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China HCI Symposium 2010 March: Augmented Social Cognition Research from PARC, by Ed H. Chi

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This is the talk I gave in March 2010 at the China-HCI symposium that outlines ASC research at PARC on Web2.0 systems.

This is the talk I gave in March 2010 at the China-HCI symposium that outlines ASC research at PARC on Web2.0 systems.

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  • 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 group ABSTRACT: 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 and metadata. 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.
  • 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, <pause>, you guessed it, about our own George W .
  • In the enterprise, these have become the standard set of Web 2.0 tools in practice. They have several benefits – they can be set up by end users without needing IT, they have familiar UIs from consumer versions, And in terms of knowledge sharing, an important advantage these tools have over traditional KM systems is that knowledge can be captured and archived through the act of communication without requiring extra work by users. These tools will become increasingly important in the office as younger people enter the workforce and expect to be able to use them.
  • Paste controversial tag picture here Figure 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
  • 1m
  • Year 2013, <10k new articles per month is expected to be added. Knowledge does not stop growing!
  • Therefore, this is the model that we suggest!
  • 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.
  • 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.
  • In the enterprise, these have become the standard set of Web 2.0 tools in practice. They have several benefits – they can be set up by end users without needing IT, they have familiar UIs from consumer versions, And in terms of knowledge sharing, an important advantage these tools have over traditional KM systems is that knowledge can be captured and archived through the act of communication without requiring extra work by users. These tools will become increasingly important in the office as younger people enter the workforce and expect to be able to use them.
  • 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.
  • Posing the right questions is half of the work.
  • Voting systems: faddishness of information, social dashboards Col info. Structures: explicit social networks Collaborative Co-creation
  • Voting systems: faddishness of information, social dashboards Col info. Structures: explicit social networks Collaborative creation
  • In other words, a person did not see both a high-trust and low-trust visualization for the same page.
  • Remember, our goal is not to see whether they noticed the visualization or not, but how much impact it could have.
  • So we ran two parts of the experiment, here are the combined results. Notice two things: Huge effect No significant interactions – trust was impacted Bi-directional change in trust: increase over baseline and decrease below baseline
  • Informational search – ambiguity in query – where social search has most power
  • 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.
  • 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.
  • Transcript

    • 1. Enhancing the Social Web through Augmented Social Cognition Research Ed H. Chi 紀懷新 , Area Manager Peter Pirolli, Lichan Hong, Bongwon Suh, Gregorio Convertino, Les Nelson, Rowan Nairn Augmented Social Cognition Area Palo Alto Research Center Interns: Sanjay Kairam, Jilin Chen, Michael Bernstein Alumni: Raluca Budiu, Bryan Pendleton, Niki Kittur, Todd Mytkowicz, Terrell Russell, Brynn Evans, Bryan Chan, KMRC students Image from: http://www.flickr.com/photos/ourcommon/480538715/ 2010-03-15 Ed H. Chi ASC Overview
    • 2. PARC Overview
      • Interdisciplinary research center
      • Founded in 1970
      • Spun out of Xerox in 2002
      • Business model:
        • Contract research
        • Licensing
        • Joint ventures
        • Spinoffs
      2010-03-15 Ed H. Chi ASC Overview
    • 3. INNOVATION
      • chartered to create the architecture of information & the office of the future
      • invented distributed personal computing
      • established Xerox’s laser printing business
      • created the foundation for the digital revolution
      Graphical User Interface Laser Printing Ethernet Bit-mapped Displays Distributed File Systems Page Description Languages First Commercial Mouse Object-oriented Programming WYSIWYG Editing Distributed Computing VLSI Design Methodologies Optical Storage Client/Server Architecture Device Independent Imaging Cedar Programming Language
    • 4.
      • 14 years of work in visualization and foraging
      • 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]
      • Visualization and Sensemaking
        • Visualization of Web Ecologies [CHI98]
        • Visualization Spreadsheets [Infovis97, Infovis99]
      2010-03-15 Ed H. Chi ASC Overview
    • 5. V. Bush’s Vision of Augmented Cognition
      • Problem:
        • Intellectual over-specialization
      • The Memex
      • Extend the powers of the human mind with technology
        • Individuals could attend to greater spans
        • Facile command of all recorded knowledge
        • Sharing of knowledge gained
      2010-03-15 Ed H. Chi ASC Overview
    • 6. Finding a Restaurant
      • Appropriate for the occasion
      2010-03-15 Ed H. Chi ASC Overview
    • 7. Heuristics 2010-03-15 Ed H. Chi ASC Overview Poor heuristic Good heuristic
    • 8. “ Hints” 2010-03-15 Ed H. Chi ASC Overview Solo Cooperative (“good hints”)
    • 9. Wikipedia Success is counter-intuitive “ 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 2010-03-15 Ed H. Chi ASC Overview
    • 10. Wikipedia 2010-03-15 Ed H. Chi ASC Overview
    • 11. 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
      2010-03-15 Ed H. Chi ASC Overview
    • 12. Research Methodology
      • 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
      Ed H. Chi ASC Overview 2010-03-15 Characterization Models Prototypes Evaluations
    • 13. 2010-03-15 Ed H. Chi ASC Overview Characterization Models Prototypes Evaluations
    • 14. Conflict/Coordination Effects in Wikipedia [Kittur et al., CHI2007] 2010-03-15 (joint work with Niki Kittur, Bongwon Suh, Bryan Pendleton) Ed H. Chi ASC Overview
    • 15. 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
      2010-03-15 Ed H. Chi ASC Overview
    • 16. Measure of controversy
      • “ Controversial” tag
      • Use # revisions tagged controversial
      2010-03-15 Ed H. Chi ASC Overview
    • 17. Page metrics
      • Possible metrics for identifying conflict in articles
      2010-03-15 Ed H. Chi ASC Overview 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 editors) Article
    • 18. Performance: Cross-validation
      • SVM Classifier, 5x cross-validation, R 2 = 0.897
      2010-03-15 Ed H. Chi ASC Overview
    • 19. Performance: Cross-validation
      • 5x cross-validation, R 2 = 0.897
      2010-03-15 Ed H. Chi ASC Overview
    • 20. Determinants of conflict
      •   Revisions (talk)
      •   Minor edits (talk)
      •   Unique editors (talk)
      •   Revisions (article)
      •   Unique editors (article)
      •   Anonymous edits (talk)
      •   Anonymous edits (article)
      • Highly weighted features of conflict model:
      2010-03-15 Ed H. Chi ASC Overview
    • 21. 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
      • Revert: Undoing one or more edits
        • Restored to a version that existed sometime previously.
        • Often used to fight vandalism
      • Force directed layout
        • Node: user, Edge: revert relationship
      2010-03-15 Ed H. Chi ASC Overview
    • 22. Opinions on Dokdo/Takeshima 2010-03-15 Ed H. Chi ASC Overview Group A Group B Group C Group D 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 Neutral or Unidentified 7 3 6 17
    • 23. Mediator Pattern - Terri Schiavo Mediators Sympathetic to parents Sympathetic to husband Anonymous (vandals/spammers) 2010-03-15 Ed H. Chi ASC Overview
    • 24. Ratio of Reverted Contribution Monthly Ratio of Reverted Edits 2010-03-15 Ed H. Chi ASC Overview
    • 25. 2010-03-15 Ed H. Chi ASC Overview Characterization Models Prototypes Evaluations
    • 26. Example: Modeling Wikipedia Growth Bongwon Suh, Gregorio Convertino, Ed H. Chi, Peter Pirolli 2010-03-15 Ed H. Chi ASC Overview Bongwon Suh, Gregorio Convertino, Ed H. Chi, Peter Pirolli. The Singularity is Not Near: Slowing Growth of Wikipedia. In Proc. of WikiSym 2009. Oct, 2009. Florida, USA
    • 27. Something happened in early 2007 Monthly Edits 2010-03-15 Ed H. Chi ASC Overview
    • 28. Slowing Growth in Global Activity Monthly Active Editors 2010-03-15 Ed H. Chi ASC Overview
    • 29. Earlier Exponential Growth Model
      • Edits beget edits
        • more number of previous edits, more number of new edits
      Growth rate of population Current population
        • Growth rate depends on
        • current population size N and
        • r = growth rate of the population
      2010-03-15 Ed H. Chi ASC Overview
    • 30. Logistic Growth Model
      • Ecological population growth model
        • r, growth rate of the population
        • K, carrying capacity (due to resource limitation)
      K 2010-03-15 Ed H. Chi ASC Overview
    • 31. Number of New Articles
      • Follows a logistic growth curve
      http://en.wikipedia.org/wiki/Wikipedia:Modelling_Wikipedia’s_growth New Article 2010-03-15 Ed H. Chi ASC Overview
    • 32. A Modified Logistic Model
      • Carrying Capacity as a function of time.
      K(t) 2010-03-15 Ed H. Chi ASC Overview
    • 33. Two Sides of Tagging
      • Encoding
      • Retrieval
      http://edge.org “ science research cognition” http://www.ted.com/index.php/speakers “ video people talks technology” 2010-03-15 Ed H. Chi ASC Overview
    • 34. Using Information Theory to Model Social Tagging [Ed H. Chi, Todd Mytkowicz, ACM Hypertext 2008] Topics Users Documents Decoding 2010-03-15 Ed H. Chi ASC Overview Concepts Tags T 1 …T n Encoding Noise
    • 35. H(Tag) shows saturation in tag usage 2010-03-15 Ed H. Chi ASC Overview
    • 36. H(Doc | Tag), browsability 2010-03-15 Ed H. Chi ASC Overview
    • 37. I ( Doc ; Tag ) Mutual Information 2010-03-15 Ed H. Chi ASC Overview Source: Hypertext 2008 study on del.icio.us (Chi & Mytkowicz)
    • 38. Raise in avg. tag per bookmark (note parallel the development in increasing # of query words) 2010-03-15 Ed H. Chi ASC Overview
    • 39. Understanding a new area… 2010-03-15 Characterization Models Prototypes Evaluations Ed H. Chi ASC Overview
    • 40. MrTaggy.com: social search browser with social bookmarks 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. 2010-03-15 Ed H. Chi ASC Overview
    • 41.
      • Synonyms
      • Misspellings
      • Morphologies
      • People use different tag words to express similar concepts.
      Social Tagging Creates Noise 2010-03-15 Ed H. Chi ASC Overview
    • 42. 2010-03-15 Ed H. Chi ASC Overview
    • 43. TagSearch: Use Semantic Analysis to Reduce Noise http://mrtaggy.com 2010-03-15 Ed H. Chi ASC Overview Guide Web Howto Tips Help Tools Tip Tricks Tutorial Tutorials Reference Semantic Similarity Graph
    • 44. MapReduce Implementation
      • Spreading Activation in a bi-graph
      • Computation over a very large data set
        • 150 Million+ bookmarks
      2010-03-15 Ed H. Chi ASC Overview Tags URLs P(URL|Tag) P(Tag|URL)
    • 45. TagSearch Architecture
      • MapReduce: months of computation to a single day
      • Development of novel scoring function
      2010-03-15 Ed H. Chi ASC Overview
    • 46. Understanding a new area… 2010-03-15 Characterization Models Prototypes Evaluations Ed H. Chi ASC Overview
    • 47. Baseline Interface
    • 48. Experiment Design
      • 2 interface x 3 task domain design
        • 2 Interface (between-subjects)
          • Exploratory vs. Baseline
        • 3 task domains (within-subjects)
          • Future Architecture, Global Warming, Web Mashups
      • 30 Subjects (22 male, 8 female)
        • Intermediate or advanced computer and web search skills
        • Half assigned Exploratory, half Baseline.
      • For each domain, single block with 3 task types:
        • Easy and Difficult Page Collection Task [6min each]
        • Summarization Task [12min]
        • Keyword Generation Task [2min]
    • 49. Procedure [2 hours]
      • Prior Knowledge Test
      • 1 st Task Domain
        • With easy and difficult page collection tasks, summarization and keyword generation task.
        • NASA cognitive load questionnaire
      • 2 nd Task Domain
        • Same battery of tasks and cognitive load questionaire
      • 3 rd Task Domain
      • Experimental Survey
    • 50. Experimental Evauation [Kammerer et al, CHI2009]
      • Exploratory interface users:
        • performed more queries,
        • took more time,
        • wrote better summaries (in 2/3 domains),
        • generated more relevant keywords (in 2/3 domains), and
        • had a higher cognitive load.
      • Suggestive of deeper engagement and better learning.
      • Some evidence of scaffolding for novices in the keyword generation and summarization tasks.
      2010-03-15 Ed H. Chi ASC Overview
    • 51. Model-Driven Research Methodology
      • 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
      Ed H. Chi ASC Overview 2010-03-15 Characterization Models Prototypes Evaluations
    • 52. Living Laboratory: Prototyping Social Applications on the Internet Create a Living Laboratory as a platform to develop, test, and market innovations [HCIC workshop 2009, HCII 2009, IEEE Computer Sep/2008] 2010-03-15 Ed H. Chi ASC Overview
    • 53. WikiDashboard for Wikipedia 2010-03-15 Ed H. Chi ASC Overview
    • 54. SparTag.us Social Reading Tool 2010-03-15 Ed H. Chi ASC Overview
    • 55. Topic-oriented browsing of your Twitter feed Browse over arbitrary periods of time Dashboard identifies topics that might be of interest
    • 56. Augmented Social Cognition
      • Mail2Tag
      Build on email practices To promote sharing across the organization email-based news sharing system where people ‘CC’ news to keyword tags Mail2Tag system
    • 57. Research Platform Strategy Extracts data in the form of tuples from applications, e.g. (user, tag, URL) (user, activity, object) Hadoop MapReduce, Pig, MySQL, Django, Java Social Data Mining Platform Pattern Operators, e.g., Tag Normalization, LDA Clustering, Summarization, Voting Techniques… Recommendations Dashboard Expertise Identification Topic Identification
      • ASC is creating a plug-and-play platform to enable a number of applications in support of the Open Web Applications
      Combine with other applications to create full products … Core Advantage App Connectors App Connectors App Connectors App Connectors
    • 58. 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?
      2010-03-15 Ed H. Chi ASC Overview
    • 59. The Team 2010-03-15 Ed H. Chi ASC Overview
    • 60. Augmented Social Cognition: From Social Foraging to Social Sensemaking 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_address]
      2010-03-15 Ed H. Chi ASC Overview
    • 61. 2010-03-15 Ed H. Chi ASC Overview
    • 62. Augmented Social Cognition 2010-03-15 Ed H. Chi ASC Overview Higher Productivity via Collective Intelligence Intelligence that emerges from the collaboration and competition of many individuals
      • Foundation:
      • Understanding of human cognition and behavior
      • Data mining of social data
      • Modeling of consensus-driven decision-making
      • Generic benefits:
      • Greater trust
      • Better decision-making
      • Useful sharing of info
      • Auto-organization thru social data
      Collective Intelligence search sharing foraging
      • TagSearch: Mining social data for automatic data clustering and organization:
        • Better organization via user-assigned tags
        • Better UI for browsing interesting contents
        • Recommendation instead of just search
      • Social Transparency create trust and attribution:
        • Increase participation via attribution
        • Increase credibility and trust with community feedback
        • Reduce wiki risks
      • SparTag.us: sharing of interesting contents:
        • A notebook that automatically organizes your reading
        • Social sharing of important and interesting tidbits
        • Viral sharing of highlighted and tagged paragraphs
    • 63. 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
      2010-03-15 Ed H. Chi ASC Overview
    • 64. Middle of the spectrum
      • Systems that evolve structures that can be used to organize information.
      • Del.icio.us
      • Flickr
      • YouTube
      • Friendster
      2010-03-15 Ed H. Chi ASC Overview
    • 65. 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
      2010-03-15 Ed H. Chi ASC Overview
    • 66. A way to think about these systems Voting systems Collaborative Co-Creation Col. Information Structures 2010-03-15 Ed H. Chi ASC Overview Naver Heavier collaboration Digg.com Wikipedia Slashdot eHow.com Del.icio.us IBM dogear PageRank Flickr
    • 67. Layers of Models Needed Voting systems Collaborative Co-Creation Col. Information Structures
      • Understanding of micro-economics
      • of foraging [PARC]
      • Personal vs. group [Huberman, Adamic]
      • Wisdom of Crowd [Surowieki]
      • Information cascades [Anderson and Holt]
      • Understanding of conflicts and coordination
      • Wikipedia coordination costs [PARC]
      • Invisible Colleges [Sandstrom]
      • Interference effects [Pirolli]
      • Co-laboratories [Olson and Olson]
      • Community networks / Col. Problem solving [Carroll]
      • Understanding of info and social networks
      • Tag network analysis [PARC, Golder, Yahoo]
      • Structural holes (info brokerage) [Burt]
      • Network constraints and structure [various]
      • Semantic of semiotic structures / words [IR, LSA]
      2010-03-15 Ed H. Chi ASC Overview Naver Heavier collaboration Digg.com Wikipedia Slashdot eHow.com Del.icio.us IBM dogear PageRank Flickr
    • 68. WikiDashboard: Social Transparency for Wikipedia 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. 2010-03-15 Ed H. Chi ASC Overview
    • 69. 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
      2010-03-15 Ed H. Chi ASC Overview
    • 70. Top Editor - Wasted Time R 2010-03-15 Ed H. Chi ASC Overview
    • 71. Subprime Mortgage Crisis 2010-03-15 Ed H. Chi ASC Overview
    • 72. 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 (?)
      2010-03-15 Ed H. Chi ASC Overview
    • 73. Experimental Evaluation Design
      • 3 x 2 x 2 design
      Controversial Uncontroversial High quality Low quality
      • Visualization
      • High stability
      • Low stability
      • Baseline (none)
      2010-03-15 Ed H. Chi ASC Overview Abortion George Bush Volcano Shark Pro-life feminism Scientology and celebrities Disk defragmenter Beeswax
    • 74. Example: High trust visualization 2010-03-15 Ed H. Chi ASC Overview
    • 75. Example: Low trust visualization 2010-03-15 Ed H. Chi ASC Overview
    • 76. Method
      • Users recruited via Amazon’s Mechanical Turk
        • 253 participants
        • 673 ratings
        • 7 cents per rating
        • Kittur, Chi, & Suh, CHI 2008: Crowdsourcing user studies
      • To ensure salience and valid answers, participants answered:
        • In what time period was this article the least stable?
        • How stable has this article been for the last month?
        • Who was the last editor?
        • How trustworthy do you consider the above editor?
      2010-03-15 Ed H. Chi ASC Overview
    • 77. Results
      • Significant effect of visualization
        • High > low, p < .001
      • Both positive and negative effects
        • High > baseline, p < .001
        • Low > baseline, p < .01
      • No effect of article uncertainty
        • No interaction of visualization with either quality or controversy
        • Robust across conditions
      2010-03-15 Ed H. Chi ASC Overview
    • 78. Social Search Survey [Brynn Evans, Ed H. Chi, CSCW2008]
      • Help understand the importance of:
        • social cues and information exchanges
        • vocabulary problems
        • distribution and organization
      2010-03-15 Ed H. Chi ASC Overview
    • 79. TagSearch Exploratory Focus 3 kinds of search 2010-03-15 Ed H. Chi ASC Overview navigational transactional 28% 13% You know what you want and where it is You know what you want to do Existing search engines are OK informational 59% You roughly know what you want but don’t know how to find it Difficult for existing search engines Opportunity
    • 80. SparTag.us: Social Paragraph-level Tagging 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 . 2010-03-15 Ed H. Chi ASC Overview
    • 81. Lowering Participation / Interaction Costs
      • Interaction costs determine number of people who participate
      • Surplus of attention & motivation at small transaction costs
      • Therefore …
      • Important to keep interaction costs low
      Cost of participation # People willing to produce for “free” 2010-03-15 Ed H. Chi ASC Overview
    • 82. SparTag.us
      • In situ tagging while reading
        • No new window
        • Clicking vs typing
      • Tagging + highlighting
      2010-03-15 Ed H. Chi ASC Overview
    • 83. 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
      2010-03-15 Ed H. Chi ASC Overview
    • 84. Duplicate Content via Paragraph Fingerprinting [Hong and Chi, CHI2009] 2010-03-15 Ed H. Chi ASC Overview
    • 85. My Reading Notebook 2010-03-15 Ed H. Chi ASC Overview
    • 86. Social Sharing friend’s tags my tags my highlights friend’s highlights 2010-03-15 Ed H. Chi ASC Overview
    • 87. Importance Indicator 2010-03-15 Ed H. Chi ASC Overview recall first-visit
    • 88. Experimental Evaluation: Significant Learning Gain N=18 SparTag.us + Friend superior to both individual conditions No difference between the two controls [Nelson et al., CHI2009] 2010-03-15 Ed H. Chi ASC Overview Without SparTag.us (WS) SparTag.us Only (SO) SparTag.us With A Friend (SF) SF group, M=0.46, SD=0.22 SO group, M=0.13, SD=0.32 WS group, M=0.27, SD=0.23