LSS'11: Charting Collections Of Connections In Social Media

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

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

Social Media(email, Facebook, Twitter,YouTube, and more)is all aboutconnections from people to people. 3

Patterns are leftbehind 4

There are many kinds of ties…. Like, Link, Reply, Rate, Review, Favorite, Friend, Follow, Edit, Tag, Comment… http://www.flickr.com/photos/stevendepolo/3254238329

Each contains one or more social networksWorld Wide Web

Bridges

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

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

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)

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

http://www.flickr.com/photos/marc_smith/sets/72157622437066929/

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

Now Available

Analogy: Clusters Are Occluded Hard to count nodes, clusters

Separate Clusters Are More Comprehensible

Twitter Network for “Microsoft Research” *BEFORE*

Twitter Network for “Microsoft Research” *AFTER*

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

Who we are 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

Social Media Research Foundation http://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

Facebook networkshttp://www.connectedaction.net/2010/04/25/bernie-hogans-facebook-social-network-data-provider-and-visualization-toolkit/

Twitter Networks: connections among the people who tweeted the term“Kpop” on 24 October 2011

NodeXL data import sources

Example NodeXL data importer for Twitter

NodeXL imports “edges” from social media data sources

NodeXL Automation makes analysis simple and fast

NodeXL Network Metrics

NodeXL simplifies mapping data attributes to display attributes

NodeXL displays subgraph images along with network metadata

NodeXL enables filtering of networks

NodeXL Generates Overall Network Metrics

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

Work ItemsAutofill Group AttributeMerge Edges by AttributeModal TransformMerge WorkbooksAutomated Dynamic Filters: Time Series Analysis, contrastCaptions and LegendsUpload to Graph Gallery++: captions, workbookGraph Gallery++ User Accounts, Reporting, RSS Feeds, Network Visualization Web CanvasImport: RDF, Wiki, SharePoint, Keyword networks from textMetrics: Triad CensusLayouts: Force Atlas 2, Lin Log, “Bakshy Plots”, Quality MeasuresQuery-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, d ata or employee’s time• Help promote the work of the Social Media Research Foundation

Contact: 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

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

LSS'11: Charting Collections Of Connections In Social Media

by Local Social Summit on Nov 15, 2011

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LSS’11: Charting Collections Of Connections In Social Media — Presentation Transcript