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

20110128 connected action-node xl-sea of connections Presentation 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