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2013 passbac-marc smith-node xl-sna-social media-formatted

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Presentation at the 2013 PASSBAC SQL Server Business Analytics Conference on NodeXL SNA Maps and Reports for social media networks.

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2013 passbac-marc smith-node xl-sna-social media-formatted

  1. 1. Charting Collections of SocialMedia Connections with NodeXLMaps and reports for social media networks April 10-12, Chicago, IL
  2. 2. Please silencecell phones April 10-12, Chicago, IL
  3. 3. About Me 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 April 10-12, Chicago, IL http://www.smrfoundation.org
  4. 4. http://smrfoundation.org April 10-12, Chicago, IL
  5. 5. Social Media (email,Facebook, Twitter,YouTube, and more)is all about connections from people to people. 5 5
  6. 6. Patterns are left behind 6 6
  7. 7. There are many kinds of ties…. Send, Mention, Like, Link,Reply, Rate, Review, Favorite, Friend, Follow, Forward, Edit, Tag, Comment, Check-in… http://www.flickr.com/photos/stevendepolo/3254238329
  8. 8. Strong ties
  9. 9. Weak ties
  10. 10. http://www.flickr.com/photos/fullaperture/81266869/ Strength of Weak ties
  11. 11. “Think Link” Nodes & Edges Is related toA B 11
  12. 12. Each contains one or more social networksWorld Wide Web
  13. 13. A network is born whenever two GUIDs are joined.Username Attributes Username Attributes@UserName1 Value, value @UserName2 Value, value A B Vertex1 Vertex 2 “Edge” “Vertex1” “Vertex2” Attribute Attribute Attribute @UserName1 @UserName2 value value value
  14. 14. NodeXL imports “edges” from social media data sources
  15. 15. http://byobi.com/blog/2013/03/analyzing-sql-server-object-dependencies-with-nodexl/Bill Anton (@SQLbyoBI)HOW TO BUILDa table of all the object dependencies in a database. 15
  16. 16. Social Networks Jacob Moreno ’ s early social network diagram ofHistory: positive and negative relationships amongfrom the dawn of time! members of a football team.Theory and method: Originally published in1934 -> Moreno, J. L. (1934). Who shall survive? Washington,Jacob L. Moreno DC: Nervous and Mental Disease Publishing Company. http://en.wikipedia.org/wiki/Jacob_L._Moren 16
  17. 17. Social network diagram of relationships among workers in a factory illustrates the positions different workers occupy within the workgroup. Originally published in Roethlisberger, F., and Dickson, W. (1939). Management and the worker. Cambridge, UK: Cambridge University Press.
  18. 18. Location, Location, Location
  19. 19. Position, Position, Position
  20. 20. Introduction to NodeXL Like MSPaint™ for network graphs. 21
  21. 21. Communities inCyberspace
  22. 22. http://www.flickr.com/photos/badgopher/3264760070/
  23. 23. http://www.flickr.com/photos/druclimb/2212572259/in/photostrea m/
  24. 24. http://www.flickr.com/photos/hchalkley/47839243/
  25. 25. http://www.flickr.com/photos/rvwithtito/4236716778
  26. 26. http://www.flickr.com/photos/62693815@N03/6277208708/
  27. 27. Social Network Maps Reveal Key influencers in any topic. Sub-groups. Bridges.
  28. 28. Hubs
  29. 29. Bridges
  30. 30. http://www.flickr.com/photos/storm-
  31. 31. http://www.flickr.com/photos/library_of_congress/3295494976/sizes/o/in/photostream/
  32. 32. http://www.flickr.com/photos/amycgx/3119640267/
  33. 33. 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 & Discussion people Discussion starters“Answer People” Topic setters Topic setters 41
  34. 34. NodeXL: Network Overview Discovery and Exploration add-in for Excel 2007/2010 A minimal network can illustrate the ways different locations have different values for centrality and degree 42
  35. 35. http://www.newscientist.com/blogs/onepercent/2011/11/occupy-vs-tea-party-what-their.html #teaparty 15 November 2011#occupywallstreet15 November 2011
  36. 36. 6 kinds of Twitter social media networks 45
  37. 37. #My2KPolarized 46
  38. 38. #CMgrChatIn-group / Community 47
  39. 39. LumiaBrand / Public Topic 48
  40. 40. #FLOTUS Bazaar 49
  41. 41. New York Times Article Paul Krugman Broadcast: Audience + Communities 50
  42. 42. Dell Listens/Dellcares Support 51
  43. 43. SNA questions for social media:1. What does my topic network look like?2. What does the topic I aspire to be look like?3. What is the difference between #1 and #2?4. How does my map change as I intervene? What do #SQLPass and #PASSBAC look like? 52
  44. 44. http://nodexlgraphgallery.org/Pages/Graph.aspx?graphID=3965Top 10 Vertices, Ranked by Betweenness Centrality:@sqlpass@BrentO@PaulRandal@ClerisyDatabase@SQLRockstar@jenstirrup@SQLChicken@SQLSocialite@MicrosoftBI@kekline
  45. 45. http://nodexlgraphgallery.org/Pages/Graph.aspx?graphID=3966Top 10 Vertices, Ranked by BetweennessCentrality:@passbac@MicrosoftBI@dennylee@impetustech@sqlpass@ExtendedResults@StaciaMisner@marcorus@SQLRockstar@jenstirrup
  46. 46. http://nodexlgraphgallery.org/Pages/Graph.aspx?graphID=3982Top 10 Vertices, Ranked by Betweenness Centrality:@SQLServer@eric_kavanagh@DBA_MAN@confio@DZone@SQLRockstar@YvesMulkers@BrentO@SQL_By_Joey@zymasesystems
  47. 47. http://nodexlgraphgallery.org/Pages/Graph.aspx?graphID=3983Top 10 Vertices, Ranked by Betweenness Centrality:@timoreilly@hortonworks@cloudera@YvesMulkers@TDWI@IBMbigdata@eric_kavanagh@furrier@benjguin@andreisavu
  48. 48. http://nodexlgraphgallery.org/Pages/Graph.aspx?graphID=3984Top 10 Vertices, Ranked by Betweenness Centrality:@neo4j@peterneubauer@emileifrem@jimwebber@DZone@Neo4jFr@al3xandru@volkantufekci@ajlopez@p3rnilla
  49. 49. Social Network Theoryhttp://en.wikipedia.org/wiki/Social_networkCentral tenetSocial 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), betweenness Source: Richards, W. (1986). The NEGOPY network analysisMethods program. Burnaby, BC: Department ofSurveys, interviews, observations, log file analysis, computational Communication, Simonanalysis of matrices Fraser University. pp.7- 16(Hampton &Wellman, 1999; Paolillo, 2001; Wellman, 2001) 59
  50. 50. SNA 101 • Node – “actor” on which relationships act; 1-mode versus 2-mode networks • Edge A – Relationship connecting nodes; can be directional • Cohesive Sub-Group – Well-connected group; clique; cluster A B D EB C • Key Metrics – Centrality (group or individual measure) • Number of direct connections that individuals have with others in the group (usually look at D incoming connections only) • Measure at the individual node or group level E – Cohesion (group measure) • Ease with which a network can connect • Aggregate measure of shortest path between each node pair at network level reflects average distance – Density (group measure) • Robustness of the network • Number of connections that exist in the group out of 100% possible F G – Betweenness (individual measure) • # shortest paths between each node pair that a node is on • Measure at the individual node level H I • Node roles – Peripheral – below average centrality C – Central connector – above average centrality D – Broker – above average betweenness E
  51. 51. NodeXL: Free/Open Social Network Analysis add-in for Excel 2007/2010makes graph theory as easy as a pie chart, with integrated analysis of socialmedia sources. See: http://nodexl.codeplex.com 61
  52. 52. http://www.youtube.com/watch?v=0M3T65Iw3AcNodeXL Video
  53. 53. 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 63
  54. 54. Twitter Network for “Microsoft Research” *BEFORE*
  55. 55. Twitter Network for “Microsoft Research”*AFTER* 65
  56. 56. Network Motif Simplification Cody Dunne, University of Maryland 66
  57. 57. NodeXL calculatesnetwork metrics and wordpairs 67
  58. 58. The Content summary spreadsheet displays the mostfrequently used URLs, hashtags, and user names within the network as a whole and within each calculated sub-group. 68
  59. 59. 69
  60. 60. NodeXL Ribbon in Excel
  61. 61. NodeXL imports “edges” from social media data sources
  62. 62. NodeXL creates a list of “vertices” from imported social media edges NodeXL displays subgraph images along with network metadata
  63. 63. NodeXL Perform collections of Automation common operations withmakes analysis a single clicksimple and fast
  64. 64. NodeXL Generates Overall Network Metrics
  65. 65. Social Media Research Foundation People Disciplines Institutions University Faculty Computer Science University of Maryland Students HCI, CSCW Oxford Internet Institute Industry Machine Learning Stanford University Independent Information Visualization Microsoft Research Researchers UI/UX Illinois Institute of Technology Developers Social Science/Sociology Connected Action Network Analysis Cornell Collective Action Morningside Analytics 75
  66. 66. What we are trying to do:Open Tools, Open Data, Open ScholarshipBuild the “Firefox of GraphML” – open tools for collecting andvisualizing social media dataConnect users to network analysis – makenetwork charts as easy as making a pie chartConnect researchers to social media data sourcesArchive: Be the “Allen Very Large Telescope Array” for SocialMedia data – coordinate and aggregate the results of many user’sdata collection and analysisCreate open access research papers & findingsMake “collections of connections” easy for users to manage 76
  67. 67. What we have done: Open ToolsNodeXLData providers (“spigots”)• ThreadMill Message Board• Exchange Enterprise Email• Voson Hyperlink• SharePoint• Facebook• Twitter• YouTube• Flickr 77
  68. 68. What we have done: Open DataNodeXLGraphGallery.org• User generated collection of network graphs, datasets and annotations• Collective repository for the research community• Published collections of data from a range of social media data sources to help students and researchers connect with data of interest and relevance 78
  69. 69. What we have done: Open Scholarship 79
  70. 70. What we have done: Open Scholarship 80
  71. 71. What we want to do:(Build the tools to) map the social webMove NodeXL to the web: (Node[NOT]XL)• Node for Google Doc Spreadsheets?• WebGL Canvas? D3.JS? Sigma.JSConnect to more data sources of interest:• RDF, Gmail, NYT, Citation NetworksSolve hard network manipulation UI problems:• Modal transform, Time series, Automated layoutsGrow and maintain archives of social media network data sets for research use.Improve network science education:• Workshops on social media network analysis• Live lectures and presentations• Videos and training materials 81
  72. 72. How you can helpSponsor a featureSponsor workshopsSponsor a studentSchedule trainingSponsor the foundationDonate your money, code, computation, storage, bandwidth, data oremployee’s timeHelp promote the work of the Social Media Research Foundation 82
  73. 73. Charting Collections of SocialMedia Connections with NodeXLMaps and reports for social media networks April 10-12, Chicago, IL
  74. 74. Win a Microsoft Surface Pro!Complete an online SESSION EVALUATIONto be entered into the draw.Draw closes April 12, 11:59pm CTWinners will be announced on the PASS BAConference website and on Twitter.Go to passbaconference.com/evals or follow the QR code link displayed onsession signage throughout the conference venue.Your feedback is important and valuable. All feedback will be used to improveand select sessions for future events. 84
  75. 75. Marc@connectedaction.nethttp://www.connectedaction.nethttp://www.codeplex.com/nodexlhttp://www.twitter.com/marc_smithhttp://nodexlgraphgallery.orghttp://www.slideshare.net/Marc_A_Smithhttp://www.smrfoundation.org Platinum Sponsor Thank you! Diamond Sponsor April 10-12, Chicago, IL

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