LSS'11: Charting Collections Of Connections In Social Media


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Keynote Title: Charting Collections of Connections in Social Media: Creating Maps and Measures with NodeXL

Abstract: Networks are a data structure common found across all social media services that allow populations to author collections of connections. The Social Media Research Foundation‘s NodeXL project makes analysis of social media networks accessible to most users of the Excel spreadsheet application. With NodeXL, Networks become as easy to create as pie charts. Applying the tool to a range of social media networks has already revealed the variations present in online social spaces. A review of the tool and images of Twitter, flickr, YouTube, and email networks will be presented.

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  • LSS'11: Charting Collections Of Connections In Social Media

    1. 1. Charting Collections of Connections in Social Media: Creating Maps and Measures with NodeXLA project from the Social Media Research Foundation:
    2. 2. About MeIntroductionsMarc A. SmithChief Social ScientistConnected Action Consulting GroupMarc@connectedaction.nethttp://www.connectedaction.net
    3. 3. Social Media(email, Facebook, Twitter,YouTube, and more)is all aboutconnections from people to people. 3
    4. 4. Patterns are leftbehind 4
    5. 5. There are many kinds of ties…. Like, Link, Reply, Rate, Review, Favorite, Friend, Follow, Edit, Tag, Comment…
    6. 6. Each contains one or more social networksWorld Wide Web
    7. 7. Hubs
    8. 8. Bridges
    9. 9.
    10. 10.
    11. 11. Social Network Theory• 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)
    12. 12. 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
    13. 13.
    14. 14. 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
    15. 15. Now Available
    16. 16. Analogy: Clusters Are Occluded Hard to count nodes, clusters
    17. 17. Separate Clusters Are More Comprehensible
    18. 18. Twitter Network for “Microsoft Research” *BEFORE*
    19. 19. Twitter Network for “Microsoft Research” *AFTER*
    20. 20. 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
    21. 21. 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
    22. 22. Social Media Research Foundation
    23. 23. 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
    24. 24. What we have done: Open Tools• NodeXL• Data providers (“spigots”) – ThreadMill Message Board – Exchange Enterprise Email – Voson Hyperlink – SharePoint – Facebook – Twitter – YouTube – Flickr
    25. 25. What we have done: Open Data• – 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
    26. 26. What we have done: Open Scholarship
    27. 27. What we have done: Open Scholarship
    28. 28. Facebook networks
    29. 29. Twitter Networks: connections among the people who tweeted the term“Kpop” on 24 October 2011
    30. 30. NodeXL data import sources
    31. 31. Example NodeXL data importer for Twitter
    32. 32. NodeXL imports “edges” from social media data sources
    33. 33. NodeXL Automation makes analysis simple and fast
    34. 34. NodeXL Network Metrics
    35. 35. NodeXL simplifies mapping data attributes to display attributes
    36. 36. NodeXL displays subgraph images along with network metadata
    37. 37. NodeXL enables filtering of networks
    38. 38. NodeXL Generates Overall Network Metrics
    39. 39. 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
    40. 40. 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
    41. 41. 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
    42. 42. Contact: Marc A. Smith Chief Social Scientist Connected Action Consulting Group
    43. 43. Charting Collections of Connections in Social Media: Creating Maps and Measures with NodeXLA project from the Social Media Research Foundation: