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)

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/

7. Collaboration networks are social networks

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 betweennessA 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

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

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

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

76. Badging Events in Recent Comments Stream Thanks to 3ones.com

77. User Badge Widget Thanks to 3ones.com

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"

91. Automated reward and marker system for content creators

92. Increase engagement

93. Increase trust

94. Increase credibility

95. Decrease attrition

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