Networks are a powerful way to understand social media. This talk reviews the ways the NodeXL application can be used to reveal the social media networks structures around topics.

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

3. 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

4. Crowds matter

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

6. Crowds in social media have a hidden structure

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

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

15. NodeXL Ribbon in Excel

16. NodeXL in Excel

20. #SocBiz

21. #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

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

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

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

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

28. [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

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

30. [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

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

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

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

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

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

36. 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.

37. 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.

38. 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

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

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

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

42. 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…

43. 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.

44. Patterns are
left behind
44

45. • 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

46. 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

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

48. 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

49. Now Available

50. Communities
in Cyberspace

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

53. Hubs

54. Bridges

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

56. 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?

57. [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

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

59. 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.

60. 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

61. 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.

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

63. Speak the language of the mayors

64. 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!

65. 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

66. 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)

67. [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.

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

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

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