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20110128 connected action-node xl-sea of connections
20110128 connected action-node xl-sea of connections
20110128 connected action-node xl-sea of connections
20110128 connected action-node xl-sea of connections
20110128 connected action-node xl-sea of connections
20110128 connected action-node xl-sea of connections
20110128 connected action-node xl-sea of connections
20110128 connected action-node xl-sea of connections
20110128 connected action-node xl-sea of connections
20110128 connected action-node xl-sea of connections
20110128 connected action-node xl-sea of connections
20110128 connected action-node xl-sea of connections
20110128 connected action-node xl-sea of connections
20110128 connected action-node xl-sea of connections
20110128 connected action-node xl-sea of connections
20110128 connected action-node xl-sea of connections
20110128 connected action-node xl-sea of connections
20110128 connected action-node xl-sea of connections
20110128 connected action-node xl-sea of connections
20110128 connected action-node xl-sea of connections
20110128 connected action-node xl-sea of connections
20110128 connected action-node xl-sea of connections
20110128 connected action-node xl-sea of connections
20110128 connected action-node xl-sea of connections
20110128 connected action-node xl-sea of connections
20110128 connected action-node xl-sea of connections
20110128 connected action-node xl-sea of connections
20110128 connected action-node xl-sea of connections
20110128 connected action-node xl-sea of connections
20110128 connected action-node xl-sea of connections
20110128 connected action-node xl-sea of connections
20110128 connected action-node xl-sea of connections
20110128 connected action-node xl-sea of connections
20110128 connected action-node xl-sea of connections
20110128 connected action-node xl-sea of connections
20110128 connected action-node xl-sea of connections
20110128 connected action-node xl-sea of connections
20110128 connected action-node xl-sea of connections
20110128 connected action-node xl-sea of connections
20110128 connected action-node xl-sea of connections
20110128 connected action-node xl-sea of connections
20110128 connected action-node xl-sea of connections
20110128 connected action-node xl-sea of connections
20110128 connected action-node xl-sea of connections
20110128 connected action-node xl-sea of connections
20110128 connected action-node xl-sea of connections
20110128 connected action-node xl-sea of connections
20110128 connected action-node xl-sea of connections
20110128 connected action-node xl-sea of connections
20110128 connected action-node xl-sea of connections
20110128 connected action-node xl-sea of connections
20110128 connected action-node xl-sea of connections
20110128 connected action-node xl-sea of connections
20110128 connected action-node xl-sea of connections
20110128 connected action-node xl-sea of connections
20110128 connected action-node xl-sea of connections
20110128 connected action-node xl-sea of connections
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20110128 connected action-node xl-sea of connections

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Slides for the 28 January 2011 Presentation of "Finding direction in a sea of connection" at Hartnell College in Salinas, California, sponsored by the Community Foundation for Monterey County …

Slides for the 28 January 2011 Presentation of "Finding direction in a sea of connection" at Hartnell College in Salinas, California, sponsored by the Community Foundation for Monterey County (CFMCO.org)

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  • http://www.flickr.com/photos/library_of_congress/3295494976/sizes/o/in/photostream/
  • http://www.flickr.com/photos/amycgx/3119640267/
  • A tutorial on analyzing social media networks is available from: casci.umd.edu/NodeXL_TeachingDifferent positions within a network can be measured using network metrics.
  • 2010 - May - 7 - NodeXL - twitter global warming
  • 2010 - May - 7 - NodeXL - twitter climate change
  • Transcript

    • 1. Finding direction in a sea of connection:
      Mapping networks and
      Social media
      Marc A. Smith
      Chief Social ScientistConnected Action Consulting Group
      marc@connectedaction.net
      http://www.connectedaction.net
      http://www.codeplex.com/nodexl
      A project from the Social Media Research Foundation: http://www.smrfoundation.org
    • 2. About Me
      Introductions
      Marc A. Smith
      Chief Social Scientist
      Connected Action Consulting Group
      Marc@connectedaction.net
      http://www.connectedaction.net
      http://www.codeplex.com/nodexl
      http://www.twitter.com/marc_smith
      http://delicious.com/marc_smith/Paper
      http://www.flickr.com/photos/marc_smith
      http://www.facebook.com/marc.smith.sociologist
      http://www.linkedin.com/in/marcasmith
      http://www.slideshare.net/Marc_A_Smith
      http://www.smrfoundation.org
    • 3. About You
      Introductions
      Organization
      Interest in networks
      Technical skills
      Social media usage
      Data sets
      Questions you want networks to help answer
    • 4. http://www.flickr.com/photos/library_of_congress/3295494976/sizes/o/in/photostream/
    • 5. http://www.flickr.com/photos/amycgx/3119640267/
    • 6.
    • 7. Collaboration networks are social networks
    • 8. SNA 101
      • Node
      • 9. “actor” on which relationships act; 1-mode versus 2-mode networks
      • 10. Edge
      • 11. Relationship connecting nodes; can be directional
      • 12. Cohesive Sub-Group
      • 13. Well-connected group; clique; cluster
      • 14. Key Metrics
      • 15. Centrality (group or individual measure)
      • 16. Number of direct connections that individuals have with others in the group (usually look at incoming connections only)
      • 17. Measure at the individual node or group level
      • 18. Cohesion (group measure)
      • 19. Ease with which a network can connect
      • 20. Aggregate measure of shortest path between each node pair at network level reflects average distance
      • 21. Density (group measure)
      • 22. Robustness of the network
      • 23. Number of connections that exist in the group out of 100% possible
      • 24. Betweenness (individual measure)
      • 25. # shortest paths between each node pair that a node is on
      • 26. Measure at the individual node level
      • 27. Node roles
      • 28. Peripheral – below average centrality
      • 29. Central connector – above average centrality
      • 30. Broker – above average betweenness
      A
      B
      C
      A
      B
      D
      E
      D
      E
      G
      F
      C
      D
      H
      I
      E
    • 31. Location, Location, Location
    • 32. Network of connections among “SharePoint” mentioning Twitter users
      Position, Position, Position
    • 33. Most “between” people in the Network of connections among “SharePoint” Twitter users
    • 34. There are many kinds of ties….
      http://www.flickr.com/photos/stevendepolo/3254238329
    • 35. “Think Link”Nodes & Edges
      Is related to
      B
      A
      In and Out Degree
    • 36. “Think Link”Nodes & Edges
      Is related to
      Edits
      B
      A
      Shares membership
      Ties of different types
    • 37. “Think Link”Nodes & Edges
      Is related to
      Edits
      Person
      Document
      Shares membership
      Nodesof different types
    • 38. Collections of ConnectionsCentralities
      Degree
      Closeness
      Betweenness
      Eigenvector
      http://en.wikipedia.org/wiki/Centrality
    • 39. Each contains one or more social networks
      World Wide Web
    • 40. NodeXLNetwork Overview Discovery and Exploration add-in for Excel 2007/2010
      Heather has high betweenness
      Diane has high degree
      A minimal network can illustrate the ways different locations have different values for centrality and degree
    • 41. Social Networks
      History: from the dawn of time!
      Theory and method: 1934 ->
      Jacob L. Moreno
      http://en.wikipedia.org/wiki/Jacob_L._Moreno
    • 42. Social Network Theoryhttp://en.wikipedia.org/wiki/Social_network
      Central tenet
      Social structure emerges from
      the aggregate of relationships (ties)
      among members of a population
      Phenomena of interest
      Emergence of cliques and clusters
      from patterns of relationships
      Centrality (core), periphery (isolates),
      betweenness
      Methods
      Surveys, interviews, observations, log file analysis, computational analysis of matrices
      (Hampton &Wellman, 1999; Paolillo, 2001; Wellman, 2001)
      Source: Richards, W. (1986). The NEGOPY network analysis program. Burnaby, BC: Department of Communication, Simon Fraser University. pp.7-16
    • 43.
    • 44.
    • 45.
    • 46.
    • 47. Welser, Howard T., Eric Gleave, Danyel Fisher, and Marc Smith. 2007. Visualizing the Signatures of Social Roles in Online Discussion Groups.The Journal of Social Structure. 8(2).
      Experts and “Answer People”
      Discussion people, Topic setters
      Discussion starters, Topic setters
    • 48. Friends, foes, and fringe: norms and structure in political discussion networks. Proceedings of the 2006 International Conference on Digital Government Research.
      John Kelly, Danyel Fisher, and Marc Smith.
    • 49. Introduction to NodeXL
    • 50. NodeXL: Network Overview, Discovery and Exploration for Excel
      Leverage spreadsheet for storage of edge and vertex data
      http://www.codeplex.com/nodexl
    • 51. Social Media Research Foundation
      Open Tools, Open Data, Open Scholarship
    • 52. Social Media Research Foundationhttp://smrfoundation.org
    • 53. Now Available
    • 54. Communities in Cyberspace
    • 55. Import from multiple social media networksources
    • 56. http://www.youtube.com/watch?v=0M3T65Iw3Ac
      NodeXL Video
    • 57. NodeXL
      Free/Open Social Network Analysis add-in for Excel 2007 makes graph theory as easy as a bar chart, integrated analysis of social media sources.
      http://nodexl.codeplex.com
    • 58.
    • 59. 2010 - May - 7 - NodeXL - twitter global warming
    • 60. 2010 - May - 7 - NodeXL - twitter climate change
    • 61. Bernie Hogan is a Research Fellow at the Oxford Internet Institute at the University of Oxford. Bernie's work focuses on the process of networking, or maintaining connections with other people. His dissertation focused on the use of multiple media for networking while his current research on Facebook looks at the complexities of networking with multiple groups on a single site.
    • 62. Facebook “ego” networks
    • 63. Scott Golder (@redlog) is a graduate student in Sociology at Cornell University. He was previously a researcher at HP Labs, and holds an A.B. in Linguistics with Computer Science from Harvard University and an M.S. in Media Arts and Sciences from the MIT Media Laboratory. His research interests broadly include network and social identity effects online, which he has examined in a variety of environments including usenet, online poker, social bookmarking and social network services. His website is www.redlog.net.
      Vladimir Barash (@vlad43210) is a graduate student in Information Science at Cornell University. He holds a BA in Cognitive Science from Yale University. His research interests include social media, online communities and diffusion, and his thesis topic is on the structural properties of diffusion in social networks. His websited is www.vlad43210.com
    • 64. Tuesday 18 May
      4:00pm
      Arlen Specter
      Following: 348
      Followers: 8704
      Tweets: 580
      Joe Sestak
      Following: 3845
      Followers: 3631
      Tweets: 763
    • 65. Tuesday 18 May
      4:00pm
      Arlen Specter
      Following: 348
      Followers: 8704
      Tweets: 580
      Joe Sestak
      Following: 3845
      Followers: 3631
      Tweets: 763
    • 66. Social media looks like...
      http://www.cmu.edu/joss/content/articles/volume8/Welser/
    • 67. Which contains subgraphs
    • 68. That result in “Badges” Markers of social status
      Thanks to 3ones.com
    • 69. Social Media NetworkBadges
      Connected Action badges allow publishers and community developers to encourage the community engagement they value by rewarding the user behaviors they desire.
    • 70. Network Based Game Mechanics for Social Media
      How badges shape behavior:
      > Status markers
      > Aspirational targets
      > Volume and location rewards:         
              longer posts, more prominently located
    • 71. Questions we answer
      • Who contributes the most effectively?Resulting in most pageviews
      • 72. Who connects the most?Resulting in new users/visits/cross-pollination of content
      • 73. Who answers the questions?Adding authoritativeness to your community discussions
      • 74. Who starts the conversations?Resulting in new engagement, increased time spent and pageviews
      • 75. How to encourage more of this behavior?Resulting in more of the same
    • Badges on Comments
      Thanks to 3ones.com
    • 76. Badging Events in Recent Comments Stream
      Thanks to 3ones.com
    • 77. User Badge Widget
      Thanks to 3ones.com
    • 78. Badge Types
      • Basic Badges:
      • 79. Popular: people who are connected to many other people
      • 80. Networker: people who span widely across the community, connecting many
      • 81. Influential: people who are connected to the highly connected people
      • 82. Advanced badges include:
      • 83. Answer Person: people who have provided brief replies to many low frequency contributors
      • 84. Agenda Setter: people who introduce topics that attract many repliers
      • 85. Question Person: people who ask questions that get answered by Answer People
      • 86. Discussion Person: people who connect to many people who also connect to each other
      • 87. Eclectic: people who connect to a wide range of content
      • 88. Newcomers get badges of their own: "Newest Bridge Builder, Newest Discussion person"
      • 89. Mayor of Topics: Long term contributors in each role get recognized: Senior Bridge Builder, Senior Discussion Person"
      • 90. Bridge Builder: people who connect with the most diverse collection of others.
    • Intended Results
      • Badges from your site's Activity Stream
      • 91. Automated reward and marker system for content creators
      • 92. Increase engagement
      • 93. Increase trust
      • 94. Increase credibility
      • 95. Decrease attrition
      • 96. Increase pageviews and visits
    • Summary: SNA tells you:
      Macro:
      What is the “shape” of the crowd?
      Are there sub-groups/clusters?
      Micro:
      Who is at the “center”?
      Who is at the “edge”?
      Who is the “bridge”?
    • 97. Contact:
      Marc A. Smith
      Chief Social Scientist
      Connected Action Consulting Group
      Marc@connectedaction.net
      http://www.connectedaction.net
      http://www.codeplex.com/nodexl
      http://www.twitter.com/marc_smith
      http://delicious.com/marc_smith/Paper
      http://www.flickr.com/photos/marc_smith
      http://www.facebook.com/marc.smith.sociologist
      http://www.linkedin.com/in/marcasmith
      http://www.slideshare.net/Marc_A_Smith
      http://www.smrfoundation.org
    • 98. Finding direction in a sea of connection:
      Mapping networks and
      Social media
      Marc A. Smith
      Chief Social ScientistConnected Action Consulting Group
      marc@connectedaction.net
      http://www.connectedaction.net
      http://www.codeplex.com/nodexl
      A project from the Social Media Research Foundation: http://www.smrfoundation.org

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