1.
Visualizing Big Data:
Social Network Analysis
By Michael Lieberman

2.
SNA 101
• Social media network maps visualize the patterns of
connection that form when people follow, reply and mention
one another in Internet communication services like Twitter.
• When analyzed in aggregate collections of individual
connections form web-like network structures.
• Network structures form the nature of the conversation.
• Social media network structures come in several major forms:
• Divided
• Unified
• Fragmented
• Clustered

3.
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.

4.
The NSA Uses Social Network Analysis To Map
Terrorist Networks

5.
Why Social Network Visualizations
• Whenever you are studying a social network
• When you wish to understand how to improve
the effectiveness of the network
• Visualize your data so as to uncover patterns in
relationships or interactions
• When you want to follow the paths that
information follows in social networks
• When you do quantitative or branding research

6.
World Wide Web
Each contains one or more
social networks

7.
Interpretation of measures
CNM Social Media Module – Giorgos Cheliotis (gcheliotis@nus.edu.sg)7
 Degree
 Betweenness
 Closeness
 Eigenvector
How many people can this person reach directly?
How likely is this person to be the most direct
route between two people in the network?
How fast can this person reach everyone in the
network?
How well is this person connected to other well-
connected people?
Centrality measure Interpretation in social networks

8.
Social Network Maps
• Networks
• Tie Strength
• Key Players
• Cohesion
• Influencers
9
How to represent various social networks
How to identify strong/weak ties in the network
How to identify key/central nodes in network
Measures of overall network structure
Sub-groups
Bridges
Islands
CNM Social Media Module – Giorgos Cheliotis (gcheliotis@nus.edu.sg)

9.
Hubs
HUBS BRIDGES
ISLANDS
CROWDS/CLUSTERS

10.
6 kinds of Twitter social media networks

11.
#MONAELTAHAWY
PolarizedCourtesy Marc Smith

12.
#CASRO
In-group / Community

13.
#BMW
Brand / Public Topic

14.
@WendyDavis
Bazaar Network

15.
Ann Handley
@marketingprofs
Broadcast: Audience + Communities

16.
#Hashtag Clusters in Map Show Brand Snapshot on Twitter
Word pairs highlight Brand Conversation
Contrasting #Hashtags @WendyDavis
Top Hashtags in Tweet in G1: Top Hashtags in Tweet in G2: Top Hashtags in Tweet in G8:
WendyDavis WendyDavis WendyDavis
SingleMomLife tcot LizWarren
dfw caring tcot
FauxLife TEXAS unitered
TeamWendy msnbc prostitute
caring prolife lies
PointBreak badmom MoreFakeThanWendyDavis
dem KellyFile GregAbbott
prolife ccot prolife
Warren AbortionBarbie KeepTexasRed
Top Word Pairs in Tweet in G1:Top Word Pairs in Tweet in G2:Top Word Pairs in Tweet in G8:
rt,dloesch court,order rt,rednationrising
single,mom order,use kellyfile,more
dloesch,wendydavis use,drugs more,wendydavis
wendydavis,attack drugs,before wendydavis,story
attack,personal before,seeing story,divorced
personal,story seeing,kids divorced,man
story,life kids,reconsider man,paid
life,thing reconsider,political paid,education
thing,story political,aspirations education,immediately
mom,worked aspirations,wendydavis immediately,graduation

17.
Social Network Analysis and Facebook
Michael Lieberman’s Facebook Network
• G1 are High School Classmates
• G2 Current Friends – Jerusalem
• G3 Family
• G9 Worked at Ristorante Alfredo 1989
Hunterdon Central
Class of 1982
Social Circle
Extended
Family
Ristorante Alfredo 1989
For Facebook accounts passwords are required.

18.
Facebook Like’s Pages - Starbucks
Comments Made on the Starbucks ‘Likes’
1/21/2014 – 1/23/2014
Graph Metric Value
Graph Type Undirected
Vertices 11777
Unique Edges 365085
Edges With Duplicates 0
Total Edges 365085

19.
Facebook Like’s Pages - Starbucks
Comments -- Starbucks ‘Likes’
1/21/2014 – 1/23/2014
Name Locale Comments Between Centrality
Eigenvector
Centrality
Agnes Pylko en_US I quite like that-I am quite enjoying Starbucks tea these days 0.743626069 0.754179725
Irma Ofelia Rodriguez en_GB Que sabroso como estas como estan los ninos saludos Lupita. 0.68678461 0.623989193
Somer Alderson en_GB
You weren't open when I was gonna to get coffee tonight
and it made me sad. Darn campus store.
0.17252211 0.481296922
Karen Craven en_US
I love there expresso peppermint ice macho they so good
karen c
0.324485608 0.397906029
Chrystal Combitsis en_US
Maybe it is time to stop patronizing Starbucks. Too bad...I
liked the coffee.
0.124382885 0.194781237
Kostee Hoover Nikitakis en_US god i love this specific brew. LOVE IT I TELL YA 0.796447418 0.157976213
Barbara Saline en_US
I bought Starbuck individual cup coffee this week at Cosco,
absolutely delicious and a great price
0.125920577 0.129647526
Tom Wood en_US Oh, Starbucks - I wish you delivered. 0.651564928 0.127384168
Anne Gormley en_US Looks like the Melita filter and funnel 0.354619577 0.010865484
Keller O'Rourke en_US Wasted coffee beans. 0.653199984 0.006393752
•Facebook Influencers Using Eigenvalue Centrality Measures

20.
Mapping Hyperlink Networks
• Webpages are still a company’s most prominent presence on the
World Wide Web
• Virtual Observatory for the Study of Online Networks (VOSON)
• Set seed site (s). In VOSON, such as www.CASRO.org
• VOSON finds inbound and outbound links to see sites, creates links.
• Visualization of the VOSON Hyperlink Network in NodeXL shows a
hyperlink network space
• For example, you have a new website, how do you get potential
customers to find out about your fantastic new social network analysis
tool?

21.
Hyperlink Space
CASRO.org Ego Hyperlink Network

22.
Hyperlink Space
Marketing Research Industry

23.
Hyperlink Space
Casual Dining Restaurants

24.
About NodeXL
Introductions
Marc A. Smith
Chief Social Scientist
Connected Action Consulting Group
Marc@connectedaction.net
http://www.connectedaction.net
http://www.codeplex.com/nodexl
Michael Lieberman
Multivariate Solutions
michael@mvsolution.com