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2014 TheNextWeb-Mapping connections with NodeXL

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Slides from a talk at the 2014 TheNextWeb in Amsterdam.
NodeXL social media network analysis of Twitter reveals six common structures in Twitter networks.

Published in: Internet, Technology, Business

2014 TheNextWeb-Mapping connections with NodeXL

  1. 1. A project from the Social Media Research Foundation: http://www.smrfoundation.org
  2. 2. About Me Introductions 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://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 http://www.smrfoundation.org
  3. 3. • 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 Social Network Theory http://en.wikipedia.org/wiki/Social_network
  4. 4. SNA 101 • Node – “actor” on which relationships act; 1-mode versus 2-mode networks • Edge – Relationship connecting nodes; can be directional • Cohesive Sub-Group – Well-connected group; clique; cluster • Key Metrics – Centrality (group or individual measure) • Number of direct connections that individuals have with others in the group (usually look at incoming connections only) • 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) • # shortest paths between each node pair that a node is on • Measure at the individual node level • Node roles – Peripheral – below average centrality – Central connector – above average centrality – Broker – above average betweenness E D F A CB H G I C D E A B D E
  5. 5. OF
  6. 6. Crowds matter
  7. 7. Kodak Brownie Snap- Shot Camera The first easy to use point and shoot!
  8. 8. http://www.flickr.com/photos/amycgx/3119640267/
  9. 9. Social Media (email, Facebook, Twitter, YouTube, and more) is all about connections from people to people. 10
  10. 10. Patterns are left behind 11
  11. 11. There are many kinds of ties…. Send, Mention, http://www.flickr.com/photos/stevendepolo/3254238329 Like, Link, Reply, Rate, Review, Favorite, Friend, Follow, Forward, Edit, Tag, Comment, Check-in…
  12. 12. “Think Link” Nodes & Edges Is related to A BIs related to Is related to
  13. 13. “Think Link” Nodes & Edges Is related to A BIs related to Is related to
  14. 14. World Wide Web Social media must contain one or more social networks
  15. 15. Vertex1 Vertex 2 “Edge” Attribute “Vertex1” Attribute “Vertex2” Attribute @UserName1 @UserName2 value value value A network is born whenever two GUIDs are joined. Username Attributes @UserName1 Value, value Username Attributes @UserName2 Value, value A B
  16. 16. NodeXL imports “edges” from social media data sources
  17. 17. Location, Location, Location
  18. 18. Position, Position, Position
  19. 19. Mapping and Measuring Connections with Like MSPaint™ for graphs. — the Community
  20. 20. Now Available
  21. 21. Communities in Cyberspace
  22. 22. 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
  23. 23. 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
  24. 24. What we have done: Open Tools • NodeXL • Data providers (“spigots”) – ThreadMill Message Board – Exchange Enterprise Email – Voson Hyperlink – SharePoint – Facebook – Twitter – YouTube – Flickr
  25. 25. NodeXL Ribbon in Excel
  26. 26. What we have done: Open Data • NodeXLGraphGallery.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
  27. 27. What we have done: Open Scholarship
  28. 28. http://www.pewinternet.org/2014/02/20/mapping-twitter-topic-networks-from-polarized-crowds-to-community-clusters/
  29. 29. http://www.flickr.com/photos/badgopher/3264760070/
  30. 30. Example NodeXL data importer for Twitter
  31. 31. http://www.flickr.com/photos/druclimb/2212572259/in/photostream/
  32. 32. http://www.flickr.com/photos/hchalkley/47839243/
  33. 33. http://www.flickr.com/photos/rvwithtito/4236716778
  34. 34. http://www.flickr.com/photos/62693815@N03/6277208708/
  35. 35. Social Network Maps Reveal Key influencers in any topic. Sub-groups. Bridges.
  36. 36. Hubs
  37. 37. Bridges
  38. 38. http://www.flickr.com/photos/storm-crypt/3047698741
  39. 39. http://www.flickr.com/photos/library_of_congress/3295494976/sizes/o/in/photostream/
  40. 40. [Divided] Polarized Crowds [Unified] Tight Crowd [Fragmented] Brand Clusters [Clustered] Community Clusters [In-Hub & Spoke] Broadcast Network [Out-Hub & Spoke] Support Network 6 kinds of Twitter social media networks
  41. 41. [Divided] Polarized Crowds [Unified] Tight Crowd [Fragmented] Brand Clusters [Clustered] Community Clusters [In-Hub & Spoke] Broadcast Network [Out-Hub & Spoke] Support Network 6 kinds of Twitter social media networks
  42. 42. #My2K Polarized
  43. 43. #CMgrChat In-group / Community
  44. 44. Lumia Brand / Public Topic
  45. 45. #FLOTUS Bazaar
  46. 46. New York Times Article Paul Krugman Broadcast: Audience + Communities
  47. 47. Dell Listens/Dellcares Support
  48. 48. 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 does #YourHashtag look like?
  49. 49. Top 10 Vertices @tnwconference @shingy @aral @patrick @jarnoduursma @sarahmarshall @boris @briansolis @technifista @qadabraplatform
  50. 50. Most central: @bitpay @coindesk @tuurdemeester @bitgiveorg @allthingsbtc @ihavebitcoins @btcmarketsnews @sp0rkyd0rky @hermetec @redditbtc
  51. 51. strataconf Twitter NodeXL SNA Map and Report for 2014-02-11 12-53-27 Top 10 Vertices, Ranked by Betweenness Centrality: @strataconf @peteskomoroch @acroll @oreillymedia @orthonormalruss @ayirpelle @bigdata @furrier @marketpowerplus @sassoftware
  52. 52. datavis Twitter NodeXL SNA Map and Report for Tuesday, 11 February 2014 at 18:55 UTC Top 10 Vertices, Ranked by Betweenness Centrality: @bigpupazzoverde @randal_olson @twitterdata @7of13 @yochum @edwardtufte @twittersports @grandjeanmartin @smfrogers @albertocairo
  53. 53. [Divided] Polarized Crowds [Unified] Tight Crowd [Fragmented] Brand Clusters [Clustered] Community Clusters [In-Hub & Spoke] Broadcast Network [Out-Hub & Spoke] Support Network 6 kinds of Twitter social media networks
  54. 54. [Divided]Polarize d Crowds [Unified]Tig ht Crowd [Fragmented] Brand Clusters [Clustered] Communities [In-Hub & Spoke]Broadcast Network [Out-Hub & Spoke]Support Network [Low probability] Find bridge users. Encourage shared material. [Low probability] Get message out to disconnected communities. [Possible transition] Draw in new participants. [Possible transition] Regularly create content. [Possible transition] Reply to multiple users. [Undesirable transition] Remove bridges, highlight divisions. [Low probability] Get message out to disconnected communities. [High probability] Draw in new participants. [Possible transition] Regularly create content. [Possible transition] Reply to multiple users. [Undesirable transition] Increase density of connections in two groups. [Low probability] Dramatically increase density of connections. [High probability] Increase retention, build connections. [Possible transition] Regularly create content. [Possible transition] Reply to multiple users. [Undesirable transition] Increase density of connections in two groups. [Low probability] Dramatically increase density of connections. [Undesirable transition] Increase population, reduce connections. [Possible transition] Regularly create content. [Possible transition] Reply to multiple users. [Undesirable transition] Increase density of connections in two groups. [Low probability] Dramatically increase density of connections. [Low probability] Get message out to disconnected communities. [Possible transition] Increase retention, build connections. [High probability] Increase reply rate, reply to multiple users. [Undesirable transition] Increase density of connections in two groups. [Low probability] Dramatically increase density of connections. [Possible transition] Get message out to disconnected communities. [High probability] Increase retention, build connections. [High probability] Increase publication of new content and regularly create content.
  55. 55. C. Scott Dempwolf, PhD Research Assistant Professor & Director UMD - Morgan State Center for Economic Development
  56. 56. What is Social Network Analysis? How is it useful for the humanities? 1. New framework for analysis 2. Data visualization allows new perspectives – less linear, more comprehensive Social Network Analysis and Ancient History Diane H. Cline, Ph.D. University of Cincinnati
  57. 57. Strategies for social media engagement based on social media network analysis
  58. 58. Request your own network map and report http://connectedaction.net
  59. 59. 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
  60. 60. 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
  61. 61. Thank you!
  62. 62. A project from the Social Media Research Foundation: http://www.smrfoundation.org

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