2015 #MMeasure-Marc Smith-NodeXL Mapping social media using social network maps and reports

1. Social Media Analytics: how to “think link” A project from the Social Media Research Foundation: http://www.smrfoundation.org

2. About Me Introductions Marc A. Smith Chief Social Scientist / Director Social Media Research Foundation marc@smrfoundation.org http://www.smrfoundation.org http://www.codeplex.com/nodexl http://www.twitter.com/marc_smith http://www.linkedin.com/in/marcasmith http://www.slideshare.net/Marc_A_Smith http://www.flickr.com/photos/marc_smith http://www.facebook.com/marc.smith.sociologist

3. Crowds matter

4. http://www.flickr.com/photos/amycgx/3119640267/ Crowds in social media matter

5. Crowds in social media have a hidden structure

6. https://demo-3dg-viz.herokuapp.com/

7. Kodak Brownie Snap- Shot Camera The first easy to use point and shoot!

8. NodeXL Ribbon in Excel

9. NodeXL in Excel

10. #SocBiz

11. #socbiz Twitter NodeXL SNA Map and Report for Tuesday, 15 September 2015 at 14:43 UTC Broadcast Broadcast Brand (Isolates) Broadcast Broadcast Broadcast Broadcast Broadcast

12. https://nodexlgraphgallery.org/Pages/Graph.aspx?graphID=53137#headerTopVertices

13. Are you the next mayor of #MMeasure? Tweet!

14. Request a sample social media network map for the topic of your choice: http://bit.ly/1J7waPY

15. http://www.pewinternet.org/2014/02/20/mapping-twitter-topic-networks-from-polarized-crowds-to-community-clusters/

16. [Divided] Polarized Crowds [Unified] Tight Crowd [Fragmented] Brand Clusters [Clustered] Community Clusters [In-Hub & Spoke] Broadcast Network [Out-Hub & Spoke] Support Network 6 kinds of Twitter social media networks

17. http://www.pewresearch.org/fact-tank/2014/02/20/the-six-types-of-twitter-conversations/

19. custexp Twitter NodeXL SNA Map and Report for Tuesday, 25 August 2015 at 19:18 UTC

20. https://nodexlgraphgallery.org/Pages/Graph.aspx?graphID=52355#headerTopVertices

21. https://nodexlgraphgallery.org/Pages/Graph.aspx?graphID=52355#headerTopHashtags

22. “Think Link” Nodes & Edges Is related to A BIs related to Is related to

23. “Think Link” Using Nodes & Edges to find people in the “middle of things” A B Is related to C Is related to

24. Who is the “mayor” of your hashtag? • How to measure “influence” in social media? • Influence is a property of the network, not the individual. • It is a location in the network where messages tend to be repeated. • Network measures of “centrality” can be applied to find “influential” people in social media.

25. The “mayor” of your hashtag • Some people are at the center of the conversation • “Centrality” is about being in the middle of the discussion • Not “Followers” • Not “Tweets” • Not “RTs” • Not “Mentions” • The “mayor” has an audience that may be bigger than yours.

26. Vertex1 Vertex 2 “Edge” Attribute “Vertex1” Attribute “Vertex2” Attribute @UserName1 @UserName2 value value value A network is born whenever two GUIDs are joined. Username Attributes @UserName1 Value, value Username Attributes @UserName2 Value, value A B

27. NodeXL imports “edges” from social media data sources

28. World Wide Web Social media must contain one or more social networks Crowds in social media form networks

29. Social Media (email, Facebook, Twitter, YouTube, and more) is all about connections from people to people. 41

30. There are many kinds of ties…. Send, Mention, http://www.flickr.com/photos/stevendepolo/3254238329 Like, Link, Reply, Rate, Review, Favorite, Friend, Follow, Forward, Edit, Tag, Comment, Check-in…

31. Social media network analysis • Social media is inherently made of networks, • which are created when people link and reply. • Collections of connections have an emergent shape, • Some shapes are better than others. • Some people are located in strategic locations in these shapes, • Centrally located people are more influential than others.

32. Patterns are left behind 44

33. • 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 Social Network Theory http://en.wikipedia.org/wiki/Social_network

34. 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 E D F A CB H G I C D E A B D E

35. http://www.bonkersworld.net/organizational-charts/

36. 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 starters, Topic setters Discussion people, Topic setters

37. Now Available

38. Communities in Cyberspace

39. Social Network Maps Reveal Key influencers in any topic. Sub-groups. Bridges.

40. Hubs

41. Bridges

42. http://www.flickr.com/photos/storm-crypt/3047698741

43. SNA questions for social media: 1. What does my topic network look like? 2. What does the topic I aspire to be look like? 3. What is the difference between #1 and #2? 4. How does my map change as I intervene? What does #YourHashtag look like? Who is the mayor of #YourHashtag?

45. Your social media audience is smaller… …than the audiences of ten influential voices.

46. The “mayor” of your hashtag • Some people are at the center of the conversation • “Centrality” is about being in the middle of the discussion • Not “Followers” • Not “Tweets” • Not “RTs” • Not “Mentions” • The “mayor” has an audience that may be bigger than yours.

47. Build a collection of mayors • Map multiple topics • Your brand and company names • Your competitor brands and company names • The names of the activities or locations related to your products • Identify the top people in each topic • Follow these people • 30-50% of the time they follow you back • Re-tweet these people (if they did not follow you) • 30-50% of the time they follow you back

48. Speak the language of the mayors • Use NodeXL content analysis to identify each users most salient: • Words • Word pairs • URLs • #Hashtags • Mix the language of the Mayors with your brand’s messages.

49. Speak the language of the mayors The “perfect” tweet: .@Theirname #Theirhashtag News about your brand using their words http://your.site #Yourhashtag

50. Speak the language of the mayors

51. Some shapes are better than others: • The value of Broadcast versus community network! • From community to brand! • Support and why community can be a signal of failure!

52. Three network phases of social media success Phase 1: You get an audience Phase 2: Your audience gets an audience Phase 3: Audience becomes community

53. Some shapes are better than others • Each shape reflects the kind of social activity that generates it: • Divided: Conflict • Unified: In-group • Brand: Fragmentation • Community: Clustering • Broadcast: Hub and spoke (In) • Support: Hub and spoke (Out)

54. [Divided] Polarized Crowds [Unified] Tight Crowd [Fragmented] Brand Clusters [Clustered] Communities [In-Hub & Spoke] Broadcast Network [Out-Hub & Spoke] Support Network [Low probability] Find bridge users. Encourage shared material. [Low probability] Get message out to disconnected communities. [Possible transition] Draw in new participants. [Possible transition] Regularly create content. [Possible transition] Reply to multiple users. [Undesirable transition] Remove bridges, highlight divisions. [Low probability] Get message out to disconnected communities. [High probability] Draw in new participants. [Possible transition] Regularly create content. [Possible transition] Reply to multiple users. [Undesirable transition] Increase density of connections in two groups. [Low probability] Dramatically increase density of connections. [High probability] Increase retention, build connections. [Possible transition] Regularly create content. [Possible transition] Reply to multiple users. [Undesirable transition] Increase density of connections in two groups. [Low probability] Dramatically increase density of connections. [Undesirable transition] Increase population, reduce connections. [Possible transition] Regularly create content. [Possible transition] Reply to multiple users. [Undesirable transition] Increase density of connections in two groups. [Low probability] Dramatically increase density of connections. [Low probability] Get message out to disconnected communities. [Possible transition] Increase retention, build connections. [High probability] Increase reply rate, reply to multiple users. [Undesirable transition] Increase density of connections in two groups. [Low probability] Dramatically increase density of connections. [Possible transition] Get message out to disconnected communities. [High probability] Increase retention, build connections. [High probability] Increase publication of new content and regularly create content.

55. Request your own network map and report http://connectedaction.net

56. Monitor your topics with social network maps • Identify the • Key people • Groups • Top topics • Locate your social media accounts within the network

57. Social Media Analytics: how to “think link” A project from the Social Media Research Foundation: http://www.smrfoundation.org