 14
  • Col. Information Collaborative Voting systems Structures Co-Creation Digg.com eHow.com Understanding of Understanding of info Understanding of Wikipedia micro-economics and social networks conflicts and IBM dogear PageRank coordination Slashdot Naver Del.icio.us Flickr •  of foraging [PARC] •  Tag network analysis [PARC, •  Wikipedia coordination Golder, Yahoo] costs [PARC] •  Personal vs. group •  Structural holes (info brokerage) Heavier [Huberman, Adamic] •  Invisible Colleges [Sandstrom] •  Wisdom of Crowd [Burt] collaboration effects [Pirolli] •  Interference •  Co-laboratories [Olson and [Surowieki] •  Network constraints and Olson] •  Information cascades structure [various] •  Community networks / Col. [Anderson and Holt] •  Semantic of semiotic structures / Problem solving [Carroll] words [IR, LSA] 2008-05-01 Ed H. Chi ASC Overview 15
  • 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.

 2008-05-01 Ed H. Chi ASC Overview 16
  • Characteriza*on
 Models
 Evalua*ons
 Prototypes
 2008-05-01 Ed H. Chi ASC Overview 17
  • Characteriza*on
 Models
 Evalua*ons
 Prototypes
 2008-05-01 Ed H. Chi ASC Overview 18
  • John
Tukey

 (not
a
direct
quote)
 2008-05-01 Ed H. Chi ASC Overview 19
  • 2008-05-01 Ed H. Chi ASC Overview 20
  • 2008-05-01 Ed H. Chi ASC Overview 21
  • (joint
work
with
Niki
Kittur,
Bongwon
Suh,
 Bryan
Pendleton)
 Aniket
Kittur,
Bongwon
Suh,
Bryan
Pendleton,
Ed
H.
Chi.
He
Says,
She
 Says:
Conflict
and
Coordination
in
Wikipedia.
In
Proc.
of
ACM
Conference
 on
Human
Factors
in
Computing
Systems
(CHI2007),
pp.
453‐‐462,
April
 2007.
ACM
Press.
San
Jose,
CA.
 2008-05-01 Ed H. Chi ASC Overview 22
  • Wikipedia is the best thing ever. Anyone in the world can write anything they want about any subject, so you know you’re getting the best possible information.” – Steve Carell, The Office 2008-05-01 Ed H. Chi ASC Overview 23
  • Understanding
coordination
costs
is
vital
for
long‐term
   viability
of
collaborative
information
environment
 Data:
   –  Entire
dump
on
July
2,
2006
 –  58
million
revisions
 –  4.7
million
wiki
pages
 –  2.4
million
article
pages
 –  800
gigabytes
 2008-05-01 Ed H. Chi ASC Overview 24
  • 2008-05-01 Ed H. Chi ASC Overview 25
  • 2008-05-01 Ed H. Chi ASC Overview 26
  • 2008-05-01 Ed H. Chi ASC Overview 27
  • 2008-05-01 Ed H. Chi ASC Overview 28
  • 2008-05-01 Ed H. Chi ASC Overview 29
  • source: xkcd 2008-05-01 Ed H. Chi ASC Overview 30
  • Decrease
in
proportion
of
edits
to
article
page
   1 0.95 0.9 70% 0.85 Edit proportion 0.8 0.75 0.7 0.65 0.6 0.55 0.5 2001 2002 2003 2004 2005 2006 2008-05-01 Ed H. Chi ASC Overview 31
  • Increase
in
proportion
of
edits
to
user
talk
   0.2 8% 0.18 0.16 0.14 Edit Proportion 0.12 0.1 0.08 0.06 0.04 0.02 0 2001 2002 2003 2004 2005 2006 2008-05-01 Ed H. Chi ASC Overview 32
  • Increase
in
proportion
of
edits
to
user
talk
   Increase
in
proportion
of
edits
to
procedure
   0.2 11% 0.18 0.16 0.14 Edit proportion 0.12 0.1 0.08 0.06 0.04 0.02 0 2001 2002 2003 2004 2005 2006 2008-05-01 Ed H. Chi ASC Overview 33
  • Increase
in
proportion
of
edits
that
are
reverts
   0.2 7% 0.18 0.16 Edit proportion 0.14 0.12 0.1 0.08 0.06 0.04 0.02 0 2001 2002 2003 2004 2005 2006 2008-05-01 Ed H. Chi ASC Overview 34
  • Increase
in
proportion
of
edits
that
are
reverts
   Increase
in
proportion
of
edits
reverting
vandalism
   % Edits (marked Vandalism) 0.03 1-2% 0.025 Edit proportion 0.02 0.015 0.01 0.005 0 2001 2002 2003 2004 2005 2008-05-01 Ed H. Chi ASC Overview 35
  • Conflict
and
coordination
costs
are
growing
   –  Less
direct
work
(articles)
 +  More
indirect
work
(article
talk,
user,
procedure)
 +  More
maintenance
work
(reverts,
vandalism)
 100% Maintenance 95% 90% Percentage of total edits Other 85% 80% User Talk 75% User 70% Article Talk 65% Article 60% 2001 2002 2003 2004 2005 2006 2008-05-01 Ed H. Chi ASC Overview 36
  • Characteriza*on
 Models
 Evalua*ons
 Prototypes
 2008-05-01 Ed H. Chi ASC Overview 37
  • Conflict
is
growing
at
the
global
level,
and
we
have
   some
idea
about
where
it
is.
 But
what
defines
conflict
inside
Wikipedia?
   Build
a
characterization
model
of
article
conflict
   –  Identify
metrics
relevant
to
conflict
 –  Automatically
identify
high‐conflict
articles
 2008-05-01 Ed H. Chi ASC Overview 38
  • Controversial”
tag
   Use
#
revisions
tagged
controversial
   2008-05-01 Ed H. Chi ASC Overview 39
  • Possible
metrics
for
identifying
conflict
in
articles
   Metric type Page Type Revisions (#) Article, talk, article/talk Page length Article, talk, article/talk Unique editors Article, talk, article/talk Unique editors / revisions Article, talk Links from other articles Article, talk Links to other articles Article, talk Anonymous edits (#, %) Article, talk Administrator edits (#, %) Article, talk Minor edits (#, %) Article, talk Reverts (#, by unique Article editors) 2008-05-01 Ed H. Chi ASC Overview 40
  • 5x
cross‐validation,
R2
=
0.897
   10000 9000 Actual controversial revisions 8000 7000 6000 5000 4000 3000 2000 1000 0 0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000 Predicted controversial revisions 2008-05-01 Ed H. Chi ASC Overview 41
  • 5x
cross‐validation,
R2
=
0.897
   10000 9000 Actual controversial revisions 8000 7000 6000 5000 4000 3000 2000 1000 0 0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000 Predicted controversial revisions 2008-05-01 Ed H. Chi ASC Overview 42
  • Highly weighted features of conflict model:   Revisions
(talk)
  Minor
edits
(talk)
  Unique
editors
(talk)
  Revisions
(article)
  Unique
editors
(article)
  Anonymous
edits
(talk)
  Anonymous
edits
(article)
  2008-05-01 Ed H. Chi ASC Overview 43
  • (joint
work
with
Todd
Mytkowicz)
 Ed
H.
Chi,
Todd
Mytkowicz.
Understanding
the
Efficiency
of
Social
 Tagging
Systems
using
Information
Theory.
In
Proc.
of
ACM
Conference
 on
Hypertext
2008.
(to
appear).
ACM
Press,
2008.
Pittsburgh,
PA.
 2008-05-01 Ed H. Chi ASC Overview 44
  • 2008-05-01 Ed H. Chi ASC Overview 45
  • Topics
 Concepts
 Documents
 Users
 Noise
 Tags
 Decoding
 Encoding
 T1…Tn
 46 2008-05-01 Ed H. Chi ASC Overview
  • How
do
we
evaluate
a
tagging
system?
   Given
a
tag
vocabulary,
how
effective
is
it
in
describing
a
   set
of
URLs?

 Approach:
   –  Crawled
the
del.icio.us
bookmark
set
 –  Information
theory
provides
a
nice
framework
for
analysis
 2008-05-01 Ed H. Chi ASC Overview 47
  • Measures
the
uncertainty
about
a
particular
event
associated
with
a
   probability
distribution
 Thought
experiment:
drawing
colored
balls
out
of
a
box
   –  Maximum
when
p
is
uniform,
no
single
color
predominates
 –  Minimum
when
p
is
1,
only
one
color
 Entropy
measure
the
amount
of
information
associated
with
a
   drawn
ball.
 2008-05-01 Ed H. Chi ASC Overview 48
  • Entropy
increases
when
   –  (a)
total
number
of
events
x
increases
 –  (b)
distribution
on
X
becomes
more
uniform
 Conditional
Entropy,
H(Y|X)
   –  Measures
how
much
entropy
a
random
variable
Y
has
 remaining
if
we
have
already
learned
completely
the
value
 of
a
second
variable
X.
 –  Can
be
understood
by
thinking
about
the
joint
entropy
 H(Y|X)
=
H(X,Y)
–
H(X)
 2008-03-28 ICWSM Poster 49
  • 2008-05-01 Ed H. Chi ASC Overview 50
  • 2008-05-01 Ed H. Chi ASC Overview 51
  • 2008-05-01 Ed H. Chi ASC Overview 52
  • Source: Hypertext 2008 study on del.icio.us (Chi & Mytkowicz) 2008-03-26 Ed H. Chi ASC Overview 53
  • Entropy
can
be
used
effectively
as
a
measure
for
social
   tagging
systems.
 As
a
map,
over
time,
social
tagging
systems
seems
to
   lose
their
ability
to
guide
users
efficiently.
 –  However,
there
are
ways
to
deal
with
this
pressure.
 2008-05-01 Ed H. Chi ASC Overview 54
  • Characteriza*on
 Models
 Evalua*ons
 Prototypes
 2008-05-01 Ed H. Chi ASC Overview 55
  • Create
a
Living
Laboratory
as
a
platform
to
 develop,
test,
and
market
innovations
 2008-05-01 Ed H. Chi ASC Overview 56
  • Joint
work
with

 Rowan
Nairn,
Peter
Lai,
Lichan
Hong,
Lawrence
Lee
 2008-05-01 Ed H. Chi ASC Overview 57
  • fitness
   Java,
AJAX
   Ireland
travel
   Web2.0
   Social
search
   Second
Life
   Su
   2008-05-01 Ed H. Chi ASC Overview 58
  • Joint
work
with

 Bongwon
Suh,
Aniket
Kittur,
Bryan
Pendleton
 Bongwon
Suh,
Ed
H.
Chi,
Aniket
Kittur,
Bryan
A.
Pendleton.
Lifting
the
 Veil:
Improving
Accountability
and
Social
Transparency
in
Wikipedia
with
 WikiDashboard.
In
Proceedings
of
the
ACM
Conference
on
Human‐factors
 in
Computing
Systems
(CHI2008).
ACM
Press,
2008.
Florence,
Italy.
 2008-05-01 Ed H. Chi ASC Overview 59
  • Factual
accuracy
   Motives
of
editors
   Uncertain
expertise
   Volatility
   Spotty
coverage
   Unproven/non‐independent
source
   2008-05-01 Ed H. Chi ASC Overview 60
  • Social
translucent
for
effective
communication
and
collaboration

   [Erickson
and
Kellogg
2002]
 –  Make
socially
significant
information
visible
and
salient
 –  Support
awareness
of
the
rules
and
constraints
 –  Accountability
for
actions
 Wikis
can
be
a
prime
candidate
   –  Every
edit
is
logged
and
retrievable
 –  WikiScanner.com:
analyze
anonymous
IP
edits
 –  WikiRage.com:
top
edits
 2008-05-01 Ed H. Chi ASC Overview 61
  • 2008-05-01 Ed H. Chi ASC Overview 62
  • 2008-05-01 Ed H. Chi ASC Overview 63
  • List
of
every
edits
that
a
user
made
   Let
readers
examine
each
individual
revision
for
validity,
which
is
hard
to
accomplish
   when
only
provided
with
aggregate
visual
summaries.
 2008-05-01 Ed H. Chi ASC Overview 64
  • Surfacing
hidden
social
context
to
users
   For
readers
   –  Any
incidents
in
the
past
e.g.
A
sudden
burst
of
edits?
 –  Who
are
the
editors?
 –  What
is
their
motivation
/
point
of
views
/
expertise
/
topics
of
 interest

 –  Help
them
judging
the
quality/trustworthiness/usefulness
of
an
 article
 For
writers
   –  Measure
expertise
/
contribution
/
reputation
 –  Motivate
them
to
be
more
active
/
responsible
(?)
 2008-05-01 Ed H. Chi ASC Overview 65
  • Joint
work
with
 Lichan
Hong,
Raluca
Budiu,
Les
Nelson,
Peter
Pirolli

 Lichan
Hong,
Ed
H.
Chi,
Raluca
Budiu,
Peter
Pirolli,
and
Les
Nelson.
SparTag.us:
A
Low
 Cost
Tagging
System
for
Foraging
of
Web
Content.
In
Proceedings
of
the
Advanced
 Visual
Interface
(AVI2008),
(to
appear).
ACM
Press,
2008.
 2008-05-01 Ed H. Chi ASC Overview 66
  • Interaction
costs
   # People willing to produce for “free” determine
number
of
 people
who
participate
 Surplus
of
attention
&
   motivation
at
small
 transaction
costs
 Therefore…
   Important
to
keep
   interaction
costs
low
 Cost of participation 2008-05-01 Ed H. Chi ASC Overview 67
  • In situ tagging while reading   –  No new window –  Clicking vs typing Tagging + highlighting   2008-05-01 Ed H. Chi ASC Overview 68
  • Intuition:
sub‐doc
nuggets
useful
   –  Entities,
facts,
concepts,
paragraphs
 Annotations
attached
to

paragraphs
   Portable
across
pages
and
other
contents
(e.g.
   Word
documents)
 –  Dynamic
pages
 –  Duplicate
content
 2008-05-01 Ed H. Chi ASC Overview 69
  • 2008-05-01 Ed H. Chi ASC Overview 70
  • 2008-05-01 Ed H. Chi ASC Overview 71
  • 2008-05-01 Ed H. Chi ASC Overview 72
  • 2008-05-01 Ed H. Chi ASC Overview 73
  • Characteriza*on
 Models
 Evalua*ons
 Prototypes
 2008-05-01 Ed H. Chi ASC Overview 74
  • Encoding
 Retrieval 
 “video

people

talks
technology”

 h?p://www.ted.com/index.php/speakers
 h?p://edge.org
 “science

research
cogni*on”
 75

  • 2008-05-01 Ed H. Chi ASC Overview 76
  • 2008-05-01 Ed H. Chi ASC Overview 77
  • Crowdsourcing
[collaborative
co‐creation]
   –  Is
there
a
wisdom
of
the
crowd
in
Wikipedia?
 Collective
Intelligence
[folksonomy]
   –  Are
social
tags
collectively
gathered
useful
for
organization
of
a
large
 document
collection?
 Collective
Averaging
[social
attention]

   –  Does
voting
systems
identify
the
best
quality
and
most
interesting
 information
for
that
community?
 Participation
Architecture
[AJAX]

   –  Does
lowering
the
interaction
cost
barrier
increase
participation
 productively?
 Expertise
finding
[social
networking]

   –  Does
getting
experts
through
social
network
gets
you
to
better
quality
 information
sooner?
 2008-05-01 Ed H. Chi ASC Overview 78
  • 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
   Image from: http://www.flickr.com/photos/ourcommon/480538715/ 2008-05-01 Ed H. Chi ASC Overview 79