Analyzing Social Media Networks with NodeXL Marc A. Smith

1. Analyzing Social Media Networks with NodeXL Marc A. Smith Chief Social ScientistConnected Action Consulting Group marc@connectedaction.net http://www.connectedaction.net http://www.codeplex.com/nodexl

3. The NodeXL Project Team

4. 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

5. 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), • betweenness • Methods • Surveys, interviews, observations, log file analysis, computational analysis of matrices (Hampton &Wellman, 1999; Paolillo, 2001; Wellman, 2001) • Source: Richards, W. (1986). The NEGOPY network analysis program. Burnaby, BC: Department of Communication, Simon Fraser University. pp.7-16

6. Social Networks • History: from the dawn of time! • Theory and method: 1934 -> • Jacob L. Moreno • http://en.wikipedia.org/wiki/Jacob_L._Moreno

7. SNA 101 • Node • “actor” on which relationships act; 1-mode versus 2-mode networks • Edge • Relationship connecting nodes; can be directional • Cohesive Sub-Group • Well-connected group; clique; cluster • Key Metrics • Centrality (group or individual measure) • Number of direct connections that individuals have with others in the group (usually look at incoming connections only) • 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) • # shortest paths between each node pair that a node is on • Measure at the individual node level • Node roles • Peripheral – below average centrality • Central connector – above average centrality • Broker – above average betweenness A B C A B D E D E G F C D H I E

8. Mapping Newsgroup Social Ties Microsoft.public.windowsxp.server.general Two “answer people” with an emerging 3rd.

9. Distinguishing attributes of online social roles • Answer person • Outward ties to local isolates • Relative absence of triangles • Few intense ties • Reply Magnet • Ties from local isolates often inward only • Sparse, few triangles • Few intense ties

10. Distinguishing attributes: • Answer person • Outward ties to local isolates • Relative absence of triangles • Few intense ties • Discussion person • Ties from local isolates often inward only • Dense, many triangles • Numerous intense ties

11. Introduction to NodeXL

12. NodeXL: Network Overview, Discovery and Exploration for Excel Leverage spreadsheet for storage of edge and vertex data http://www.codeplex.com/nodexl

14. #WIN09

15. NodeXL Video http://www.youtube.com/watch?v=0M3T65Iw3Ac

16. NodeXL Free/Open Social Network Analysis add-in for Excel 2007 makes graph theory as easy as a bar chart, integrated analysis of social media sources. http://nodexl.codeplex.com

17. Import data from a variety of SNA and Social Media data sources

18. NodeXL Network Metrics

19. NodeXLNetwork Overview Discovery and Exploration add-in for Excel 2007 Heather has high betweeness A minimal network can illustrate the ways different locations have different values for centrality and degree Diane has high degree

20. NodeXL: Display nodes with subgraph images sorted by network attributes using Excel Data|Sort

21. “SAP” mentioning twitter users

22. “SAP” mentioning twitter users Size = Followers Edge = # relationship ties

23. The NodeXL project is Available via the CodePlex Open Source Project Hosting Site:http://www.codeplex.com/nodexl

24. Display community members sorted by network attributes using Excel Data|Sort

25. Summary network metrics Displayed on “Overall Metrics” tab

26. Map data to display attributes

27. Dynamic Filters Now feature Metrics histograms

28. Import from flickr tag and user networks

29. Network Clusters visualization showing three Flickr tag clusters,each representing a different context for “mouse”.

30. Isolate clusters showing three different contexts for the “mouse” tag in Flickr: mouse animal, computer mouse, and Mickey Mouse character.

31. NodeXL Network of Flickr users who comment onMarc_Smith’s photos (network depth 1.5; edge weight ≥ 4).

32. Import data from Twitter user and term networks

33. Book forthcoming: Analyzing social media networks with NodeXL: Insights from a connected world NodeXL Tutorial http://casci.umd.edu/

34. Social media network archives • On-going collection • Additional sources: enterprise/consumer • More metrics • Performance • Cross-platform/Web • Clustering • Time series analysis

35. Analyzing Social Media Networks with NodeXL Marc A. Smith Chief Social ScientistConnected Action Consulting Group marc@connectedaction.net http://www.connectedaction.net http://www.codeplex.com/nodexl

39. AnswerPersonSignatures Discussion People

40. Discussion Starter Spammer Reply orientedDiscussion Flame Warrior