This document discusses using social network analysis to understand social media crowds. It explains key concepts in social network analysis like nodes, edges, centrality measures, and network visualization tools. The document promotes NodeXL as a tool for conducting social network analysis on social media data to understand crowd dynamics at both the macro level of overall structures and the micro level of individual roles. The goal is to provide insights about who the most central and influential users are and how information spreads through the network.

1. Says who? Mapping the shape of social media crowds 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. http://www.flickr.com/photos/library_of_congress/3295494976/sizes/o/in/photostream/

4. http://www.flickr.com/photos/amycgx/3119640267/

6. Telecom networks are social networks

8. “actor” on which relationships act; 1-mode versus 2-mode networks

9. Edge

10. Relationship connecting nodes; can be directional

11. Cohesive Sub-Group

12. Well-connected group; clique; cluster

13. Key Metrics

14. Centrality (group or individual measure)

15. Number of direct connections that individuals have with others in the group (usually look at incoming connections only)

16. Measure at the individual node or group level

17. Cohesion (group measure)

18. Ease with which a network can connect

19. Aggregate measure of shortest path between each node pair at network level reflects average distance

20. Density (group measure)

21. Robustness of the network

22. Number of connections that exist in the group out of 100% possible

23. Betweenness (individual measure)

24. # shortest paths between each node pair that a node is on

25. Measure at the individual node level

26. Node roles

27. Peripheral – below average centrality

28. Central connector – above average centrality

29. Broker – above average betweennessA B C A B D E D E G F C D H I E

30. Location, Location, Location

31. Network of connections among “MWA2010” mentioning Twitter users Position, Position, Position

32. Most “between” people in the Network of connections among “MWA2010” Twitter users

33. “Think Link”Nodes & Edges Is related to B A In and Out Degree

34. Each contains one or more social networks World Wide Web

35. Collections of ConnectionsCentralities Degree Closeness Betweenness Eigenvector http://en.wikipedia.org/wiki/Centrality

36. NodeXLNetwork Overview Discovery and Exploration add-in for Excel 2007 Heather has high betweenness Diane has high degree A minimal network can illustrate the ways different locations have different values for centrality and degree

37. Social Networks History: from the dawn of time! Theory and method: 1934 -> Jacob L. Moreno http://en.wikipedia.org/wiki/Jacob_L._Moreno

38. 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. 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

44. 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.

45. Introduction to NodeXL

46. NodeXL: Network Overview, Discovery and Exploration for Excel Leverage spreadsheet for storage of edge and vertex data http://www.codeplex.com/nodexl

47. Social Media Research Foundation Open Tools, Open Data, Open Scholarship

48. Social Media Research Foundationhttp://smrfoundation.org

49. Now Available

50. Communities in Cyberspace

51. Import from multiple social media networksources

52. http://www.youtube.com/watch?v=0M3T65Iw3Ac NodeXL Video

53. 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

55. Twitter “Pigspotter” – September 28, 2010

56. Twitter “Malema” – September 28, 2010

57. Twitter “Palin” – September 28, 2010

58. Twitter Vodacom – September 29, 2010

59. Twitter Worldcup – September 24, 2010

60. Twitter #TCOT – September 24, 2010

61. 2010 - April - 21 - NodeXL - Twitter - teaparty followers scaled

62. 2010 - May - 7 - NodeXL - twitter global warming

63. 2010 - May - 7 - NodeXL - twitter climate change

64. 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.

65. Facebook “ego” networks

66. 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

67. Tuesday 18 May 4:00pm Arlen Specter Following: 348 Followers: 8704 Tweets: 580 Joe Sestak Following: 3845 Followers: 3631 Tweets: 763

68. Tuesday 18 May 4:00pm Arlen Specter Following: 348 Followers: 8704 Tweets: 580 Joe Sestak Following: 3845 Followers: 3631 Tweets: 763

69. Social media looks like... http://www.cmu.edu/joss/content/articles/volume8/Welser/

70. Which contains subgraphs

71. That result in “Badges” Markers of social status Thanks to 3ones.com

72. 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.

73. Network Based Game Mechanics for Social Media How badges shape behavior: > Status markers > Aspirational targets > Volume and location rewards: longer posts, more prominently located

75. Who connects the most?Resulting in new users/visits/cross-pollination of content

76. Who answers the questions?Adding authoritativeness to your community discussions

77. Who starts the conversations?Resulting in new engagement, increased time spent and pageviews

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

80. User Badge Widget Thanks to 3ones.com

81. Moderation of Badges Thanks to 3ones.com

83. Popular: people who are connected to many other people

84. Networker: people who span widely across the community, connecting many

85. Influential: people who are connected to the highly connected people

86. Advanced badges include:

87. Answer Person: people who have provided brief replies to many low frequency contributors

88. Agenda Setter: people who introduce topics that attract many repliers

89. Question Person: people who ask questions that get answered by Answer People

90. Discussion Person: people who connect to many people who also connect to each other

91. Eclectic: people who connect to a wide range of content

92. Newcomers get badges of their own: "Newest Bridge Builder, Newest Discussion person"

93. Mayor of Topics: Long term contributors in each role get recognized: Senior Bridge Builder, Senior Discussion Person"

97. Automated reward and marker system for content creators

98. Increase engagement

99. Increase trust

100. Increase credibility

101. Decrease attrition

103. 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

104. Says who? Mapping the shape of social media crowds 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