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2013 NodeXL Social Media Network Analysis

Social media network analysis and visualization with NodeXL - the network overview discovery and exploration add-in for Excel. Map Twitter, Facebook, email, blogs, and the web with a point and click interface within the familiar spreadsheet.

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2013 NodeXL Social Media Network Analysis

  1. 1. Charting Collections of Connections In Social Media: Creating Maps & Measures with NodeXLA project from the Social Media Research Foundation:
  2. 2. About MeIntroductionsMarc A. SmithChief Social ScientistConnected Action Consulting GroupMarc@connectedaction.nethttp://www.connectedaction.net
  3. 3. Social Media Research Foundation
  4. 4. Social Media(email, Facebook, Twitter,YouTube, and more)is all aboutconnections from people to people. 4
  5. 5. Patterns are left behind 5
  6. 6. There are many kinds of ties….Like, Link, Reply, Rate, Review, Favorite, Friend, Follow, Forward, Edit, Tag, Comment, Check-in…
  7. 7. “Think Link” Nodes & Edges Is related toA B
  8. 8. Each contains one or more social networksWorld Wide Web
  9. 9. Location, Location, Location
  10. 10. Position, Position, Position
  11. 11. Strong ties
  12. 12. Weak ties
  13. 13. Strength of Weak tiesp://
  14. 14. Social Networks• History: from the dawn of time!• Theory and method: 1934 ->• Jacob L. Moreno• /Jacob_L._Mor eno Jacob Moreno’s early social network diagram of positive and negative relationships among members of a football team. Originally published in Moreno, J. L. (1934). Who shall survive? Washington, DC: Nervous and Mental Disease Publishing Company.
  15. 15. A nearly 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.
  16. 16. Like MSPaint™ for graphs. — the CommunityIntroduction to NodeXL
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  22. 22. Social Network Maps RevealKey influencers in any topic. Sub-groups. Bridges.
  23. 23. Hubs
  24. 24. Bridges
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  28. 28. Network of connections among “#Debate AND Obama” mentioning Twitter users
  29. 29. NodeXLNetwork 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
  30. 30. 6 kinds of Twitter social media networks
  31. 31. #My2KPolarized
  32. 32. #CMgrChatIn-group / Community
  33. 33. LumiaBrand / Public Topic
  34. 34. #FLOTUS Bazaar
  35. 35. New York Times Article Paul KrugmanBroadcast: Audience + Communities
  36. 36. Dell Listens/Dellcares Support
  37. 37. #teaparty 15 November 2011#occupywallstreet15 November 2011
  38. 38. Social Network Theory• 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), Source: Richards, W. – betweenness (1986). The NEGOPY• Methods network analysis program. Burnaby, BC: – Surveys, interviews, observations, Department of Communication, Simon log file analysis, computational Fraser University. pp.7- analysis of matrices 16(Hampton &Wellman, 1999; Paolillo, 2001; Wellman, 2001)
  39. 39. SNA 101 • Node A – “actor” on which relationships act; 1-mode versus 2-mode networks • EdgeB – Relationship connecting nodes; can be directional C • Cohesive Sub-Group – Well-connected group; clique; cluster A B D E • Key Metrics – Centrality (group or individual measure) D • Number of direct connections that individuals have with others in the group (usually look at incoming connections only) E • Measure at the individual node or group level – 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 – Betweenness (individual measure) F G • # shortest paths between each node pair that a node is on • Measure at the individual node level • Node roles – Peripheral – below average centrality C H – Central connector – above average centrality D I – Broker – above average betweenness E
  40. 40. NodeXL Free/Open Social Network Analysis add-in for Excel 2007/2010 makes graphtheory as easy as a pie chart, with integrated analysis of social media sources.
  41. 41. Video
  42. 42. 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
  43. 43. Twitter Network for “Microsoft Research” *BEFORE*
  44. 44. Twitter Network for “Microsoft Research” *AFTER*
  45. 45. Network Motif Simplification Cody Dunne, University of Maryland
  46. 46. NodeXLGraph Gallery
  47. 47. Now Available
  48. 48. Communitiesin Cyberspace
  49. 49. This graph represents a directed network of 1,360 Twitter users whose recent tweetscontained "contraceptive OR contraception". The network was obtained on Friday, 08 June 2012 at 13:22 UTC. There is an edge for each follows relationship. There is an edge for each "replies- to" relationship in a tweet. There is an edge for each "mentions" relationship in a tweet. There is a self-loop edge for each tweet that is not a "replies-to" or "mentions". The tweets were made over the 2-day period from Thursday, 07 June 2012 at 18:46 UTC to Friday, 08 June 2012 at 13:06 UTC. The graphsvertices were grouped bycluster using the Clauset- Newman-Moore cluster algorithm. The edge colors are based on relationship values. Thevertex sizes are based on each user’s number of followers. Table 1 reports the summary network metrics that describe the graph.
  50. 50. Summary network metrics Table 1. Summary network metrics for the graph in Figure 1 Network Metric Value Graph Type Directed Vertices 1360 Unique Edges 5641 Edges With Duplicates 771 Total Edges 6412 Self-Loops 1096 Connected Components 427 Single-Vertex Connected Components 395 Maximum Vertices in a Connected Component 880 Max Edges in a Connected Component 5818 Maximum Geodesic Distance (Diameter) 12 Average Geodesic Distance 3.557807 Graph Density 0.002705817 Modularity 0.446145
  51. 51. The Vertices spreadsheet lists users who contributed a tweet containing the terms “contraception ORcontraceptives” over two days in early June 2012. Users are ranked by their computed betweenness centrality within the network of follows, replies, and mentions edges. The top 10 vertices, ranked by betweenness centrality are the accounts at the center of the network. These include:@thinkprogress, @gatesfoundation, @SandraFluke, @maleeek, @Change, @foxandfriends, @melindagates, @AshleyJu dd, @cnalive, and @SOHLTC.
  52. 52. 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
  53. 53. NodeXL calculatesnetwork metrics and word pairs
  54. 54. Contrasting groups
  55. 55. 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.
  56. 56. Contrast hashtags in Groups 2 & 4
  57. 57. Contrasting URL references
  58. 58. Word Pair Contrasts
  59. 59. NodeXL Ribbon in Excel
  60. 60. NodeXL data import sources
  61. 61. Example NodeXL data importer for Twitter
  62. 62. NodeXL imports “edges” from social media data sources
  63. 63. NodeXL displays subgraph images along with network metadataNodeXL creates a list of “vertices” from imported social media edges
  64. 64. Perform collections of common operations with NodeXL a single click Automationmakes analysissimple and fast
  65. 65. NodeXL Network Metrics
  66. 66. NodeXL “Autofill columns” simplifies mapping data attributes to display attributes
  67. 67. NodeXL enables filtering of networks
  68. 68. NodeXL Generates Overall Network Metrics
  69. 69. Social Media Research Foundation People Disciplines Institutions University Computer Science University of Maryland Faculty 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
  70. 70. What we are trying to do:Open Tools, Open Data, Open Scholarship• Build the “Firefox of GraphML” – open tools for collecting and visualizing social media data• Connect users to network analysis – make network charts as easy as making a pie chart• Connect researchers to social media data sources• Archive: Be the “Allen Very Large Telescope Array” for Social Media data – coordinate and aggregate the results of many user’s data collection and analysis• Create open access research papers & findings• Make “collections of connections” easy for users to manage
  71. 71. What we have done: Open Tools• NodeXL• Data providers (“spigots”) – ThreadMill Message Board – Exchange Enterprise Email – Voson Hyperlink – SharePoint – Facebook – Twitter – YouTube – Flickr
  72. 72. What we have done: Open Data• – 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
  73. 73. What we have done: Open Scholarship
  74. 74. What we have done: Open Scholarship
  75. 75. What we want to do:(Build the tools to) map the social web• Move NodeXL to the web: (Node[NOT]XL) – Node for Google Doc Spreadsheets? – WebGL Canvas? D3.JS? Sigma.JS• Connect to more data sources of interest: – RDF, MediaWikis, Gmail, NYT, Citation Networks• Solve hard network manipulation UI problems: – Modal transform, Time series, Automated layouts• Grow 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
  76. 76. How you can help• Sponsor a feature• Sponsor workshops• Sponsor a student• Schedule training• Sponsor the foundation• Donate your money, code, computation, storage, bandwidth, data or employee’s time• Help promote the work of the Social Media Research Foundation
  77. 77. Who is the mayor of your hashtag? Find out at:
  78. 78. Who is the mayor of your hashtag? Find out at:
  79. 79. Who is the mayor of your hashtag? Find out at:
  80. 80. Charting Collections of Connections In Social Media: Creating Maps & Measures with NodeXLA project from the Social Media Research Foundation: