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20110128 connected action-node xl-sea of connections
The document discusses the mapping of networks and social media, focusing on concepts such as collaboration networks, centrality, and cohesion within social structures. It introduces NodeXL, a tool for social network analysis that facilitates the integration of social media data into Excel for analysis. The work emphasizes understanding social roles, community engagement, and the importance of network metrics in evaluating social interactions online.
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20110128 connected action-node xl-sea of connections
- 1. Finding direction ina sea of connection: Mapping networks and Social mediaMarc A. SmithChief Social ScientistConnected Action Consulting Groupmarc@connectedaction.nethttp://www.connectedaction.nethttp://www.codeplex.com/nodexlA project from the Social Media Research Foundation: http://www.smrfoundation.org
- 2. About MeIntroductionsMarc A.SmithChief Social ScientistConnected Action Consulting GroupMarc@connectedaction.nethttp://www.connectedaction.nethttp://www.codeplex.com/nodexlhttp://www.twitter.com/marc_smithhttp://delicious.com/marc_smith/Paperhttp://www.flickr.com/photos/marc_smithhttp://www.facebook.com/marc.smith.sociologisthttp://www.linkedin.com/in/marcasmithhttp://www.slideshare.net/Marc_A_Smithhttp://www.smrfoundation.org
- 3. About YouIntroductionsOrganizationInterest innetworksTechnical skillsSocial media usageData setsQuestions you want networks to help answer
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- 7. Collaboration networks aresocial networks
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- 9. “actor” on whichrelationships act; 1-mode versus 2-mode networks
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- 11. Relationship connecting nodes;can be directional
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- 15. Centrality (group orindividual measure)
- 16. Number of directconnections that individuals have with others in the group (usually look at incoming connections only)
- 17. Measure at theindividual node or group level
- 18.
- 19. Ease with whicha network can connect
- 20. Aggregate measure ofshortest path between each node pair at network level reflects average distance
- 21.
- 22. Robustness of thenetwork
- 23. Number of connectionsthat exist in the group out of 100% possible
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- 25. # shortest pathsbetween each node pair that a node is on
- 26. Measure at theindividual node level
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- 28. Peripheral – belowaverage centrality
- 29. Central connector –above average centrality
- 30. Broker – aboveaverage betweennessABCABDEDEGFCDHIE
- 31.
- 32. Network of connectionsamong “SharePoint” mentioning Twitter usersPosition, Position, Position
- 33. Most “between” peoplein the Network of connections among “SharePoint” Twitter users
- 34. There are manykinds of ties….http://www.flickr.com/photos/stevendepolo/3254238329
- 35. “Think Link”Nodes &EdgesIs related toBAIn and Out Degree
- 36. “Think Link”Nodes &EdgesIs related toEditsBAShares membership Ties of different types
- 37. “Think Link”Nodes &EdgesIs related toEditsPersonDocumentShares membership Nodesof different types
- 38.
- 39. Each contains oneor more social networksWorld Wide Web
- 40. NodeXLNetwork Overview Discoveryand Exploration add-in for Excel 2007/2010Heather has high betweennessDiane has high degreeA minimal network can illustrate the ways different locations have different values for centrality and degree
- 41. Social NetworksHistory: fromthe dawn of time!Theory and method: 1934 ->Jacob L. Morenohttp://en.wikipedia.org/wiki/Jacob_L._Moreno
- 42. Social Network Theoryhttp://en.wikipedia.org/wiki/Social_networkCentraltenet Social structure emerges from the aggregate of relationships (ties) among members of a populationPhenomena of interestEmergence of cliques and clusters from patterns of relationshipsCentrality (core), periphery (isolates), betweennessMethodsSurveys, 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 settersDiscussion starters, Topic setters
- 48. Friends, foes, andfringe: 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.
- 50. NodeXL: Network Overview,Discovery and Exploration for ExcelLeverage spreadsheet for storage of edge and vertex datahttp://www.codeplex.com/nodexl
- 51. Social Media ResearchFoundationOpen Tools, Open Data, Open Scholarship
- 52. Social Media ResearchFoundationhttp://smrfoundation.org
- 53.
- 54.
- 55. Import from multiplesocial media networksources
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- 57. NodeXLFree/Open Social NetworkAnalysis 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 isa 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.
- 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 May4:00pmArlenSpecterFollowing: 348 Followers: 8704Tweets: 580Joe SestakFollowing: 3845 Followers: 3631Tweets: 763
- 65. Tuesday 18 May4:00pmArlenSpecterFollowing: 348 Followers: 8704Tweets: 580Joe SestakFollowing: 3845 Followers: 3631Tweets: 763
- 66. Social media lookslike...http://www.cmu.edu/joss/content/articles/volume8/Welser/
- 67.
- 68. That result in“Badges” Markers of social statusThanks to 3ones.com
- 69. Social Media NetworkBadgesConnectedAction badges allow publishers and community developers to encourage the community engagement they value by rewarding the user behaviors they desire.
- 70. Network Based GameMechanics for Social MediaHow badges shape behavior:> Status markers> Aspirational targets> Volume and location rewards: longer posts, more prominently located
- 71. Questions we answerWhocontributes the most effectively?Resulting in most pageviews
- 72. Who connects themost?Resulting in new users/visits/cross-pollination of content
- 73. Who answers thequestions?Adding authoritativeness to your community discussions
- 74. Who starts theconversations?Resulting in new engagement, increased time spent and pageviews
- 75. How to encouragemore of this behavior?Resulting in more of the sameBadges on CommentsThanks to 3ones.com
- 76. Badging Events inRecent Comments StreamThanks to 3ones.com
- 77. User Badge WidgetThanksto 3ones.com
- 78.
- 79. Popular: people whoare connected to many other people
- 80. Networker: people whospan widely across the community, connecting many
- 81. Influential: people whoare connected to the highly connected people
- 82.
- 83. Answer Person: peoplewho have provided brief replies to many low frequency contributors
- 84. Agenda Setter: peoplewho introduce topics that attract many repliers
- 85. Question Person: peoplewho ask questions that get answered by Answer People
- 86. Discussion Person: peoplewho connect to many people who also connect to each other
- 87. Eclectic: people whoconnect to a wide range of content
- 88. Newcomers get badgesof 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: peoplewho connect with the most diverse collection of others.Intended ResultsBadges from your site's Activity Stream
- 91. Automated reward andmarker system for content creators
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- 95.
- 96. Increase pageviews andvisitsSummary: 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. SmithChiefSocial ScientistConnected Action Consulting GroupMarc@connectedaction.nethttp://www.connectedaction.nethttp://www.codeplex.com/nodexlhttp://www.twitter.com/marc_smithhttp://delicious.com/marc_smith/Paperhttp://www.flickr.com/photos/marc_smithhttp://www.facebook.com/marc.smith.sociologisthttp://www.linkedin.com/in/marcasmithhttp://www.slideshare.net/Marc_A_Smithhttp://www.smrfoundation.org
- 98. Finding direction ina sea of connection: Mapping networks and Social mediaMarc A. SmithChief Social ScientistConnected Action Consulting Groupmarc@connectedaction.nethttp://www.connectedaction.nethttp://www.codeplex.com/nodexlA project from the Social Media Research Foundation: http://www.smrfoundation.org
Editor's Notes
- #5 http://www.flickr.com/photos/library_of_congress/3295494976/sizes/o/in/photostream/
- #6 http://www.flickr.com/photos/amycgx/3119640267/
- #19 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.
- #38 2010 - May - 7 - NodeXL - twitter global warming
- #39 2010 - May - 7 - NodeXL - twitter climate change