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Charting Collections of Connections In Social Media: Creating Maps & Measures with NodeXLA project from the Social Media Research Foundation: http://www.smrfoundation.org

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About MeIntroductionsMarc A. SmithChief Social ScientistConnected Action Consulting GroupMarc@connectedaction.nethttp://www.connectedaction.nethttp://www.codeplex.com/nodexlhttp://www.twitter.com/marc_smithhttp://delicious.com/marc_smith/Paperhttp://www.flickr.com/photos/marc_smithhttp://www.facebook.com/marc.smith.sociologisthttp://www.linkedin.com/in/marcasmithhttp://www.slideshare.net/Marc_A_Smithhttp://www.smrfoundation.org

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Social Media Research Foundation http://smrfoundation.org

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Social Media(email, Facebook, Twitter,YouTube, and more)is all aboutconnections from people to people. 4

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Patterns are left behind 5

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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

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“Think Link” Nodes & Edges Is related toA B

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Each contains one or more social networksWorld Wide Web

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Location, Location, Location

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Position, Position, Position

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Strong ties

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Weak ties

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Strength of Weak tiesp://www.flickr.com/photos/fullaperture/81266869/

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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.

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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.

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Like MSPaint™ for graphs. — the CommunityIntroduction to NodeXL

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http://www.flickr.com/photos/badgopher/3264760070/

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http://www.flickr.com/photos/druclimb/2212572259/in/photostream/

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http://www.flickr.com/photos/hchalkley/47839243/

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http://www.flickr.com/photos/rvwithtito/4236716778

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http://www.flickr.com/photos/62693815@N03/6277208708/

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Social Network Maps RevealKey influencers in any topic. Sub-groups. Bridges.

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Hubs

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Bridges

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http://www.flickr.com/photos/storm-crypt/3047698741

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http://www.flickr.com/photos/library_of_congress/3295494976/sizes/o/in/photostream/

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http://www.flickr.com/photos/amycgx/3119640267/

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Network of connections among “#Debate AND Obama” mentioning Twitter users

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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

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6 kinds of Twitter social media networks

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#My2KPolarized

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#CMgrChatIn-group / Community

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LumiaBrand / Public Topic

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#FLOTUS Bazaar

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New York Times Article Paul KrugmanBroadcast: Audience + Communities

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Dell Listens/Dellcares Support

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#teaparty 15 November 2011#occupywallstreet15 November 2011http://www.newscientist.com/blogs/onepercent/2011/11/occupy-vs-tea-party-what-their.html

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Social Network Theoryhttp://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)

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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

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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. http://nodexl.codeplex.com

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http://www.youtube.com/watch?v=0M3T65Iw3AcNodeXL Video

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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

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Twitter Network for “Microsoft Research” *BEFORE*

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Twitter Network for “Microsoft Research” *AFTER*

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Network Motif Simplification Cody Dunne, University of Maryland

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NodeXLGraph Gallery

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Now Available

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Communitiesin Cyberspace

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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.

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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

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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.

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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

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NodeXL calculatesnetwork metrics and word pairs

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Contrasting groups

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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.

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Contrast hashtags in Groups 2 & 4

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Contrasting URL references

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Word Pair Contrasts

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NodeXL Ribbon in Excel

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NodeXL data import sources

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Example NodeXL data importer for Twitter

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NodeXL imports “edges” from social media data sources

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NodeXL displays subgraph images along with network metadataNodeXL creates a list of “vertices” from imported social media edges

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Perform collections of common operations with NodeXL a single click Automationmakes analysissimple and fast

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NodeXL Network Metrics

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NodeXL “Autofill columns” simplifies mapping data attributes to display attributes

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NodeXL enables filtering of networks

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NodeXL Generates Overall Network Metrics

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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

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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

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What we have done: Open Tools• NodeXL• Data providers (“spigots”) – ThreadMill Message Board – Exchange Enterprise Email – Voson Hyperlink – SharePoint – Facebook – Twitter – YouTube – Flickr

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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

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What we have done: Open Scholarship

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What we have done: Open Scholarship

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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

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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

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

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

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

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Charting Collections of Connections In Social Media: Creating Maps & Measures with NodeXLA project from the Social Media Research Foundation: http://www.smrfoundation.org