Keynote talk given at the International Conference on Asia-Pacific Digital Libraries (ICADL) 2008. December, 2008 in Bali, Indonesia ICADL 2008 link here
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 talk, 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 and metadata.
Presentation made for the purpose of an academic assignment. Summarizes the concept of Web2.0, discusses its issues, case studies and perspective on its utilization.
Building Killer Communities And Taking Confluence SocialAtlassian
What's with all the hype around enterprise social computing? And how can Confluence be used to support collaborative applications that are social? This session breaks through the hype around social computing, discusses the practical benefits of being people-oriented, and explores approaches to use Confluence in a social context.
Customer Speakers: Guy Fraser of Adaptavist, Ali Ouni of KAPIT, Peter Reiser of SUN Microsystems
Key Takeaways:
* New social capabilities in Confluence 3.0
* Primer on enterprise social computing
* Approaches to make Confluence deployments social
Presentation made for the purpose of an academic assignment. Summarizes the concept of Web2.0, discusses its issues, case studies and perspective on its utilization.
Building Killer Communities And Taking Confluence SocialAtlassian
What's with all the hype around enterprise social computing? And how can Confluence be used to support collaborative applications that are social? This session breaks through the hype around social computing, discusses the practical benefits of being people-oriented, and explores approaches to use Confluence in a social context.
Customer Speakers: Guy Fraser of Adaptavist, Ali Ouni of KAPIT, Peter Reiser of SUN Microsystems
Key Takeaways:
* New social capabilities in Confluence 3.0
* Primer on enterprise social computing
* Approaches to make Confluence deployments social
2010-03-10 PARC Augmented Social Cognition Research OverviewEd Chi
This is an overview of the 3-year research works done at the Augmented Social Cognition research group at PARC.
See blog at:
http://asc-parc.blogspot.com
paper presented at 5th International Conference on INFORMATICS, EDUCATIONAL TECHNOLOGY AND NEW MEDIA IN EDUCATION - Sombor, Serbia,.
You can view the paper here:
http://www.scribd.com/doc/2413801/The-Role-of-delicious-in-education
March 29 - 30th 2008
Semantic Web research anno 2006:main streams, popular falacies, current statu...Frank van Harmelen
This keynote at the Cooperative Intelligent Agents Workshop was a good opportunity to give my view on the current state of Semantic Web research: what is it about, what is it not about, what has been achieved, what remains to be done. (Includes the now infamous slide "What's it like to be a machine")
Presentation at Télécom Bretagne on my thesis & related work (draws from EKAW talk). Part of a European Science foundation Exploratory Workshop on Measuring Evaluation managing online communities European virtual institute #OOC2013
'Living Laboratories': Rethinking Ecological Designs and Experimentation in H...Ed Chi
HCI have long moved beyond the evaluation setting of a single user sitting in front of a single desktop computer, yet many of our fundamentally held viewpoints about evaluation continues to be ruled by outdated biases derived from this legacy. We need to engage with real users in 'Living Laboratories', in which researchers either adopt or create functioning systems that are used in real settings. These new experimental platforms will greatly enable researchers to conduct evaluations that span many users, places, time, location, and social factors in ways that are unimaginable before.
A presentation on the educational implications of the Web 2.0. It is the latest version, I believe better worked out and clean.
Redondo Beach edition. May 2009.
2017 10-10 (netflix ml platform meetup) learning item and user representation...Ed Chi
Learning item and user representations with sparse data in recommender systems
Ed H. Chi
Google Inc.
Recommenders match users in a particular context with the best personalized items that they will engage with. The problem is that users have shifting item and topic preferences, and give sparse feedback over time (or no-feedback at all). Contexts shift from interaction-to-interaction at various time scales (seconds to minutes to days). Learning about users and items is hard because of noisy and sparse labels, and the user/item set changes rapidly and is large and long-tailed. Given the enormity of the problem, it is a wonder that we learn anything at all about our items and users.
In this talk, I will outline some research at Google to tackle the sparsity problem. First, I will summarize some work on focused learning, which suggests that learning about subsets of the data requires tuning the parameters for estimating the missing unobserved entries. Second, we utilize joint feature factorization to impute possible user affinity to freshly-uploaded items, and employ hashing-based techniques to perform extremely fast similarity scoring on a large item catalog, while controlling variance. This approach is currently serving a ~1TB model on production traffic using distributed TensorFlow Serving, demonstrating that our techniques work in practice. I will conclude with some remarks on possible future directions.
HCI Korea 2012 Keynote Talk on Model-Driven Research in Social ComputingEd Chi
Model-Driven Research in Social Computing
Research in Augmented Social Cognition is aimed at enhancing the ability of a group of people to remember, think, and reason. Our approach to creating this augmentation or enhancement is primarily model-driven. Our system developments are informed by models such as information scent, sensemaking, information theory, probabilistic models, and more recently, evolutionary dynamic models. These models have been used to understand a wide variety of user behaviors, from individuals interacting with social bookmark search in Delicious and MrTaggy.com to groups of people working on articles in Wikipedia. These models range in complexity from a simple set of assumptions to complex equations describing human and group behaviors.
By studying online social systems such as Google Plus, Twitter, Delicious, and Wikipedia, we further our understanding of how knowledge is constructed in a social context. In this talk, I will illustrate how a model-driven approach could help illuminate the path forward for research in social computing and community knowledge building.
More Related Content
Similar to Enhancing the Social Web through Augmented Social Cognition Research
2010-03-10 PARC Augmented Social Cognition Research OverviewEd Chi
This is an overview of the 3-year research works done at the Augmented Social Cognition research group at PARC.
See blog at:
http://asc-parc.blogspot.com
paper presented at 5th International Conference on INFORMATICS, EDUCATIONAL TECHNOLOGY AND NEW MEDIA IN EDUCATION - Sombor, Serbia,.
You can view the paper here:
http://www.scribd.com/doc/2413801/The-Role-of-delicious-in-education
March 29 - 30th 2008
Semantic Web research anno 2006:main streams, popular falacies, current statu...Frank van Harmelen
This keynote at the Cooperative Intelligent Agents Workshop was a good opportunity to give my view on the current state of Semantic Web research: what is it about, what is it not about, what has been achieved, what remains to be done. (Includes the now infamous slide "What's it like to be a machine")
Presentation at Télécom Bretagne on my thesis & related work (draws from EKAW talk). Part of a European Science foundation Exploratory Workshop on Measuring Evaluation managing online communities European virtual institute #OOC2013
'Living Laboratories': Rethinking Ecological Designs and Experimentation in H...Ed Chi
HCI have long moved beyond the evaluation setting of a single user sitting in front of a single desktop computer, yet many of our fundamentally held viewpoints about evaluation continues to be ruled by outdated biases derived from this legacy. We need to engage with real users in 'Living Laboratories', in which researchers either adopt or create functioning systems that are used in real settings. These new experimental platforms will greatly enable researchers to conduct evaluations that span many users, places, time, location, and social factors in ways that are unimaginable before.
A presentation on the educational implications of the Web 2.0. It is the latest version, I believe better worked out and clean.
Redondo Beach edition. May 2009.
2017 10-10 (netflix ml platform meetup) learning item and user representation...Ed Chi
Learning item and user representations with sparse data in recommender systems
Ed H. Chi
Google Inc.
Recommenders match users in a particular context with the best personalized items that they will engage with. The problem is that users have shifting item and topic preferences, and give sparse feedback over time (or no-feedback at all). Contexts shift from interaction-to-interaction at various time scales (seconds to minutes to days). Learning about users and items is hard because of noisy and sparse labels, and the user/item set changes rapidly and is large and long-tailed. Given the enormity of the problem, it is a wonder that we learn anything at all about our items and users.
In this talk, I will outline some research at Google to tackle the sparsity problem. First, I will summarize some work on focused learning, which suggests that learning about subsets of the data requires tuning the parameters for estimating the missing unobserved entries. Second, we utilize joint feature factorization to impute possible user affinity to freshly-uploaded items, and employ hashing-based techniques to perform extremely fast similarity scoring on a large item catalog, while controlling variance. This approach is currently serving a ~1TB model on production traffic using distributed TensorFlow Serving, demonstrating that our techniques work in practice. I will conclude with some remarks on possible future directions.
HCI Korea 2012 Keynote Talk on Model-Driven Research in Social ComputingEd Chi
Model-Driven Research in Social Computing
Research in Augmented Social Cognition is aimed at enhancing the ability of a group of people to remember, think, and reason. Our approach to creating this augmentation or enhancement is primarily model-driven. Our system developments are informed by models such as information scent, sensemaking, information theory, probabilistic models, and more recently, evolutionary dynamic models. These models have been used to understand a wide variety of user behaviors, from individuals interacting with social bookmark search in Delicious and MrTaggy.com to groups of people working on articles in Wikipedia. These models range in complexity from a simple set of assumptions to complex equations describing human and group behaviors.
By studying online social systems such as Google Plus, Twitter, Delicious, and Wikipedia, we further our understanding of how knowledge is constructed in a social context. In this talk, I will illustrate how a model-driven approach could help illuminate the path forward for research in social computing and community knowledge building.
Location and Language in Social Media (Stanford Mobi Social Invited Talk)Ed Chi
http://forum.stanford.edu/events/2012mobi.php
Title: Location and Language in Social Media
Ed H. Chi
Staff Research Scientist, Google Research
(work done at [Xerox] PARC)
Abstract:
Despite the widespread adoption of social media internationally,
little research has investigated the differences among users of
different languages. Moreover, we know relatively little about how
people reveal their location information. In this talk, I will
outline our recent characterization studies on how users of differing
geographical locations and languages use social media.
First, on geographical location: We found that 34% of users did not
provide real location information in Twitter, frequently incorporating
fake locations or sarcastic comments that can fool traditional
geographic information tools. We performed a simple machine learning
experiment to determine whether we can identify a user’s location by
only looking at what that user tweets.
Second, on language, Examining users of the top 10 languages, we
discovered cross-language differences in adoption of features such as
URLs, hashtags, mentions, replies, and retweets.
We discuss our work’s implications for research on large-scale social
systems and design of cross-cultural communication tools.
Homepage:
edchi.net
Speaker Bio:
Ed H. Chi is a Staff Research Scientist at Google. Until recently, he
was the Area Manager and a Principal Scientist at Palo Alto Research
Center's Augmented Social Cognition Group. He led the group in
understanding how Web2.0 and Social Computing systems help groups of
people to remember, think and reason. Ed completed his three degrees
(B.S., M.S., and Ph.D.) in 6.5 years from University of Minnesota, and
has been doing research on user interface software systems since 1993.
He has been featured and quoted in the press, including the Economist,
Time Magazine, LA Times, and the Associated Press.
With 20 patents and over 90 research articles, his most well-known
past project is the study of Information Scent --- understanding how
users navigate and understand the Web and information environments. He
also led a group of researchers at PARC to understand the underlying
mechanisms in online social systems such as Wikipedia and social
tagging sites. He has also worked on information visualization,
computational molecular biology, ubicomp, and recommendation/search
engines, and has won awards for both teaching and research. In his spare time, Ed is an avid Taekwondo martial artist, photographer, and
snowboarder.
Model-Driven Research in Social Computing
Abstract:
Research in Augmented Social Cognition is aimed at enhancing the ability of a group of people to remember, think, and reason. Our approach to creating this augmentation or enhancement is primarily model-driven. Our system developments are informed by models such as information scent, sensemaking, information theory, probabilistic models, and more recently, evolutionary dynamic models. These models have been used to understand a wide variety of user behaviors, from individuals interacting with social bookmark search in Delicious and MrTaggy.com to groups of people working on articles in Wikipedia. These models range in complexity from a simple set of assumptions to complex equations describing human and group behaviors.
By studying online social systems such as Google Plus, Twitter, Delicious, and Wikipedia, we further our understanding of how knowledge is constructed in a social context. In this talk, I will illustrate how a model-driven approach could help illuminate the path forward for research in social computing and community knowledge building
Bio: Ed H. Chi is a Staff Research Scientist at Google, working on the Google+ project. Very recently, Ed was the Area Manager and a Principal Scientist at Palo Alto Research Center's Augmented Social Cognition Group. He led the group in understanding how Web2.0 and Social Computing systems help groups of people to remember, think and reason. Ed completed his three degrees (B.S., M.S., and Ph.D.) in 6.5 years from University of Minnesota, and has been doing research on user interface software systems since 1993. He has been featured and quoted in the press, including the Economist, Time Magazine, LA Times, and the Associated Press.
With 20 patents and over 80 research articles, his most well-known past project is the study of Information Scent — understanding how users navigate and understand the Web and information environments. Most recently, he leads a group of researchers at PARC to understand the underlying mechanisms in online social systems such as Wikipedia and social tagging sites. He has also worked on information visualization, computational molecular biology, ubicomp, and recommendation/search engines. He has won awards for both teaching and research. In his spare time, Ed is an avid Taekwondo martial artist, photographer, and snowboarder.
CSCL 2011 Keynote on Social Computing and eLearningEd Chi
Ed H. Chi
Google Research (Work done at Xerox PARC)
CSCL2011 Keynote Abstract:
Our research in Augmented Social Cognition is aimed at enhancing the ability of a group of people to remember, think, and reason. Our approach to creating this augmentation or enhancement is primarily model-driven. Our system developments are informed by models such as information scent, sensemaking, information theory, probabilistic models, and more recently, evolutionary dynamic models. These models have been used to understand a wide variety of user behaviors, from individuals interacting with social bookmark search in Delicious and MrTaggy.com to groups of people working on articles in Wikipedia. These models range in complexity from a simple set of assumptions to complex equations describing human and group behaviors.
Indeed, increasingly, new social online resources such as social bookmarking sites and Wikis are becoming central in eLearning. By studying them, we further our understanding of how knowledge is constructed in a social context. In this talk, I will illustrate how a model-driven approach could help illuminate the path forward for social computing and social learning.
-----
Large Scale Social Analytics on Wikipedia, Delicious, and Twitter (presented ...Ed Chi
Ed H. Chi, Palo Alto Research Center
Large-Scale Social Analytics in Wikipedia, Delicious, and Twitter
Abstract
We will illustrate an analytical research approach in social computing. Our research in Augmented Social Cognition is aimed at enhancing the ability of a group of people to remember, think, and reason. The drive to build models and theories for social computing research should further our understanding of how network science, behavioral economics, and evolutionary theories could explain how social systems work. Here we will summarize the published research we conducted on large-scale social analytics in Wikipedia, Delicious, and Twitter, and point out how social analytics can help us understand the intricacies of large social systems.
About the Speaker
Ed H. Chi is area manager and principal scientist at Palo Alto Research Center's Augmented Social Cognition Group. He leads the group in understanding how Web2.0 and Social Computing systems help groups of people to remember, think and reason. Ed completed his three degrees (B.S., M.S., and Ph.D.) in 6.5 years from University of Minnesota, and has been doing research on user interface software systems since 1993. He has been featured and quoted in the press, such as the Economist, Time Magazine, LA Times, and the Associated Press. With 20 patents and over 70 research articles, he has won awards for both teaching and research. In his spare time, Ed is an avid Taekwondo martial artist, photographer, and snowboarder.
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ACM Hypertext 2010 Conference
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Model-driven Research for Augmenting Social Cognition
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Using Information Scent to Model Users in Web1.0 and Web2.0Ed Chi
This talk summarizes the work I have been doing on modeling user behavior on Web1.0 and Web2.0 systems in the last 13 years
Talk given at a workshop on Cognitive Modeling in Utrecht, Netherlands on March 20, 2010.
2010-02-22 Wikipedia MTurk Research talk given in Taiwan's Academica SinicaEd Chi
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Session Overview
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Enhancing the Social Web through Augmented Social Cognition Research
1. Enhancing the Social Web through Augmented
Social Cognition research
Ed H. Chi
Peter Pirolli, Lichan Hong, Bongwon Suh, Gregorio Convertino,
Les Nelson, Rowan Nairn
Augmented Social Cognition Area
Palo Alto Research Center
Interns: Terrell Russell, Brynn Evans, Bryan Chan, KMRC students
Alumni: Raluca Budiu, Bryan Pendleton, Niki Kittur, Todd Mytkowicz
2008-11-07 Ed H. Chi ASC Overview 1
Image from: http://www.flickr.com/photos/ourcommon/480538715/
2. FOUNDING VISION FOR PARC
Bold strategic investment
– Founded bysemiconductors, software, systems
– Xerox in 1970, recognition of the coming digital revolution
– Chartered the organization to create “The Office of the Future”
– Challenged researchers to become the “architects of information”
Unique multi‐disciplinary culture
– Physicists, electronics engineers, computer scientists…theory & practice
– Able to see problems and integrate solutions from multiple perspectives
2008-11-07 Ed H. Chi ASC Overview 2
3. 14 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]
Information Foraging
– ScentTrails [TOCHI2003]
– ScentIndex [CHI2004]
– ScentHighlight [IUI2005]
– Visual foraging of highlighted text [HCII]
Sensemaking
– Visualization of Web Ecologies [CHI98]
– Visualization Spreadsheets [Infovis97, Infovis99]
2008-11-07 Ed H. Chi ASC Overview 3
5. What is Wikipedia?
“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
2008-11-07 Ed H. Chi ASC Overview 5
7. High‐end of the collaboration spectrum
Groups utilize systems to
make sense and share
complex topics and
materials.
Wikipedia (social status)
Slashdot (karma points)
WikiHow.com
Lostpedia.com
2008-11-07 Ed H. Chi ASC Overview 7
8. Middle of the spectrum
Systems that evolve structures
that can be used to organize
information.
Del.icio.us
Flickr
YouTube
Friendster
2008-11-07 Ed H. Chi ASC Overview 8
9. Lightweight social processes
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
2008-11-07 Ed H. Chi ASC Overview 9
10. A way to think about these systems
Voting systems Col. Information Collaborative
Structures Co-Creation
Digg.com eHow.com
IBM dogear Wikipedia
PageRank
Del.icio.us Flickr Slashdot Naver
Heavier
collaboration
2008-11-07 Ed H. Chi ASC Overview 10
11. Layers of Models Needed
Voting systems Col. Information Collaborative
Structures Co-Creation
Digg.com
Understanding of eHow.com
Understanding of info Understanding of
micro-economics and social networks
IBM dogear Wikipedia
conflicts and
PageRank coordination
• of foraging [PARC] Del.icio.us Flickr
• Tag network analysis [PARC, Slashdot Naver
Golder, Yahoo] • Wikipedia coordination
• Personal vs. group costs [PARC]
[Huberman, Adamic] • Structural holes (info brokerage) Heavier
• Invisible Colleges [Sandstrom]
• Wisdom of Crowd [Burt] collaboration effects [Pirolli]
• Interference
[Surowieki] • Network constraints and • Co-laboratories [Olson and
• Information cascades structure [various] Olson]
• Community networks / Col.
[Anderson and Holt] • Semantic of semiotic structures /
Problem solving [Carroll]
words [IR, LSA]
2008-11-07 Ed H. Chi ASC Overview 11
12. Research Vision
Augmented Social Cognition
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
2008-11-07 Ed H. Chi ASC Overview 12
13. Understanding a new area…
Characteriza*on Models
Evalua*ons Prototypes
2008-11-07 Ed H. Chi ASC Overview 13
14. Characteriza*on Models
Evalua*ons Prototypes
2008-11-07 Ed H. Chi ASC Overview 14
15. Conflict/Coordination Effects in Wikipedia
[Kittur et al., CHI2007]
100%
95% Maintenance
90%
Percentage of total edits
Other
85%
80%
User Talk
75%
User
70%
Article Talk
65%
Article
60%
2001 2002 2003 2004 2005 2006
(joint work with Niki Kittur, Bongwon Suh, Bryan Pendleton)
2008-11-07 Ed H. Chi ASC Overview 15
16. Conflict in Wikipedia
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
2008-11-07 Ed H. Chi ASC Overview 16
17. Measure of controversy
“Controversial” tag
Use # revisions tagged controversial
2008-11-07 Ed H. Chi ASC Overview 17
18. Page metrics
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)
2008-11-07 Ed H. Chi ASC Overview 18
23. Revert Graph [Suh et al., IEEE VAST 2007]
Research Goal
– How can we identify point of views between users?
– Group people share a common point of view
Using revert as proxy for disagreement between users
– Revert edits: 3,711,638 6.3 % of total edits
– Due to vandalism: 577,643 0.99% of total edits (15.6% of reverts)
Force directed layout
– Node: user, Edge: revert relationship
2008-11-07 Ed H. Chi ASC Overview 23
24. Opinions on Dokdo/Takeshima
Group D
Group A
Group B Group C
Number of users in user group A B C Total
Users with Korean point of view 10 6 0 16
Users with Japanese point of view 1 8 7 16
2008-11-07 Neutral ASC Overview
Ed H. Chi or Unidentified 7 3 6 2417
25. Mediator Pattern ‐ Terri Schiavo
Anonymous
(vandals/spammers)
Sympathetic to husband
Mediators
Sympathetic to parents
2008-11-07 Ed H. Chi ASC Overview 25
34. Risks in Using Wikipedia [Denning et al. 2005]
Factual accuracy
Motives of editors
Uncertain expertise
Volatility
Spotty coverage
Unproven/non‐independent source
2008-11-07 Ed H. Chi ASC Overview 34
35. Social Dashboard
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
2008-11-07 Ed H. Chi ASC Overview 35
39. WikiDashboard
Surfacing hidden social context to users
For readers
– Any incidents in the past e.g. A sudden burst of edits?
– Who are the top 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 (?)
2008-11-07 Ed H. Chi ASC Overview 39
41. TagSearch http://mrtaggy.com
http://mrtaggy.com
Semantic Similarity Graph
Web
Tools
Reference
Guide
Howto
Tutorial
Tips
Help
Tip Tutorials
Tricks
2008-11-07 Ed H. Chi ASC Overview 41
43. Lowering Participation / Interaction
Costs
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
2008-11-07 Ed H. Chi ASC Overview 43
44. SparTag.us
In situ tagging while reading
– No new window
– Clicking vs typing
Tagging + highlighting
2008-11-07 Ed H. Chi ASC Overview 44
45. Paragraph Tagging
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
2008-11-07 Ed H. Chi ASC Overview 45
50. Two Sides of Tagging
Encoding Retrieval
“video people talks technology”
h>p://www.ted.com/index.php/speakers
h>p://edge.org
“science research cogni*on”
50
2008-11-07 Ed H. Chi ASC Overview 50
51. Augmented Social Cognition questions:
Crowdsourcing [collaborative co‐creation]
– Is there a wisdom of the crowd in Wikipedia?
– How does conflict drive content creation?
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 [interaction]
– 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?
2008-11-07 Ed H. Chi ASC Overview 51
53. Augmented Social Cognition:
From Social Foraging to Social Sensemaking
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
2008-11-07 Ed H. Chi ASC Overview 53
Image from: http://www.flickr.com/photos/ourcommon/480538715/