1. Mapping Your Digital Audiences Nicole Fernandez, Georgetown University @GeorgetownCCT Erin Gamble, ACDI/VOCA @eringam Charrosé King, ACDI/VOCA @charroseck March 6, 2015 #15NTC #15NTCdigmap

2. Housekeeping #15NTCdigmap Collaboration Notes: http://po.st/mEKWpR http://po.st/mEKWpR Evaluation Survey: http://po.st/HHuNu5 http://po.st/HHuNu5

3. NodeXL—free tool Download it here: http://nodexl.codeplex.com/

4. Who We Are Nicole Fernandez Adjunct Lecturer for Georgetown University’s Communication, Culture, and Technology Erin Gamble Online Media Director for ACDI/VOCA Charrosé King Online Media Coordinator for ACDI/VOCA

5. Why is mapping important? Image source: Wikimedia Commons

6. What can I expect today? Intro to Social Network Analysis Case study examples Tool demonstrations Further resources

7. What is Social Network Analysis?

8. Social Network Analysis (SNA) Social network analysis is not just about Facebook.

9. Social Network Analysis (SNA) SNA is a way to look at relationship information and interactions.

10. Social Network Analysis Applying graph theory to sociological studies Nodes Links Flow

11. SNA Graph

12. SNA Graph and Matrix = ABCD A-110 B1-11 C11-0 D010-

13. Metrics Overview

14. What is does your graph represent? What are the nodes? What are the edges?

15. An Undirected Graph Task: Determine Degree

16. Deg(A) = 4 Deg (B) = 2

17. Directed Graph

18. INDEGREEOUTDEGREE S21 N22 R01 T11 L11

19. Density

20. Density: First Ask How Many Possible Relationships?

21. Density = Actual Ties / Possible Ties

22. This graph has a Density of.4

23. Paths: Get from A to D Walk ACED Length 3 Walk ABD Length 2 Walk AD Length 1 This is our Shortest Path

24. Shortest Paths are needed so we can calculate closeness centrality

25. Closeness Centrality Node A vs. Node C

26. Graph Metrics VertexDegree In- Degree Out- Degree Betweenness Centrality Closeness Centrality A 0.111 B 0.167 C 0.200 D 0.125 E

27. Closeness Centrality of Node A ToBCDE From A ABABCABCDABCE Length1233

28. Closeness Centrality as Average Shortest Path Length 1.Identify Shortest Paths to all other nodes 2.Identify the length of those paths 3.Average those lengths

29. Closeness Centrality of Node A = 2.25 ToBCDE From A ABABCABCDABCE Length1233

30. Betweenness Centrality Potential Measure of Brokerage For a particular node, how many shortest paths is that node inside?

31. ABCDE A ABABCABCDABCE B BCBCDBCE C CDCE D DCE E

32. What is the Betweenness Centrality of Node B?

33. Focus on Node B ABCDE A ABABCABCDABCE B BCBCDBCE C CDCE D DCE E

34. Graph Metrics VertexDegree In- Degree Out- Degree Betweenness Centrality A 0.000 B 3.000 C 5.000 D 0.000 E

35. Eigenvector Centrality Not just who you are connected to, but who are they connected to?

36. Eigenvector Centrality is a way to distinguish nodes with equal degree.

37. Compare Nodes Y and Z

38. Both Have Degree 4

39. Node Y is Connected to a More Connected Node

40. NodeXL will calculate it for you!

41. Practical Applications: Context Matters

42. Recognize the Impact of Missing Data

43. Social Media Context Directed Graphs Provide Additional Detail

44. Mapping Examples Let’s dig into some case studies

45. Case Study: Girls Inc. DC Question: Who should Girls Inc. DC interact with on Twitter?

46. #StrongSmartBold February 2-7, 2015

47. #StrongSmartBold February 9-12, 2015

48. #STEM and #Girls

49. Case Study: Fundraising Question: What insights can we gain from mapping online contributions?

50. Kenya Fundraising

51. Egypt Fundraising

52. Campaign Overlap

54. Case Study: Online Audience Analysis Question: How does our online audience connect to ACDI/VOCA?

55. Survey of External Audience

56. Methodology for SNA Component Pulled data from external survey questionnaire Created edge lists Used NodeXL to analyze connections Used SPSS (statistical analysis software) to create cross- tabulations

57. Findings Bipartite graph without isolates (i.e., without “No” responses)

58. Findings Website * Enewsletter Crosstabulation Count EnewsletterTotal B_Enewsletter_YesNo Website A_Website_Yes185300485 No10735142 Total292335627

59. Findings Twitter * Facebook Crosstabulation Count FacebookTotal D_Facebook_YesNo Twitter C_Twitter_Yes4812 No29586615 Total33594627

60. Outcomes »Shared findings with colleagues »Developed outreach strategies »Repeated cycle: issued new survey  analyze findings  strategize and optimize outreach

61. Practical Steps: Using NodeXL

65. Include Attributes on the Vertices Page

70. NodeXL demo Which NTCs have you been to? D.C. Minneapolis San Francisco Atlanta

71. What do I do now? 1.Download NodeXL 2.Identify what data you want to map Online and offline Current audience, fundraising, etc. New audience opportunities 3.Be flexible. Be curious Ask questions Look for patterns Repeat investigations

72. Thoughts and questions? Experiences to share?

73. Remember networks are fluid. You can shape them with both online and offline interactions.

74. Before an event

75. After an event

76. Thank you! Evaluation Survey: http://po.st/HHuNu5http://po.st/HHuNu5 Collaboration Notes: http://po.st/mEKWpRhttp://po.st/mEKWpR #15NTCdigmap Nicole Fernandez, ncf7@georgetown.edu, @GeorgetownCCTncf7@georgetown.edu Erin Gamble, egamble@acdivoca.org, @eringamegamble@acdivoca.org Charrosé King, cking@acdivoca.org, @charroseckcking@acdivoca.org

77. Since 1963 and in 146 countries, ACDI/VOCA has empowered people to succeed in the global economy.