20110719 social media research foundation-charting collections of connections

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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.
  • Social networks in Twitter among people with at least one connection to someone else who Tweeted “Obama” on January 25, 2011
  • Network of word pairs frequently mentions among people who Tweeted the name “Obama” on January 25, 2011
  • 20110719 social media research foundation-charting collections of connections

    1. 1. Charting Collections of Connections in Social Media: Creating maps and Measures with NodeXL<br />Marc A. Smith<br />Director<br />Social Media Research Foundationmarc@smrfoundation.org<br />http://www.codeplex.com/nodexl<br />
    2. 2. About Us<br />Introductions<br />Marc A. Smith<br />Director<br />Social Media Research Foundation<br />Marc@smrfoundation.org<br />http://www.smrfoundation.org<br />http://www.codeplex.com/nodexl<br />http://www.twitter.com/marc_smith<br />http://delicious.com/marc_smith/Paper<br />http://www.linkedin.com/in/marcasmith<br />http://www.slideshare.net/Marc_A_Smith<br />http://www.flickr.com/photos/marc_smith<br />http://www.slideshare.net/SMRFoundation/<br />http://www.facebook.com/marc.smith.sociologist<br />
    3. 3. http://www.flickr.com/photos/library_of_congress/3295494976/sizes/o/in/photostream/<br />
    4. 4. http://www.flickr.com/photos/amycgx/3119640267/<br />
    5. 5. Location, Location, Location<br />
    6. 6. Network of connections among “SharePoint” mentioning Twitter users<br />Position, Position, Position<br />
    7. 7. What is social media?<br />A Sociological Frame:<br />Collective Goodsproduced through Computer-Mediated Collective Action<br />formed through<br />Interaction Networks<br />
    8. 8. What makes social media social?<br />Who makes it?<br />Who consumes it?<br />Who owns it?/Who profits from it?<br />Who or what makes it successful?<br />How to harness the swarm?<br />How to map and understand its dynamics?<br />How do people and groups vary?<br />Who links to whom?<br />What is next for social media?<br />
    9. 9. Some Dimensions of Social Media<br />How large are the social groups producing and consuming social media?<br />How large and interactive are the objects produced and consumed?<br />What does it mean to own a social media object?<br />
    10. 10. Producers<br />Individuals<br />How large are the social groups producing and consuming social media?<br />Small Groups<br />Consumers<br />Large Groups<br />Individuals<br />Small Groups<br />Large Groups<br />
    11. 11. Dimensions of Social Media:<br />How large are the pieces of social media?<br />How interactive is the rate of exchange?<br />
    12. 12. Who owns social media content?<br />Dimensions of Social Media:<br />Who can exercise what property rights over social media?<br />
    13. 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.<br />Wellman, Barry. 1997. “An electronic group is virtually a social network.” In S. Kiesler (Ed.), The Culture of the Internet. Hillsdale, NJ: Lawrence Erlbaum. <br />13<br />
    14. 14. Collective Action Dilemma Theory<br />Central tenet<br />Individual rationality leads to collective disaster<br />Phenomena of interest<br />Provision and/or sustainable consumption of collective resources<br />Public Goods, Common Property, "Free Rider” Problems, Tragedies<br />Signaling intent<br />Methods<br />Surveys, interviews, participant observation, log file analysis, computer modeling<br />(Axelrod, 1984; Hess, 1995; Kollock & Smith, 1996) <br />Community Computer Mediated Collective Action<br />
    15. 15. Common goods that require controlled consumption<br />http://flickr.com/photos/himalayan-trails/275941886/<br />
    16. 16. Common goods that require collective contribution<br />http://flickr.com/photos/jose1jose2jose3/241450368/<br />
    17. 17. 17<br />Motivations for contribution to computer mediated public goods<br />Source: xkcd, http://xkcd.com/386/<br />
    18. 18. Interactionist Sociology<br />Central tenet<br />Focus on the active effort of accomplishing interaction<br />Phenomena of interest<br />Presentation of self <br />Claims to membership<br />Juggling multiple (conflicting) roles<br />Frontstage/Backstage <br />Strategic interaction<br />Managing one’s own and others’ “face”<br />Methods<br />Ethnography and participant observation<br />(Goffman, 1959; Hall, 1990)<br />
    19. 19. The Fan Dance of <br />Concealment <br />And <br />Exposure<br />http://flickr.com/photos/csb13/2178250762/<br />
    20. 20. Social Network Theoryhttp://en.wikipedia.org/wiki/Social_network<br />Central tenet <br />Social structure emerges from <br /> the aggregate of relationships (ties) <br /> among members of a population<br />Phenomena of interest<br />Emergence of cliques and clusters <br /> from patterns of relationships<br />Centrality (core), periphery (isolates), <br /> betweenness<br />Methods<br />Surveys, interviews, observations, log file analysis, computational analysis of matrices<br />(Hampton &Wellman, 1999; Paolillo, 2001; Wellman, 2001)<br />Source: Richards, W. (1986). The NEGOPY network analysis program. Burnaby, BC: Department of Communication, Simon Fraser University. pp.7-16<br />
    21. 21. SNA 101<br /><ul><li>Node
    22. 22. “actor” on which relationships act; 1-mode versus 2-mode networks
    23. 23. Edge
    24. 24. Relationship connecting nodes; can be directional
    25. 25. Cohesive Sub-Group
    26. 26. Well-connected group; clique; cluster
    27. 27. Key Metrics
    28. 28. Centrality (group or individual measure)
    29. 29. Number of direct connections that individuals have with others in the group (usually look at incoming connections only)
    30. 30. Measure at the individual node or group level
    31. 31. Cohesion (group measure)
    32. 32. Ease with which a network can connect
    33. 33. Aggregate measure of shortest path between each node pair at network level reflects average distance
    34. 34. Density (group measure)
    35. 35. Robustness of the network
    36. 36. Number of connections that exist in the group out of 100% possible
    37. 37. Betweenness (individual measure)
    38. 38. # shortest paths between each node pair that a node is on
    39. 39. Measure at the individual node level
    40. 40. Node roles
    41. 41. Peripheral – below average centrality
    42. 42. Central connector – above average centrality
    43. 43. Broker – above average betweenness</li></ul>A<br />B<br />C<br />A<br />B<br />D<br />E<br />D<br />E<br />G<br />F<br />C<br />D<br />H<br />I<br />E<br />
    44. 44. Email (and more) is from people to people<br />22<br />
    45. 45. Patterns are left behind<br />23<br />
    46. 46. There are many kinds of ties….<br />http://www.flickr.com/photos/stevendepolo/3254238329<br />
    47. 47. Each contains one or more social networks<br />World Wide Web<br />
    48. 48.
    49. 49.
    50. 50. Whyte, William H. 1971. City: Rediscovering the Center. New York: Anchor Books.<br />
    51. 51. AnswerPersonSignatures<br />Discussion<br />People<br />
    52. 52. Discussion Starter<br />Spammer<br />Reply orientedDiscussion<br />Flame<br />Warrior<br />30<br />
    53. 53. Youse.<br />Y’all.<br />Yes, youse.<br />31<br />
    54. 54. I wish I knew you<br />I like your picture<br />You are cool<br />I was paid to link to you<br />I want your reflected glory<br />Everybody else links to you<br />I’d vote for you<br />Can I date you?<br />Are you my friend?<br />We met at a conference and it seemed like the thing to do.<br />no<br />yes<br />I kind of like you<br />I really like you<br />I like you<br />I feel socially obligated to link to you<br />I know you<br />I beat you on Xbox Live<br />Hi, Mom<br />I have fake alter egos<br />
    55. 55. 33<br />
    56. 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).<br />Experts and “Answer People”<br />Discussion people, Topic setters<br />Discussion starters, Topic setters<br />
    57. 57. Tag Ecologies I<br />Adamic et al. WWW 2008<br />
    58. 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.<br />Brandy Aven, CMU<br />http://www.sciencenews.org/view/generic/id/330731/title/Information_flow_can_reveal_dirty_deeds<br />Networks reveal patterns<br />
    59. 59. Goal: Make SNA easier<br />Existing Social Network Tools are challenging for many novice users<br />Tools like Excel are widely used<br />Leveraging a spreadsheet as a host for SNA lowers barriers to network data analysis and display<br />
    60. 60. Social Media Research Foundation<br />Open Tools, Open Data, Open Scholarship<br />
    61. 61. Social Media Research Foundationhttp://smrfoundation.org<br />
    62. 62. NodeXLNetwork Overview Discovery and Exploration add-in for Excel 2007/2010<br />Heather has high betweenness<br />Diane has high degree<br />A minimal network can illustrate the ways different locations have different values for centrality and degree<br />
    63. 63. Now Available<br />
    64. 64. Communities in Cyberspace<br />
    65. 65.
    66. 66. NodeXL map of flickr tags associated with Lipari<br />
    67. 67. http://vimeo.com/21088958<br />
    68. 68. http://www.flickr.com/photos/marc_smith/sets/72157622437066929/<br />
    69. 69.
    70. 70. http://www.connectedaction.net/2010/04/25/bernie-hogans-facebook-social-network-data-provider-and-visualization-toolkit/<br />
    71. 71. NodeXL data import sources<br />
    72. 72. Example NodeXL data importer for Twitter<br />
    73. 73. NodeXL imports “edges” from social media data sources<br />
    74. 74. NodeXL Automation makes analysis simple and fast<br />
    75. 75. NodeXL Network Metrics<br />
    76. 76. NodeXL simplifies mapping data attributes to display attributes<br />
    77. 77. NodeXL Generates “Sub-Graph” Images<br />
    78. 78. NodeXL displays subgraph images along with network metadata<br />
    79. 79. NodeXL allows for fine control over the display of the network<br />
    80. 80. NodeXL Generates Images of Networks<br />
    81. 81. NodeXL Generates Network Graph Images<br />
    82. 82. NodeXL enables filtering of networks<br />
    83. 83. NodeXL Generates Filtered Network Images<br />
    84. 84. NodeXL Generates Overall Network Metrics<br />
    85. 85. NodeXL Map of Connections Among People who Tweeted “Galway”<br />
    86. 86.
    87. 87. Social networks in Twitter among people with at least one connection to someone else who Tweeted “Obama” on January 25, 2011<br />
    88. 88. Network of word pairs frequently mentions among people who Tweeted the name “Obama” on January 25, 2011 <br />
    89. 89. US Congressman Paul Ryan word network (January 22, 2011)<br />
    90. 90. Congresswoman Michel Bachmann keyword network (January 25, 2011)<br />
    91. 91. NodeXL – Next Steps<br /><ul><li> Time and dynamic networks
    92. 92. Edge bundling, routing
    93. 93. Aggregate groups of nodes
    94. 94. Spigots: Wikis, Facebook, Gmail, ….?
    95. 95. Move to the Web!</li></li></ul><li>About Us<br />Introductions<br />Marc A. Smith<br />Director<br />Social Media Research Foundation<br />Marc@smrfoundation.org<br />http://www.smrfoundation.org<br />http://www.codeplex.com/nodexl<br />http://www.twitter.com/marc_smith<br />http://delicious.com/marc_smith/Paper<br />http://www.linkedin.com/in/marcasmith<br />http://www.slideshare.net/Marc_A_Smith<br />http://www.flickr.com/photos/marc_smith<br />http://www.slideshare.net/SMRFoundation/<br />http://www.facebook.com/marc.smith.sociologist<br />
    96. 96. Charting Collections of Connections in Social Media: Creating maps and Measures with NodeXL<br />Marc A. Smith<br />Director<br />Social Media Research Foundationmarc@smrfoundation.org<br />http://www.codeplex.com/nodexl<br />

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