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Charting Collections of
                                                       Connections
                                                     In Social Media:
                                                    Creating Maps &
                                                      Measures with
                                                         NodeXL




A project from the Social Media Research Foundation: http://www.smrfoundation.org
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
Social Media Research Foundation
       http://smrfoundation.org
Social Media
(email, Facebook, Twitter,
YouTube, and more)
is all about
connections

     from people


               to people.

                             4
Patterns are

               left behind
                             5
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
Each contains one or more
                      social networks




World Wide Web
Location, Location, Location
Position, Position, Position
Strong ties
Weak ties
Strength of Weak ties
p://www.flickr.com/photos/fullaperture/81266869/
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.
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.
Like MSPaint™ for graphs.
                    — the Community




Introduction to NodeXL
http://www.flickr.com/photos/badgopher/3264760070/
http://www.flickr.com/photos/druclimb/2212572259/in/photostream/
http://www.flickr.com/photos/hchalkley/47839243/
http://www.flickr.com/photos/rvwithtito/4236716778
http://www.flickr.com/photos/62693815@N03/6277208708/
Social Network Maps Reveal


Key influencers in any topic.

        Sub-groups.

          Bridges.
Hubs
Bridges
http://www.flickr.com/photos/storm-crypt/3047698741
http://www.flickr.com/photos/library_of_congress/3295494976/sizes/o/in/photostream/
http://www.flickr.com/photos/amycgx/3119640267/
Network of connections among “#Debate AND Obama” mentioning Twitter users
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
6 kinds of Twitter social media networks
#My2K




Polarized
#CMgrChat




In-group / Community
Lumia




Brand / Public Topic
#FLOTUS




 Bazaar
New York Times Article
            Paul Krugman




Broadcast: Audience + Communities
Dell Listens/Dellcares




       Support
#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
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
http://www.youtube.com/watch?v=0M3T65Iw3Ac

NodeXL Video
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
Twitter Network for “Microsoft Research”
              *BEFORE*
Twitter Network for “Microsoft Research”
               *AFTER*
Network Motif Simplification




                 Cody Dunne, University of Maryland
NodeXL
Graph Gallery
Now Available
Communities
in Cyberspace
This graph represents a
     directed network of
      1,360 Twitter users
    whose recent tweets
contained "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 graph's
vertices were grouped by
cluster using the Clauset-
 Newman-Moore cluster
    algorithm. The edge
     colors are based on
 relationship values. The
vertex sizes are based on
   each user’s number of
      followers. Table 1
    reports the summary
    network metrics that
      describe the graph.
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
The Vertices spreadsheet lists users who contributed a
       tweet containing the terms “contraception OR
contraceptives” 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, @male
eek, @Change, @foxandfriends, @melindagates, @AshleyJu
                dd, @cnalive, and @SOHLTC.
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
NodeXL calculates
network metrics and
    word pairs
Contrasting groups
The Content summary
 spreadsheet displays the most
frequently used URLs, hashtags,
   and user names within the
 network as a whole and within
   each calculated sub-group.
Contrast hashtags in Groups 2 & 4
Contrasting URL references
Word Pair Contrasts
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 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
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
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
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
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
Charting Collections of
                                                       Connections
                                                     In Social Media:
                                                    Creating Maps &
                                                      Measures with
                                                         NodeXL




A project from the Social Media Research Foundation: http://www.smrfoundation.org
2013 NodeXL Social Media Network Analysis

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2013 NodeXL Social Media Network Analysis

  • 1. Charting Collections of Connections In Social Media: Creating Maps & Measures with NodeXL A project from the Social Media Research Foundation: http://www.smrfoundation.org
  • 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://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
  • 3. Social Media Research Foundation http://smrfoundation.org
  • 4. Social Media (email, Facebook, Twitter, YouTube, and more) is all about connections from people to people. 4
  • 5. Patterns are left behind 5
  • 6. 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
  • 7. “Think Link” Nodes & Edges Is related to A B
  • 8. Each contains one or more social networks World Wide Web
  • 13. Strength of Weak ties p://www.flickr.com/photos/fullaperture/81266869/
  • 14.
  • 15. 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.
  • 16. 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.
  • 17.
  • 18. Like MSPaint™ for graphs. — the Community Introduction to NodeXL
  • 19.
  • 21.
  • 23.
  • 25.
  • 27.
  • 29.
  • 30. Social Network Maps Reveal Key influencers in any topic. Sub-groups. Bridges.
  • 31. Hubs
  • 36. Network of connections among “#Debate AND Obama” mentioning Twitter users
  • 37. 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
  • 38.
  • 39. 6 kinds of Twitter social media networks
  • 44. New York Times Article Paul Krugman Broadcast: Audience + Communities
  • 46. #teaparty 15 November 2011 #occupywallstreet 15 November 2011 http://www.newscientist.com/blogs/onepercent/2011/11/occupy-vs-tea-party-what-their.html
  • 47. 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)
  • 48. 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
  • 49. 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
  • 51. 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
  • 52. Twitter Network for “Microsoft Research” *BEFORE*
  • 53. Twitter Network for “Microsoft Research” *AFTER*
  • 54. Network Motif Simplification Cody Dunne, University of Maryland
  • 58. This graph represents a directed network of 1,360 Twitter users whose recent tweets contained "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 graph's vertices were grouped by cluster using the Clauset- Newman-Moore cluster algorithm. The edge colors are based on relationship values. The vertex sizes are based on each user’s number of followers. Table 1 reports the summary network metrics that describe the graph.
  • 59. 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
  • 60. The Vertices spreadsheet lists users who contributed a tweet containing the terms “contraception OR contraceptives” 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, @male eek, @Change, @foxandfriends, @melindagates, @AshleyJu dd, @cnalive, and @SOHLTC.
  • 61. 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
  • 64. The Content summary spreadsheet displays the most frequently used URLs, hashtags, and user names within the network as a whole and within each calculated sub-group.
  • 65. Contrast hashtags in Groups 2 & 4
  • 68.
  • 71. Example NodeXL data importer for Twitter
  • 72. NodeXL imports “edges” from social media data sources
  • 73. NodeXL displays subgraph images along with network metadata NodeXL creates a list of “vertices” from imported social media edges
  • 74. Perform collections of common operations with NodeXL a single click Automation makes analysis simple and fast
  • 76. NodeXL “Autofill columns” simplifies mapping data attributes to display attributes
  • 77.
  • 79. NodeXL Generates Overall Network Metrics
  • 80.
  • 81.
  • 82. 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
  • 83. 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
  • 84. What we have done: Open Tools • NodeXL • Data providers (“spigots”) – ThreadMill Message Board – Exchange Enterprise Email – Voson Hyperlink – SharePoint – Facebook – Twitter – YouTube – Flickr
  • 85. 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
  • 86. What we have done: Open Scholarship
  • 87. What we have done: Open Scholarship
  • 88. 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
  • 89. 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
  • 90.
  • 91. Who is the mayor of your hashtag? Find out at: http://netbadges.com
  • 92. Who is the mayor of your hashtag? Find out at: http://netbadges.com
  • 93. Who is the mayor of your hashtag? http://netbadges.com Find out at: http://netbadges.com
  • 94. Charting Collections of Connections In Social Media: Creating Maps & Measures with NodeXL A project from the Social Media Research Foundation: http://www.smrfoundation.org

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/badgopher/3264760070/
  4. http://www.flickr.com/photos/library_of_congress/3295494976/sizes/o/in/photostream/
  5. http://www.flickr.com/photos/amycgx/3119640267/
  6. 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.
  7. The network of connections among people who tweeted “#My2K” over the 1-day, 21-hour, 39-minute period from Sunday, 06 January 2013 at 03:30 UTC to Tuesday, 08 January 2013 at 01:09 UTC.
  8. The graph represents a network of 268 Twitter users whose recent tweets contained "#cmgrchat OR #smchat. The network was obtained on Friday, 18 January 2013 at 15:44 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 3-day, 21-hour, 15-minute period from Monday, 14 January 2013 at 18:23 UTC to Friday, 18 January 2013 at 15:38 UTC.
  9. The graph represents a network of 1,227 Twitter users whose recent tweets contained "lumia. The network was obtained on Saturday, 12 January 2013 at 19:52 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 5-hour, 1-minute period from Saturday, 12 January 2013 at 14:36 UTC to Saturday, 12 January 2013 at 19:37 UTC.
  10. The graph represents a network of 1,260 Twitter users whose recent tweets contained "flotus". The network was obtained on Friday, 18 January 2013 at 18:26 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 3-hour, 3-minute period from Friday, 18 January 2013 at 15:16 UTC to Friday, 18 January 2013 at 18:20 UTC.
  11. The graph represents a network of 399 Twitter users whose recent tweets contained "http://www.nytimes.com/2013/01/11/opinion/krugman-coins-against-crazies.html. The network was obtained on Friday, 11 January 2013 at 14:27 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 12-hour, 32-minute period from Friday, 11 January 2013 at 01:52 UTC to Friday, 11 January 2013 at 14:24 UTC.
  12. The graph represents a network of 388 Twitter users whose recent tweets contained "delllistens OR dellcares”. The network was obtained on Tuesday, 19 February 2013 at 17:44 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 6-day, 21-hour, 58-minute period from Tuesday, 12 February 2013 at 19:34 UTC to Tuesday, 19 February 2013 at 17:33 UTC.
  13. Virgin America
  14. Dell Listens and Dell Cares