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20120301 strata-marc smith-mapping social media networks with no coding using node xl
NodeXL is a free and open social network analysis add-in for Excel that makes analyzing social media networks as easy as making a pie chart. It allows users to import social media network data from various sources, visualize the resulting networks, and perform automated network analysis and metrics calculations with just a few clicks. The tool aims to open up social network analysis to more users by simplifying the traditionally complex processes of data collection, network mapping, and metrics calculation.
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20120301 strata-marc smith-mapping social media networks with no coding using node xl
- 2. Mapping social media networks (with no coding) using NodeXL A project from the Social Media Research Foundation: http://www.smrfoundation.org
- 3. Social Media ResearchFoundation http://smrfoundation.org
- 4. Social Media ResearchFoundation 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
- 5. 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
- 6. What we aretrying 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
- 7. What we havedone: Open Tools • NodeXL • Data providers (“spigots”) – ThreadMill Message Board – Exchange Enterprise Email – Voson Hyperlink – SharePoint – Facebook – Twitter – YouTube – Flickr
- 8. What we havedone: 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
- 9. What we havedone: Open Scholarship
- 10. What we havedone: Open Scholarship
- 11. We envision hundredsof 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
- 12. Social Media (email, Facebook,Twitter, YouTube, and more) is all about connections from people to people. 12
- 13. Patterns are left behind 13
- 14. There are manykinds of ties…. Like, Link, Reply, Rate, Review, Favorite, Friend, Follow, Forward, Edit, Tag, Comment, Check-in… http://www.flickr.com/photos/stevendepolo/3254238329
- 15. “Think Link” Nodes & Edges Is related to A B
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- 18. 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.
- 19. Each contains oneor more social networks World Wide Web
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- 26. Like MSPaint™ forgraphs. — the Community Introduction to NodeXL
- 27. NodeXL Network Overview Discoveryand Exploration add-in for Excel 2007/2010 A minimal network can illustrate the ways different locations have different values for centrality and degree
- 28. #teaparty 15 November 2011 #occupywallstreet 15 November 2011 http://www.newscientist.com/blogs/onepercent/2011/11/occupy-vs-tea-party-what-their.html
- 29. 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)
- 30. 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
- 31. 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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- 34. NodeXL Free/Open SocialNetwork 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
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- 37. Twitter Network for“Microsoft Research” *BEFORE*
- 38. Twitter Network for“Microsoft Research” *AFTER*
- 39. NodeXL Ribbon inExcel
- 40. NodeXL data importsources
- 41. Example NodeXL dataimporter for Twitter
- 42. NodeXL imports “edges”from social media data sources
- 43. NodeXL displays subgraphimages along with network metadata NodeXL creates a list of “vertices” from imported social media edges
- 44. Perform collections of common operations with NodeXL a single click Automation makes analysis simple and fast
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- 46. NodeXL “Autofill columns”simplifies mapping data attributes to display attributes
- 48. NodeXL enables filteringof networks
- 49. NodeXL Generates OverallNetwork Metrics
- 56. Social Network MapsReveal Key influencers in any topic. Sub-groups. Bridges.
- 57. What we wantto 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
- 58. 2012 Schedule: PlannedWorkshops March 1 - Strata March 5 2012 – PAWCON June 2012 - ICWSM July 2012 – Lipari School on Complexity August 8, 2012 - AEJMC August 21, 2012 – Webshop 2012
- 59. Pending Work Items AutofillGroup Attribute Merge Edges by Attribute Modal Transform Merge Workbooks Automated Dynamic Filters: Time Series Analysis, contrast Captions and Legends Upload to Graph Gallery++: captions, workbook Graph Gallery++ User Accounts, Reporting, RSS Feeds, Network Visualization Web Canvas Import: RDF, Wiki, SharePoint, Keyword networks from text Metrics: Triad Census Layouts: Force Atlas 2, Lin Log, “Bakshy Plots”, Quality Measures Query-by-example search for network structures
- 60. How you canhelp • 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
- 61. Thank you! The SocialMedia Research Foundation http://www.smrfoundation.org
- 62. Backup • Examples ofsocial media network analysis • Sources of social network analysis material
- 63. Who is themayor of your hashtag? Find out at: http://netbadges.com
- 64. Who is themayor of your hashtag? Find out at: http://netbadges.com
- 65. Who is themayor of your hashtag? http://netbadges.com Find out at: http://netbadges.com
Editor's Notes
- #17 http://www.flickr.com/photos/lizjones/1571656758/sizes/o/
- #18 http://www.flickr.com/photos/kjander/3123883124/sizes/o/
- #24 http://www.flickr.com/photos/library_of_congress/3295494976/sizes/o/in/photostream/
- #25 http://www.flickr.com/photos/amycgx/3119640267/
- #28 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.
- #53 Virgin America
- #54 Dell Listens and Dell Cares