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


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Mapping social media networks with no coding using NodeXL - presented at Strata 2012

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

    1. 1. Mapping social media networks (with no coding) using NodeXLA project from the Social Media Research Foundation: http://www.smrfoundation.org
    2. 2. Social Media Research Foundation http://smrfoundation.org
    3. 3. 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
    4. 4. 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
    5. 5. 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
    6. 6. What we have done: Open Tools• NodeXL• Data providers (“spigots”) – ThreadMill Message Board – Exchange Enterprise Email – Voson Hyperlink – SharePoint – Facebook – Twitter – YouTube – Flickr
    7. 7. 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
    8. 8. What we have done: Open Scholarship
    9. 9. What we have done: Open Scholarship
    10. 10. 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
    11. 11. Social Media(email, Facebook, Twitter,YouTube, and more)is all aboutconnections from people to people. 12
    12. 12. Patterns are left behind 13
    13. 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
    14. 14. “Think Link” Nodes & Edges Is related toA B
    15. 15. Strong ties
    16. 16. Weak ties
    17. 17. 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.
    18. 18. Each contains one or more social networksWorld Wide Web
    19. 19. Hubs
    20. 20. Bridges
    21. 21. http://www.flickr.com/photos/library_of_congress/3295494976/sizes/o/in/photostream/
    22. 22. http://www.flickr.com/photos/amycgx/3119640267/
    23. 23. Like MSPaint™ for graphs. — the CommunityIntroduction to NodeXL
    24. 24. 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
    25. 25. #teaparty 15 November 2011#occupywallstreet15 November 2011http://www.newscientist.com/blogs/onepercent/2011/11/occupy-vs-tea-party-what-their.html
    26. 26. 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)
    27. 27. 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
    28. 28. 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
    29. 29. http://www.flickr.com/photos/marc_smith/sets/72157622437066929/
    30. 30. 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
    31. 31. Now Available
    32. 32. Communitiesin Cyberspace
    33. 33. Twitter Network for “Microsoft Research” *BEFORE*
    34. 34. Twitter Network for “Microsoft Research” *AFTER*
    35. 35. NodeXL Ribbon in Excel
    36. 36. NodeXL data import sources
    37. 37. Example NodeXL data importer for Twitter
    38. 38. NodeXL imports “edges” from social media data sources
    39. 39. NodeXL displays subgraph images along with network metadataNodeXL creates a list of “vertices” from imported social media edges
    40. 40. Perform collections of common operations with NodeXL a single click Automationmakes analysissimple and fast
    41. 41. NodeXL Network Metrics
    42. 42. NodeXL “Autofill columns” simplifies mapping data attributes to display attributes
    43. 43. NodeXL enables filtering of networks
    44. 44. NodeXL Generates Overall Network Metrics
    45. 45. Social Network Maps RevealKey influencers in any topic. Sub-groups. Bridges.
    46. 46. 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
    47. 47. 2012 Schedule: Planned WorkshopsMarch 1 - StrataMarch 5 2012 – PAWCONJune 2012 - ICWSMJuly 2012 – Lipari School on ComplexityAugust 8, 2012 - AEJMCAugust 21, 2012 – Webshop 2012
    48. 48. Pending 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
    49. 49. 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
    50. 50. Thank you!The Social Media Research Foundationhttp://www.smrfoundation.org
    51. 51. Backup• Examples of social media network analysis• Sources of social network analysis material
    52. 52. Who is the mayor of your hashtag? Find out at: http://netbadges.com
    53. 53. Who is the mayor of your hashtag? Find out at: http://netbadges.com
    54. 54. Who is the mayor of your hashtag? http://netbadges.com Find out at: http://netbadges.com
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