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2015 pdf-marc smith-node xl-social media sna

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NodeXL social media network maps and reports for PDF15 - Personal Democracy Forum

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2015 pdf-marc smith-node xl-social media sna

  1. 1. A project from the Social Media Research Foundation: http://www.smrfoundation.org Network mapping the social media ecosystem with NodeXL
  2. 2. About Me Introductions Marc A. Smith Chief Social Scientist / Director Social Media Research Foundation marc@smrfoundation.org http://www.smrfoundation.org http://www.codeplex.com/nodexl http://www.twitter.com/marc_smith http://www.linkedin.com/in/marcasmith http://www.slideshare.net/Marc_A_Smith http://www.flickr.com/photos/marc_smith http://www.facebook.com/marc.smith.sociologist
  3. 3. Crowds matter
  4. 4. http://www.flickr.com/photos/amycgx/3119640267/ Crowds in social media matter
  5. 5. Crowds in social media have a hidden structure
  6. 6. https://demo-3dg-viz.herokuapp.com/
  7. 7. http://www.bonkersworld.net/organizational-charts/
  8. 8. Kodak Brownie Snap- Shot Camera The first easy to use point and shoot!
  9. 9. https://nodexlgraphgallery.org/Pages/Graph.aspx?graphID=46679 #pdf15 Twitter NodeXL SNA Map and Report for Thursday, 04 June 2015 at 21:18 UTC
  10. 10. NodeXL Ribbon in Excel
  11. 11. NodeXL in Excel
  12. 12. We envision hundreds of NodeXL data collectors around the world collectively generating a free and open archive of social media network snapshots on a wide range of topics. http://msnbcmedia.msn.com/i/msnbc/Components/Photos/071012/071012_telescope_hmed_3p.jpg
  13. 13. https://nodexlgraphgallery.org/Pages/Default.aspx?search=data+open
  14. 14. Top 10 Vertices: @mlsif @civichall @mitgc_cm @stone_rik @civicist @juansvas @tableteer @jcstearns @ppolitics @marc_smith #pdf15 Twitter NodeXL SNA Map and Report for Thursday, 04 June 2015 at 12:41 UTC Top 10 Hashtags: #pdf15 #ian1 #asmsg #bzbooks #bynr #civictech #nyc #authors #t4us #aga3
  15. 15. #pdf15 Twitter NodeXL SNA Map and Report for Thursday, 04 June 2015 at 12:41 UTC Broadcast Hub (stone_rik) Broadcast Hub (CivicHall, mlsif) Broadcast Hub (mitgc_cm) Brand Cluster (Isolates)
  16. 16. A DAY LATER
  17. 17. #pdf15 Twitter NodeXL SNA Map and Report for Thursday, 04 June 2015 at 21:18 UTC https://nodexlgraphgallery.org/Pages/Graph.aspx?graphID=46679 Top 10 Vertices: @mitgc_cm @stone_rik @mlsif @jgilliam @dantebarry @deanna @slaughteram @jcstearns @civicist @Digiphile Top 10 Hashtags: #pdf15 #civictech #tiimr #blacklivesmatter #ian1 #asmsg #bzbooks #bynr #pitmad #scfinalsvote
  18. 18. #pdf15 Twitter NodeXL SNA Map and Report for Thursday, 04 June 2015 at 21:18 UTC https://nodexlgraphgallery.org/Pages/Graph.aspx?graphID=46679 Community Cluster Broadcast Hub (digiphile) Brand Cluster (Isolates) Community Cluster Broadcast Hub (mlsif)
  19. 19. Hubs
  20. 20. https://flic.kr/p/4Z6GHv https://flic.kr/p/etEmeR
  21. 21. Bridges
  22. 22. http://www.flickr.com/photos/storm-crypt/3047698741
  23. 23. https://nodexlgraphgallery.org/Pages/Graph.aspx?graphID=46163 Top 10 Vertices: @niyiabiriblog @niyiabiri @codeforamerica @civichall @knightfdn @omidyarnetwork @betanyc @digiphile @elle_mccann @participatory Top 10 Hashtags: #civictech #opendata #opengov #latism #tictec #govtech #newurbanpractice #womenforward #gov20 #civichall civictech Twitter NodeXL SNA Map and Report for Tuesday, 26 May 2015 at 05:25 UTC
  24. 24. World Wide Web Social media must contain one or more social networks Crowds in social media form networks
  25. 25. Social Media (email, Facebook, Twitter, YouTube, and more) is all about connections from people to people. 30
  26. 26. Patterns are left behind 31
  27. 27. 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…
  28. 28. “Think Link” Nodes & Edges Is related to A BIs related to Is related to
  29. 29. “Think Link” Nodes & Edges Is related to A BIs related to Is related to
  30. 30. 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
  31. 31. NodeXL imports “edges” from social media data sources
  32. 32. http://techpresident.com/news/22538/cro wd-photography-cyber-tahrir-square http://foreignpolicy.com/2012/06/18/visu alizing-the-war-on-women-debate/ http://www.pewinternet.org/2014/02/20/mapping-twitter-topic- networks-from-polarized-crowds-to-community-clusters/
  33. 33. Social media network analysis • Social media is inherently made of networks, – which are created when people link and reply. • Collections of connections have an emergent shape, – Some shapes are better than others. • Some people are located in strategic locations in these shapes, – Centrally located people are more influential than others.
  34. 34. https://nodexlgraphgallery.org/Pages/Default.aspx?search=civic
  35. 35. http://www.pewinternet.org/2014/02/20/mapping-twitter-topic-networks-from-polarized-crowds-to-community-clusters/
  36. 36. [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
  37. 37. http://www.pewresearch.org/fact-tank/2014/02/20/the-six-types-of-twitter-conversations/
  38. 38. [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
  39. 39. #My2K Polarized
  40. 40. #CMgrChat In-group / Community
  41. 41. Lumia Brand / Public Topic
  42. 42. #FLOTUS Bazaar
  43. 43. New York Times Article Paul Krugman Broadcast: Audience + Communities
  44. 44. Dell Listens/Dellcares Support
  45. 45. New Book in Progress!
  46. 46. Social Network Maps Reveal Key influencers in any topic. Sub-groups. Bridges.
  47. 47. 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? Who is the mayor of #YourHashtag?
  48. 48. [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
  49. 49. Examples of social network scholarship Margarita M. Orozco Doctoral Student, School of Journalism & Mass Communication University of Wisconsin- Madison Katy Pearce (@katypearce) Assistant Prof of Communication Studies technology & inequality in Armenia & Azerbaijan. Elena Pavan, Ph.D. Post Doctoral Research Fellow Dipartimento di Sociologia e Ricerca Sociale Università di Trento via Verdi 26, 38122 Trento (Italy)
  50. 50. Examples of social network scholarship Margrét Vilborg Bjarnadóttir Robert H. Smith School of Business | University of Maryland Data Scientist | Parliamentary Special Investigation Commission Prof. Diane Harris Cline Associate Professor of History George Washington University C. Scott Dempwolf, PhD Research Assistant Professor & Director UMD - Morgan State Center for Economic Development
  51. 51. Studying the Colombian Peace Process in Twitter • Analyzing perceptions of the peace process in Colombian public opinion in Twitter. • It is important to know what are citizens thinking, perceptions, and concerns. • Q: who are the main actors in Twitter in favor and against the peace process who are leading sources of information about it? • Colombians are the world’s 15th top Twitter users. For this reason this social media constitutes an important source of information about public opinion. 6/5/2015 57 UNIVERSITY OF WISC ONSIN–MADISONMargarita M. Orozco Doctoral Student, School of Journalism & Mass Communication University of Wisconsin- Madison
  52. 52. Katy Pearce (@katypearce) Assistant Prof of Communication Studies technology & inequality in Armenia & Azerbaijan. #ProtestBaku Azerbaijan
  53. 53. Take Back The Tech! Reclaiming ICTs against Violence Against Women • Launched in 2006 by the Association for Progressive Communications Women Rights Program (APC WRP) • Runs yearly during the 16 days against Violence Against Women (VAW) • Website http://www.takebackthetech.net • “16 daily actions” to reclaim ICTs against VAW and a Tweetathon • Explored in the context of the project REACtION (http://www.reactionproject.info) in relation to the interplay between the “offline” advocacy strategy and the “online” Twitter networks over time • Findings: shifts in the advocacy strategy shift the network structure – moving from the outside to the online of the institutions (lobbying at the Commission on the Status of Women) led to a centralized Twitter network where organizational and institutional accounts play most central roles REACtION - Collective Action Networks between Online and Offline Interactions - http://www.reactionproject.info. Grant post-doc 2011 by the Provincia Autonoma di Trento (Italy) Elena Pavan, Ph.D. Post Doctoral Research Fellow Dipartimento di Sociologia e Ricerca Sociale Università di Trento via Verdi 26, 38122 Trento (Italy)
  54. 54. 2012: Outside institutions, a grassroots conversation REACtION - Collective Action Networks between Online and Offline Interactions - http://www.reactionproject.info. Grant post-doc 2011 by the Provincia Autonoma di Trento (Italy)
  55. 55. 2013: Accessing institutions, a more structured conversation REACtION - Collective Action Networks between Online and Offline Interactions - http://www.reactionproject.info. Grant post-doc 2011 by the Provincia Autonoma di Trento (Italy)
  56. 56. 2014: Inside institutions, a centralized conversation REACtION - Collective Action Networks between Online and Offline Interactions - http://www.reactionproject.info. Grant post-doc 2011 by the Provincia Autonoma di Trento (Italy)
  57. 57. Margrét Vilborg Bjarnadóttir Robert H. Smith School of Business | University of Maryland Data Scientist | Parliamentary Special Investigation Commission Data Driven Large Exposure Estimation: A Case Study of a Failed Banking System Co-authors: Sigríður Benediktsdóttir and Guðmundur Axel Hansen Supporting Publications: Margrét V. Bjarnadóttir and Gudmundur A. Hanssen. 2010. Cross-Ownership and Large Exposures; Analysis and Policy Recommendations. Report of the Special Investigation Commission, Volume 9. Sigridur Benediksdottir and Margrét V. Bjarnadóttir. “Large Exposure Estimation through Automatic Business Group Identification”. Proceedings to DSMM 2014.
  58. 58. C. Scott Dempwolf, PhD Research Assistant Professor & Director UMD - Morgan State Center for Economic Development http://www.terpconnect.umd.edu/~dempy/
  59. 59. Social Network Analysis for the humanities? Social Network Analysis and Ancient History Prof. Diane Harris Cline Associate Professor of History; Affiliated faculty member in Classical and Near Eastern Literatures and Civilizations. George Washington University 1. New framework for analysis 2. Data visualization allows new perspectives – less linear, more comprehensive
  60. 60. Applying the insights of social networks to social media: Your social media audience is smaller… …than the audiences of ten influential voices.
  61. 61. Build a collection of mayors • Map multiple topics – Your brand and company names – Your competitor brands and company names – The names of the activities or locations related to your products • Identify the top people in each topic • Follow these people – 30-50% of the time they follow you back • Re-tweet these people (if they did not follow you) • 30-50% of the time they follow you back
  62. 62. Speak the language of the mayors • Use NodeXL content analysis to identify each users most salient: – Words – Word pairs – URLs – #Hashtags • Mix the language of the Mayors with your brand’s messages.
  63. 63. Speak the language of the mayors The “perfect” tweet: .@Theirname #Theirhashtag News about your brand using their words http://your.site #Yourhashtag
  64. 64. Speak the language of the mayors
  65. 65. Some shapes are better than others: • The value of Broadcast versus community network! • From community to brand! • Support and why community can be a signal of failure!
  66. 66. Three network phases of social media success Phase 1: You get an audience Phase 2: Your audience gets an audience Phase 3: Audience becomes community
  67. 67. Some shapes are better than others • Each shape reflects the kind of social activity that generates it: – Divided: Conflict – Unified: In-group – Brand: Fragmentation – Community: Clustering – Broadcast: Hub and spoke (In) – Support: Hub and spoke (Out)
  68. 68. [Divided] Polarized Crowds [Unified] Tight 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.
  69. 69. Request your own network map and report http://connectedaction.net
  70. 70. Monitor your topics with social network maps • Identify the – Key people – Groups – Top topics • Locate your social media accounts within the network
  71. 71. 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
  72. 72. 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
  73. 73. A project from the Social Media Research Foundation: http://www.smrfoundation.org Network mapping the social media ecosystem with NodeXL

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