This document introduces NodeXL, a tool for mapping and analyzing social networks. NodeXL allows users to import social network data from sources like Twitter and Facebook. It then generates network graphs and calculates metrics like centrality and betweenness to analyze connection patterns. The document provides examples of NodeXL network maps of online discussions and social media to demonstrate how it can be used to explore information sharing and identify influential users.

1. Charting Collections of Connections in Social Media: Creating maps and Measures with NodeXL Marc A. Smith Director Social Media Research Foundationmarc@smrfoundation.org http://www.codeplex.com/nodexl

2. About Us Introductions Marc A. Smith Director Social Media Research Foundation Marc@smrfoundation.org http://www.smrfoundation.org http://www.codeplex.com/nodexl http://www.twitter.com/marc_smith http://delicious.com/marc_smith/Paper http://www.linkedin.com/in/marcasmith http://www.slideshare.net/Marc_A_Smith http://www.flickr.com/photos/marc_smith http://www.slideshare.net/SMRFoundation/ http://www.facebook.com/marc.smith.sociologist

3. http://www.flickr.com/photos/library_of_congress/3295494976/sizes/o/in/photostream/

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

5. Location, Location, Location

6. Network of connections among “SharePoint” mentioning Twitter users Position, Position, Position

7. What is social media? A Sociological Frame: Collective Goodsproduced through Computer-Mediated Collective Action formed through Interaction Networks

8. What makes social media social? Who makes it? Who consumes it? Who owns it?/Who profits from it? Who or what makes it successful? How to harness the swarm? How to map and understand its dynamics? How do people and groups vary? Who links to whom? What is next for social media?

9. Some Dimensions of Social Media How large are the social groups producing and consuming social media? How large and interactive are the objects produced and consumed? What does it mean to own a social media object?

10. Producers Individuals How large are the social groups producing and consuming social media? Small Groups Consumers Large Groups Individuals Small Groups Large Groups

11. Dimensions of Social Media: How large are the pieces of social media? How interactive is the rate of exchange?

12. Who owns social media content? Dimensions of Social Media: Who can exercise what property rights over social media?

13. Hardin, Garrett. 1968/1977. “The tragedy of the commons.” Science 162: 1243-48. Pp. 16-30 in Managing the Commons, edited by G. Hardin and J. Baden. San Francisco: Freeman. Wellman, Barry. 1997. “An electronic group is virtually a social network.” In S. Kiesler (Ed.), The Culture of the Internet. Hillsdale, NJ: Lawrence Erlbaum. 13

14. Collective Action Dilemma Theory Central tenet Individual rationality leads to collective disaster Phenomena of interest Provision and/or sustainable consumption of collective resources Public Goods, Common Property, "Free Rider” Problems, Tragedies Signaling intent Methods Surveys, interviews, participant observation, log file analysis, computer modeling (Axelrod, 1984; Hess, 1995; Kollock & Smith, 1996) Community Computer Mediated Collective Action

15. Common goods that require controlled consumption http://flickr.com/photos/himalayan-trails/275941886/

16. Common goods that require collective contribution http://flickr.com/photos/jose1jose2jose3/241450368/

17. 17 Motivations for contribution to computer mediated public goods Source: xkcd, http://xkcd.com/386/

18. Interactionist Sociology Central tenet Focus on the active effort of accomplishing interaction Phenomena of interest Presentation of self Claims to membership Juggling multiple (conflicting) roles Frontstage/Backstage Strategic interaction Managing one’s own and others’ “face” Methods Ethnography and participant observation (Goffman, 1959; Hall, 1990)

19. The Fan Dance of Concealment And Exposure http://flickr.com/photos/csb13/2178250762/

20. 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

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

23. Edge

24. Relationship connecting nodes; can be directional

25. Cohesive Sub-Group

26. Well-connected group; clique; cluster

27. Key Metrics

28. Centrality (group or individual measure)

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

30. Measure at the individual node or group level

31. Cohesion (group measure)

32. Ease with which a network can connect

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

34. Density (group measure)

35. Robustness of the network

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

37. Betweenness (individual measure)

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

39. Measure at the individual node level

40. Node roles

41. Peripheral – below average centrality

42. Central connector – above average centrality

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

44. Email (and more) is from people to people 22

45. Patterns are left behind 23

46. There are many kinds of ties…. http://www.flickr.com/photos/stevendepolo/3254238329

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

50. Whyte, William H. 1971. City: Rediscovering the Center. New York: Anchor Books.

51. AnswerPersonSignatures Discussion People

52. Discussion Starter Spammer Reply orientedDiscussion Flame Warrior 30

53. Youse. Y’all. Yes, youse. 31

54. I wish I knew you I like your picture You are cool I was paid to link to you I want your reflected glory Everybody else links to you I’d vote for you Can I date you? Are you my friend? We met at a conference and it seemed like the thing to do. no yes I kind of like you I really like you I like you I feel socially obligated to link to you I know you I beat you on Xbox Live Hi, Mom I have fake alter egos

55. 33

56. 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

57. Tag Ecologies I Adamic et al. WWW 2008

58. HUB-AND-SPOKE OF DECEIT: When Enron employees communicated about legitimate projects, e-mails were reciprocal and information was shared widely (right), but communications about an illicit project (left) reveal a sparse network with a central, informed clique and isolated external players. Brandy Aven, CMU http://www.sciencenews.org/view/generic/id/330731/title/Information_flow_can_reveal_dirty_deeds Networks reveal patterns

59. Goal: Make SNA easier Existing Social Network Tools are challenging for many novice users Tools like Excel are widely used Leveraging a spreadsheet as a host for SNA lowers barriers to network data analysis and display

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

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

62. 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

63. Now Available

64. Communities in Cyberspace

66. NodeXL map of flickr tags associated with Lipari

67. http://vimeo.com/21088958

68. http://www.flickr.com/photos/marc_smith/sets/72157622437066929/

70. http://www.connectedaction.net/2010/04/25/bernie-hogans-facebook-social-network-data-provider-and-visualization-toolkit/

71. NodeXL data import sources

72. Example NodeXL data importer for Twitter

73. NodeXL imports “edges” from social media data sources

74. NodeXL Automation makes analysis simple and fast

75. NodeXL Network Metrics

76. NodeXL simplifies mapping data attributes to display attributes

77. NodeXL Generates “Sub-Graph” Images

78. NodeXL displays subgraph images along with network metadata

79. NodeXL allows for fine control over the display of the network

80. NodeXL Generates Images of Networks

81. NodeXL Generates Network Graph Images

82. NodeXL enables filtering of networks

83. NodeXL Generates Filtered Network Images

84. NodeXL Generates Overall Network Metrics

85. NodeXL Map of Connections Among People who Tweeted “Galway”

87. Social networks in Twitter among people with at least one connection to someone else who Tweeted “Obama” on January 25, 2011

88. Network of word pairs frequently mentions among people who Tweeted the name “Obama” on January 25, 2011

89. US Congressman Paul Ryan word network (January 22, 2011)

90. Congresswoman Michel Bachmann keyword network (January 25, 2011)

92. Edge bundling, routing

93. Aggregate groups of nodes

94. Spigots: Wikis, Facebook, Gmail, ….?

96. Charting Collections of Connections in Social Media: Creating maps and Measures with NodeXL Marc A. Smith Director Social Media Research Foundationmarc@smrfoundation.org http://www.codeplex.com/nodexl