1.
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://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 Research Foundation
http://smrfoundation.org

4.
Social Media Research Foundation
People Disciplines Institutions
University
Faculty
Computer Science University of Maryland
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

5.
What we are trying 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

6.
Kodak
Brownie
Snap-
Shot
Camera
The first
easy to use
point and shoot!

7.
Crowds matter

8.
What we have done: Open Tools
• NodeXL
• Data providers (“spigots”)
– ThreadMill Message Board
– Exchange Enterprise Email
– Voson Hyperlink
– SharePoint
– Facebook
– Twitter
– YouTube
– Flickr

9.
What we have done: 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

10.
What we have done: Open Scholarship

11.
http://www.pewinternet.org/2014/02/20/mapping-twitter-topic-networks-from-polarized-crowds-to-community-clusters/

12.
Social Media
(email, Facebook, Twitter, You
Tube, and more)
is all about
connections
from people
to people.
12

13.
Patterns are
left behind
13

14.
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…

15.
Strong ties

16.
Weak ties

17.
p://www.flickr.com/photos/fullaperture/81266869/
Strength of Weak ties

18.
“Think Link”
Nodes & Edges
Is related to
A BIs related to
Is related to

19.
“Think Link”
Nodes & Edges
Is related to
A BIs related to
Is related to

20.
World Wide Web
Social media must contain
one or more
social networks

21.
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

22.
NodeXL imports “edges” from social media data sources

23.
Social
Networks
• History:
from the
dawn of
time!
• Theory and
method:
1934 ->
• Jacob L.
Moreno
• http://en.wik
ipedia.org/wi
ki/Jacob_L._
Moreno
Jacob Moreno’s early social network diagram of positive and negative relationships among members of a football
team.
Originally published in Moreno, J. L. (1934). Who shall survive? Washington, DC: Nervous and Mental Disease
Publishing Company.

24.
A nearly social network diagram of relationships among workers in a factory
illustrates the positions different workers occupy within the workgroup.
Originally published in Roethlisberger, F., and Dickson, W. (1939). Management and
the worker. Cambridge, UK: Cambridge University Press.

25.
Location, Location, Location

26.
Position, Position, Position

27.
https://www.simonsfoundation.org/quanta/20131004-the-mathematical-shape-of-things-to-come/

28.
http://simonsfoundation.s3.amazonaws.com/jwplayer/BigData/Topological_Data_Analysis_Intro.mp4

29.
Introduction to NodeXL
Like MSPaint™ for graphs.
— the Community

30.
Now Available

31.
Communities
in Cyberspace

32.
http://www.flickr.com/photos/badgopher/3264760070/

33.
http://www.flickr.com/photos/druclimb/2212572259/in/photostream/

34.
http://www.flickr.com/photos/hchalkley/47839243/

35.
http://www.flickr.com/photos/rvwithtito/4236716778

36.
http://www.flickr.com/photos/62693815@N03/6277208708/

37.
Social Network Maps Reveal
Key influencers in any topic.
Sub-groups.
Bridges.

38.
Hubs

39.
Bridges

40.
http://www.flickr.com/photos/storm-crypt/3047698741

41.
http://www.flickr.com/photos/library_of_congress/3295494976/sizes/o/in/photostream/

42.
http://www.flickr.com/photos/amycgx/3119640267/

43.
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

44.
NodeXL
Network Overview Discovery and Exploration add-in for Excel 2007/2010
A minimal network can
illustrate the ways different
locations have different values
for centrality and degree

45.
#occupywallstreet
15 November 2011
#teaparty
15 November 2011
http://www.newscientist.com/blogs/onepercent/2011/11/occupy-vs-tea-party-what-their.html

46.
[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

47.
[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

48.
#My2K
Polarized

49.
#CMgrChat
In-group / Community

50.
Lumia
Brand / Public Topic

51.
#FLOTUS
Bazaar

52.
New York Times Article
Paul Krugman
Broadcast: Audience + Communities

53.
Dell Listens/Dellcares
Support

54.
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?

55.
pawcon Twitter NodeXL SNA Map and Report for Monday, 17 March 2014 at 15:15 UTC

56.
strataconf Twitter NodeXL SNA Map and Report for 2014-02-11 12-53-27
Top 10 Vertices, Ranked by
Betweenness Centrality:
@strataconf
@peteskomoroch
@acroll
@oreillymedia
@orthonormalruss
@ayirpelle
@bigdata
@furrier
@marketpowerplus
@sassoftware

57.
datavis Twitter NodeXL SNA Map and Report for Tuesday, 11 February 2014 at 18:55 UTC
Top 10 Vertices, Ranked by
Betweenness Centrality:
@bigpupazzoverde
@randal_olson
@twitterdata
@7of13
@yochum
@edwardtufte
@twittersports
@grandjeanmartin
@smfrogers
@albertocairo

58.
[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

59.
http://www.pewresearch.org/fact-tank/2014/02/20/the-six-types-of-twitter-conversations/

60.
[Divided]Polarize
d Crowds
[Unified]Tig
ht 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.

61.
Request your own network map and report
http://connectedaction.net

62.
• 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

63.
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

64.
NodeXL
Free/Open Social Network Analysis add-in for Excel 2007/2010 makes graph
theory as easy as a pie chart, with integrated analysis of social media sources.
http://nodexl.codeplex.com

65.
http://www.youtube.com/watch?v=0M3T65Iw3Ac
NodeXLVideo

66.
Goal: Make SNA easier
• 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

67.
Twitter Network for “Microsoft Research”
*BEFORE*

68.
Twitter Network for “Microsoft Research”
*AFTER*

69.
Network Motif Simplification
Cody Dunne, University of Maryland

70.
Network Motif Simplification
D-connector (glyph on the right)
D-clique (glyphs for 4, 5, and 6
member cliques below)
Dr. Cody Dunne
Fan(glyph on the right)

71.
NodeXL
Graph Gallery

72.
Scholars using NodeXL
• Communications
– Katy Pearce
– Itai Himelboim
• Business
– Scott Dempwolf
• Humanities/Classics
– Diane Cline

73.
C. Scott
Dempwolf,
PhD
Research Assistant
Professor & Director
UMD - Morgan State
Center for Economic
Development

74.
What is Social Network Analysis?
How is it useful for the humanities?
1. New framework for analysis
2. Data visualization allows new perspectives – less linear, more comprehensive
Social Network Analysis and Ancient History
Diane H. Cline, Ph.D.
University of Cincinnati

75.
Strategies for social media engagement based on
social media network analysis

76.
NodeXL calculates metrics
about networks and content

77.
The Content summary
spreadsheet displays the most
frequently used URLs, hashtags,
and user names within the
network as a whole and within
each calculated sub-group.

78.
NodeXL Ribbon in Excel

79.
NodeXL
data import sources

80.
Example NodeXL data importer for Twitter

81.
NodeXL imports “edges” from social media data sources

82.
NodeXL creates a list of “vertices” from imported social media edges
NodeXL displays subgraph images along with network metadata

83.
NodeXL
Automation
makes analysis
simple and fast
Perform
collections
of common
operations
with a single
click

84.
NodeXL Generates Overall Network Metrics

85.
What we want to do:
(Build the tools to) map the social web
• Move NodeXL to the web: (Node[NOT]XL)
– Node for Google Doc Spreadsheets?
– WebGL Canvas? D3.JS? Sigma.JS
• 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

86.
How you can help
• Sponsor a feature
• Sponsor workshops
• 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

87.
A project from the Social Media Research Foundation: http://www.smrfoundation.org