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Mapping social
                                                   media networks
                                                   (with no coding)
                                                    using NodeXL




A project from the Social Media Research Foundation: http://www.smrfoundation.org
Social Media Research Foundation
       http://smrfoundation.org
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
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://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
http://www.smrfoundation.org
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
What we have done: Open Tools
• NodeXL
• Data providers (“spigots”)
  –   ThreadMill Message Board
  –   Exchange Enterprise Email
  –   Voson Hyperlink
  –   SharePoint
  –   Facebook
  –   Twitter
  –   YouTube
  –   Flickr
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
What we have done: Open Scholarship
What we have done: Open Scholarship
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
Social Media
(email, Facebook, Twitter,
YouTube, and more)
is all about
connections

     from people


               to people.

                             12
Patterns are

               left behind
                             13
There are many kinds of ties….
Like, Link, Reply, Rate, Review, Favorite, Friend, Follow, Forward, Edit, Tag, Comment, Check-in…




                                      http://www.flickr.com/photos/stevendepolo/3254238329
“Think Link”
    Nodes & Edges


        Is related to




A                       B
Strong ties
Weak ties
Social
   Networks
• History:
  from the
  dawn of
  time!
• Theory and
  method:
  1934 ->
• Jacob L.
  Moreno
• http://en.wiki
  pedia.org/wiki
  /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.
Each contains one or more
                      social networks




World Wide Web
Hubs
Bridges
http://www.flickr.com/photos/library_of_congress/3295494976/sizes/o/in/photostream/
http://www.flickr.com/photos/amycgx/3119640267/
Like MSPaint™ for graphs.
                    — the Community




Introduction to NodeXL
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
#teaparty
                                                                       15 November 2011


#occupywallstreet
15 November 2011




http://www.newscientist.com/blogs/onepercent/2011/11/occupy-vs-tea-party-what-their.html
Social Network Theory
http://en.wikipedia.org/wiki/Social_network
• 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)
SNA 101
                                • Node
                A
                                   – “actor” on which relationships act; 1-mode versus 2-mode networks
                                • Edge
B                                  – 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
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
http://www.flickr.com/photos/marc_smith/sets/72157622437066929/
NodeXL
 Free/Open Social Network Analysis add-in for Excel 2007/2010 makes graph
theory as easy as a pie chart, with integrated analysis of social media sources.
                          http://nodexl.codeplex.com
Now Available
Communities
in Cyberspace
Twitter Network for “Microsoft Research”
              *BEFORE*
Twitter Network for “Microsoft Research”
               *AFTER*
NodeXL Ribbon in Excel
NodeXL data import sources
Example NodeXL data importer for Twitter
NodeXL imports “edges” from social media data sources
NodeXL displays subgraph images along with network metadata




NodeXL creates a list of “vertices” from imported social media edges
Perform
                   collections of
                     common
                  operations with
    NodeXL         a single click

  Automation
makes analysis
simple and fast
NodeXL Network Metrics
NodeXL “Autofill columns” simplifies mapping data attributes to display attributes
NodeXL enables filtering of networks
NodeXL Generates Overall Network Metrics
Social Network Maps Reveal


Key influencers in any topic.

        Sub-groups.

          Bridges.
What we want to do:
(Build the tools to) map the social web
• Move NodeXL to the web:
   – Node for Google Doc Spreadsheets!
   – WebGL Canvas
• 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
2012 Schedule: Planned Workshops
March 1 - Strata
March 5 2012 – PAWCON
June 2012 - ICWSM
July 2012 – Lipari School on Complexity
August 8, 2012 - AEJMC
August 21, 2012 – Webshop 2012
Pending Work Items
Autofill Group Attribute
Merge Edges by Attribute
Modal Transform
Merge Workbooks
Automated Dynamic Filters: Time Series Analysis, contrast
Captions and Legends
Upload to Graph Gallery++: captions, workbook
Graph Gallery++
   User Accounts, Reporting, RSS Feeds,
   Network Visualization Web Canvas
Import: RDF, Wiki, SharePoint, Keyword networks from text
Metrics: Triad Census
Layouts:
   Force Atlas 2, Lin Log, “Bakshy Plots”, Quality Measures
Query-by-example search for network structures
How you can help
• Sponsor a feature
• Sponsor Webshop 2012
• 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
Thank you!

The Social Media Research Foundation


http://www.smrfoundation.org
Backup
• Examples of social media network analysis
• Sources of social network analysis material
Who is the mayor of your hashtag?




                   Find out at: http://netbadges.com
Who is the mayor of your hashtag?




                                    Find out at: http://netbadges.com
Who is the mayor of your hashtag?
         http://netbadges.com




                                Find out at: http://netbadges.com

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20120301 strata-marc smith-mapping social media networks with no coding using node xl

  • 1.
  • 2. Mapping social media networks (with no coding) using NodeXL A project from the Social Media Research Foundation: http://www.smrfoundation.org
  • 3. Social Media Research Foundation http://smrfoundation.org
  • 4. 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
  • 5. 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://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 http://www.smrfoundation.org
  • 6. 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
  • 7. What we have done: Open Tools • NodeXL • Data providers (“spigots”) – ThreadMill Message Board – Exchange Enterprise Email – Voson Hyperlink – SharePoint – Facebook – Twitter – YouTube – Flickr
  • 8. 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
  • 9. What we have done: Open Scholarship
  • 10. What we have done: Open Scholarship
  • 11. 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
  • 12. Social Media (email, Facebook, Twitter, YouTube, and more) is all about connections from people to people. 12
  • 13. Patterns are left behind 13
  • 14. There are many kinds of ties…. Like, Link, Reply, Rate, Review, Favorite, Friend, Follow, Forward, Edit, Tag, Comment, Check-in… http://www.flickr.com/photos/stevendepolo/3254238329
  • 15. “Think Link” Nodes & Edges Is related to A B
  • 18. Social Networks • History: from the dawn of time! • Theory and method: 1934 -> • Jacob L. Moreno • http://en.wiki pedia.org/wiki /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.
  • 19. Each contains one or more social networks World Wide Web
  • 20.
  • 21. Hubs
  • 25.
  • 26. Like MSPaint™ for graphs. — the Community Introduction to NodeXL
  • 27. 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
  • 28. #teaparty 15 November 2011 #occupywallstreet 15 November 2011 http://www.newscientist.com/blogs/onepercent/2011/11/occupy-vs-tea-party-what-their.html
  • 29. Social Network Theory http://en.wikipedia.org/wiki/Social_network • 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)
  • 30. SNA 101 • Node A – “actor” on which relationships act; 1-mode versus 2-mode networks • Edge B – 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
  • 31. 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
  • 32.
  • 34. NodeXL Free/Open Social Network Analysis add-in for Excel 2007/2010 makes graph theory as easy as a pie chart, with integrated analysis of social media sources. http://nodexl.codeplex.com
  • 37. Twitter Network for “Microsoft Research” *BEFORE*
  • 38. Twitter Network for “Microsoft Research” *AFTER*
  • 41. Example NodeXL data importer for Twitter
  • 42. NodeXL imports “edges” from social media data sources
  • 43. NodeXL displays subgraph images along with network metadata NodeXL creates a list of “vertices” from imported social media edges
  • 44. Perform collections of common operations with NodeXL a single click Automation makes analysis simple and fast
  • 46. NodeXL “Autofill columns” simplifies mapping data attributes to display attributes
  • 47.
  • 49. NodeXL Generates Overall Network Metrics
  • 50.
  • 51.
  • 52.
  • 53.
  • 54.
  • 55.
  • 56. Social Network Maps Reveal Key influencers in any topic. Sub-groups. Bridges.
  • 57. What we want to do: (Build the tools to) map the social web • Move NodeXL to the web: – Node for Google Doc Spreadsheets! – WebGL Canvas • 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
  • 58. 2012 Schedule: Planned Workshops March 1 - Strata March 5 2012 – PAWCON June 2012 - ICWSM July 2012 – Lipari School on Complexity August 8, 2012 - AEJMC August 21, 2012 – Webshop 2012
  • 59. Pending Work Items Autofill Group Attribute Merge Edges by Attribute Modal Transform Merge Workbooks Automated Dynamic Filters: Time Series Analysis, contrast Captions and Legends Upload to Graph Gallery++: captions, workbook Graph Gallery++ User Accounts, Reporting, RSS Feeds, Network Visualization Web Canvas Import: RDF, Wiki, SharePoint, Keyword networks from text Metrics: Triad Census Layouts: Force Atlas 2, Lin Log, “Bakshy Plots”, Quality Measures Query-by-example search for network structures
  • 60. How you can help • Sponsor a feature • Sponsor Webshop 2012 • 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. Thank you! The Social Media Research Foundation http://www.smrfoundation.org
  • 62. Backup • Examples of social media network analysis • Sources of social network analysis material
  • 63. Who is the mayor of your hashtag? Find out at: http://netbadges.com
  • 64. Who is the mayor of your hashtag? Find out at: http://netbadges.com
  • 65. Who is the mayor of your hashtag? http://netbadges.com Find out at: http://netbadges.com

Editor's Notes

  1. http://www.flickr.com/photos/lizjones/1571656758/sizes/o/
  2. http://www.flickr.com/photos/kjander/3123883124/sizes/o/
  3. http://www.flickr.com/photos/library_of_congress/3295494976/sizes/o/in/photostream/
  4. http://www.flickr.com/photos/amycgx/3119640267/
  5. 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.
  6. Virgin America
  7. Dell Listens and Dell Cares