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20111103 con tech2011-marc smith
The document outlines the Nodexl project from the Social Media Research Foundation, which aims to simplify social network analysis (SNA) using familiar tools like spreadsheets. It discusses various social media connections and provides insights into key metrics such as centrality, cohesion, and betweenness. The project seeks to create open tools and data for researchers, making social media network visualization more accessible.
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- 1. Charting Collections of Connections in Social Media: Creating Maps and Measures with NodeXL A project from the Social Media Research Foundation: http://www.smrfoundation.org
- 2. 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
- 3. Social Media (email, Facebook,Twitter, YouTube, and more) is all about connections from people to people. 3
- 4. Patterns are left behind 4
- 5. There are manykinds of ties…. Like, Link, Reply, Rate, Review, Favorite, Friend, Follow, Edit, Tag, Comment… http://www.flickr.com/photos/stevendepolo/3254238329
- 6. Each contains oneor more social networks World Wide Web
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- 13. 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)
- 14. 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
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- 17. 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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- 19. Analogy: Clusters AreOccluded Hard to count nodes, clusters
- 20. Separate Clusters AreMore Comprehensible
- 21. Twitter Network for“Microsoft Research” *BEFORE*
- 22. Twitter Network for“Microsoft Research” *AFTER*
- 23. Goal: Make SNAeasier • 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
- 24. 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
- 25. Social Media ResearchFoundation http://smrfoundation.org
- 26. 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
- 27. What we havedone: Open Tools • NodeXL • Data providers (“spigots”) – ThreadMill Message Board – Exchange Enterprise Email – Voson Hyperlink – SharePoint – Facebook – Twitter – YouTube – Flickr
- 28. 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
- 29. What we havedone: Open Scholarship
- 30. What we havedone: Open Scholarship
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- 32. Twitter Networks: connectionsamong the people who tweeted the term “Kpop” on 24 October 2011
- 33. NodeXL data importsources
- 34. Example NodeXL dataimporter for Twitter
- 35. NodeXL imports “edges”from social media data sources
- 36. NodeXL Automation makesanalysis simple and fast
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- 38. NodeXL simplifies mappingdata attributes to display attributes
- 39. NodeXL displays subgraphimages along with network metadata
- 40. NodeXL enables filteringof networks
- 41. NodeXL Generates OverallNetwork Metrics
- 42. 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
- 43. Work Items Autofill GroupAttribute 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
- 44. 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
- 45. 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
- 46. Charting Collections of Connections in Social Media: Creating Maps and Measures with NodeXL A project from the Social Media Research Foundation: http://www.smrfoundation.org