Nfais social discovery-v5
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NFAIS.org Miles Conrad Award Lecture, Social Discovery in an Information Abundant World
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Nfais social discovery-v5
- 1. Social Discovery in an Information Abundant World Ben Shneiderman ben@cs.umd.edu @benbendc Founding Director (1983-2000), Human-Computer Interaction Lab Professor, Dept. of Computer Science Member, Institute for Advanced Computer Studies Miles Conrad Award Lecture NFAIS, Feb. 28, 2011
- 2. Interdisciplinary research community - Computer Science & Info Studies - Psych, Socio, Poli Sci & MITH (www.cs.umd.edu/hcil)
- 4. Spotfire: Retinol’s role in embryos & vision
- 6. Fact-Finding Search NFAIS 2008
- 7. Discovery Process: Task Analysis Specific fact finding (known-item search) On what day was Barack Obama born? <Google succeeds> NFAIS 2008
- 8. Discovery Process: Task Analysis Specific fact finding (known-item search) On what day was Barack Obama born? <Google succeeds> Extended fact finding (vague query) What cities did John McCain live in since he became a Senator? Exploration of availability (vague result request) What genealogical information on Barack Obama is at the National Archives? NFAIS 2008
- 9. Discovery Process: Task Analysis Specific fact finding (known-item search) On what day was Barack Obama born? <Google succeeds> Extended fact finding (vague query) What cities did John McCain live in since he became a Senator? Exploration of availability (vague result request) What genealogical information on Barack Obama is at the National Archives? Open-ended browsing and problem analysis (hidden assumptions) How has John McCain’s position on the environment changed since 2001? Mismatch with metadata (requires exhaustive search) How has Barack Obama’s choice of clothing changed during his campaign? NFAIS 2008
- 10. Discovery Process: Task Analysis Specific fact finding (known-item search Extended fact finding (vague query Exploration of availability (vague result request Open-ended browsing and problem analysis (hidden assumptions Mismatch with metadata (requires exhaustive search NFAIS 2008
- 11. Discovery Process: Task Analysis Specific fact finding (known-item search Extended fact finding (vague query Exploration of availability (vague result request Open-ended browsing and problem analysis (hidden assumptions Mismatch with metadata (requires exhaustive search 1-minute Weeks & Months NFAIS 2008
- 12. Discovery Process: Design Challenges Enrich query formulation Expand result management Enable long-term effort Enhance collaboration Deal with special cases: - Legal, patent & medical searches require thoroughness - Proving non-existence is difficult - Outlier items can be critical - Bridging items can be critical NFAIS 2008
- 16. Wikipedia
- 19. http://ukwebfocus.wordpress.com/2010/06/09/if-social-discovery-is-beating-traditional-search/ Search and Rescue: How to Become Findable and Shareable in Social Media http://searchenginewatch.com/3639969 Social Referral Exceeds Solitary Search
- 26. Social Bookmarking: del.icio.us & connotea
- 27. Collaboration: Tagging & voting
- 28. www.hivegroup.com Treemap: Digg Overview
- 30. Blekko:
- 31. YAHOO! Answers
- 32. Wikia Answers
- 33. Quora Search question topics Read questions Read answers from named sources Rate & contribute
- 34. VIVOweb: Enabling Networking of Scientists
- 35. Biodiversity: Encyclopedia of Life eol.org
- 36. NASA Clickworkers: Crowdsource Science
- 38. Collaboratories: Discovery Communities Bos et al., Olson, Gary M., Zimmerman, Ann, & Bos, Nathan. (2008) Scientific Collaboration on the Internet . MIT Press.
- 39. Health, Healthcare & Wellness
- 40. Social Marketplace: Mechanical Turk
- 41. Social Challenge: Netflix Prize
- 42. Social Search/Discovery: Active & Passive
- 45. Social Discovery Social Discovery Create Capacity Seek Solution Individual Tag, Comment, Rate, Review, Summarize Ask questions, Offer challenge, Request collaboration, Seek experts Community Establish thesauri, Prepare catalogs, Aggregate knowledge, Organize resources Give answers, Respond to challenge, Discuss alternatives, Offer advice
- 46. ` All Users From Reader to Leader: Motivating Technology-Mediated Social Participation Preece & Shneiderman, AIS Trans. Human-Computer Interaction1 (1), 2009 aisel.aisnet.org/thci/vol1/iss1/5/ Reader Contributor Collaborator Leader
- 51. NodeXL: Network Overview for Discovery & Exploration in Excel www.codeplex.com/nodexl
- 52. NodeXL: Network Overview for Discovery & Exploration in Excel www.codeplex.com/nodexl
- 53. NodeXL: Import Dialogs www.codeplex.com/nodexl
- 54. Tweets at #WIN09 Conference: 2 groups
- 55. Tweets at #NFAIS11 Conference: Sunday Size by Betweenness Centrality
- 56. Tweets at #NFAIS11 Conference: Sunday
- 57. Tweets at #NFAIS11 Conference: Monday Size by Betweenness Centrality
- 58. Tweets at #NFAIS11 Conference: Monday Size by Betweenness Centrality
- 59. Tweets at #NFAIS11 Conference: Monday Size by Betweenness Centrality, Color by Cluster
- 60. Tweets at #NFAIS11 Conference: Monday Size by Followers, Color by Cluster
- 62. Tweets for #HCI: 2 groups
- 63. Kodak Twitter Community www.codeplex.com/nodexl/
- 64. Oil Spill Twitter Community www.codeplex.com/nodexl/
- 65. CHI2010 Twitter Community www.codeplex.com/nodexl/
- 67. NodeXL: HIPAA, F-R layout, Size by Followers www.codeplex.com/nodexl
- 68. NodeXL: HIPAA, H-K layout, extract smalls, size by Tweets www.codeplex.com/nodexl
- 69. NodeXL: HIPAA, H-K, Size by Betweenness Centrality www.codeplex.com/nodexl
- 70. NodeXL: HIPAA, H-K, Size by Betweenness Centrality www.codeplex.com/nodexl
- 71. NodeXL: HIPAA, H-K, Size by Betweenness Centrality www.codeplex.com/nodexl
- 72. NodeXL: HIPAA, H-K, Size by Betweenness Centrality www.codeplex.com/nodexl
- 73. Flickr clusters for “mouse” Computer Mickey Animal
- 74. Flickr networks
- 75. Analyzing Social Media Networks with NodeXL I. Getting Started with Analyzing Social Media Networks 1. Introduction to Social Media and Social Networks 2. Social media: New Technologies of Collaboration 3. Social Network Analysis II. NodeXL Tutorial: Learning by Doing 4. Layout, Visual Design & Labeling 5. Calculating & Visualizing Network Metrics 6. Preparing Data & Filtering 7. Clustering &Grouping III Social Media Network Analysis Case Studies 8. Email 9. Threaded Networks 10. Twitter 11. Facebook 12. WWW 13. Flickr 14. YouTube 15. Wiki Networks www.elsevier.com/wps/find/bookdescription.cws_home/723354/description
- 76. Social Media Research Foundation Researchers who want to - create open tools - generate & host open data - support open scholarship Map, measure & understand social media Support tool projects to collection, analyze & visualize social media data. smrfoundation.org
- 78. 28th Annual Symposium May 25-26, 2011 www.cs.umd.edu/hcil
- 79. Social Discovery Social Discovery in an Information Abundant World Ben Shneiderman ben@cs.umd.edu Twitter @benbendc University of Maryland, Founding Director (1983-2000), Human-Computer Interaction Lab Professor, Department of Computer Science, Member, Institute for Advanced Computer Studies Miles Conrad Award Lecture NFAIS, February 28, 2011 Social Discovery Create Capacity Seek Solution Individual Tag, Comment, Rate, Review, Summarize Ask questions, Offer challenge, Request collaboration, Seek experts Community Establish thesauri, Prepare catalogs, Aggregate knowledge, Organize resources Give answers, Discuss alternatives, Respond to challenge, Offer advice
Hinweis der Redaktion
- &quot;The IN Cell Analyzer automated microscope was used to identify proteins influencing the division of human cells. After the images were analyzed, quantitative results were transferred to Spotfire DecisionSite. This screen revealed the previously unknown involvement of the retinol binding protein RBP1 in cell cycle control.(Stubbs S, & Thomas N. 2006 Methods in Enzymology; 414:1-21.) Retinol a form of Vitamin A plays a crucial role in vision and during embryonic development&quot;
- Chapter 3, Figure 1 (page 6). A NodeXL social media network diagram of relationships among Twitter users mentioning the hashtag “#WIN09” used by attendees of a conference on Network Science at NYU in September 2009. Each user’s node is sized proportional to the number of tweets they have ever made to that date.
- Chapter 3, Figure 1 (page 6). A NodeXL social media network diagram of relationships among Twitter users mentioning the hashtag “#NFAIS111”
- Chapter 3, Figure 1 (page 6). A NodeXL social media network diagram of relationships among Twitter users mentioning the hashtag “#NFAIS11”
- Chapter 3, Figure 1 (page 6). A NodeXL social media network diagram of relationships among Twitter users mentioning the hashtag “#NFAIS11”
- Chapter 3, Figure 1 (page 6). A NodeXL social media network diagram of relationships among Twitter users mentioning the hashtag “#NFAIS11”
- Chapter 3, Figure 1 (page 6). A NodeXL social media network diagram of relationships among Twitter users mentioning the hashtag “#NFAIS11” used by attendees of a conference
- Chapter 3, Figure 1 (page 6). A NodeXL social media network diagram of relationships among Twitter users mentioning the hashtag “#NFAIS11” used by attendees of a conference
- Chapter 3, Figure 1 (page 6). A NodeXL social media network diagram of relationships among Twitter users mentioning the hashtag “#WIN09” used by attendees of a conference on Network Science at NYU in September 2009. Each user’s node is sized proportional to the number of tweets they have ever made to that date.
- Figure 13.20. NodeXL cluster visualization showing three Flickr tag clusters, each representing a different context for “mouse”. Figure 13.21. NodeXL display of Isolated clusters for three different contexts for the “mouse” tag in Flickr: mouse animal, computer mouse, and Mickey Mouse Disney character.
- Chapter 3, Figure 1 (page 6). A NodeXL social media network diagram of relationships among Twitter users mentioning the hashtag “#WIN09” used by attendees of a conference on Network Science at NYU in September 2009. Each user’s node is sized proportional to the number of tweets they have ever made to that date.