Marc Smith - Charting Collections of Connections in Social Media: Creating Maps and Measures with NodeXL

Marc Smith
Charting Collections of Connections in Social Media
EMERGENCE
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Name Surname
About Me
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

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

Crowds in social media have a hidden structure

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

We envision hundreds of NodeXL data collectors around the world
collectively generating a free and open archive of social media network
snapshots on a wide range of topics.
http://msnbcmedia.msn.com/i/msnbc/Components/Photos/
071012/071012_telescope_hmed_3p.jpg

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

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

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

“Think Link”Nodes & Edges
Is related to
A B
Is related to
Is related to

“Think Link”Nodes & Edges
A B
Is related to
Is related to
Is related to

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

NodeXL imports “edges”
from many social media data sources

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.

•  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

CommunicaNon,
Simon

Fraser
University.
pp.
7-‐16

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

•  Node

–  “actor”
on
which
relaNonships
act;
1-‐mode
versus
2-‐mode
networks

•  Edge

–  RelaNonship
connecNng
nodes;
can
be
direcNonal

•  Cohesive
Sub-‐Group

–  Well-‐connected
group;
clique;
cluster

•  Key
Metrics

–  Centrality
(group
or
individual
measure)

•  Number
of
direct
connecNons
that
individuals
have
with
others
in
the
group
(usually
look
at

incoming
connecNons
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
connecNons
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

SNA
101

E

D

F

A

C
B

H

G

I

C

D

E

A
B
D
E

Welser, Howard T., Eric Gleave, Danyel Fisher, & Marc Smith. 2007.
Visualizing the Signatures of Social
Roles in Online Discussion Groups.
The Journal of Social Structure. 8(2).
Experts &
“Answer People”
Discussion starters
Topic setters
Discussion people

Social Networks
•  History: from the
dawn of time!
•  Theory and method:
1932 ->
•  Jacob L. Moreno
http://en.wikipedia.org/
wiki/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.

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

[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

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

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

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

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?

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

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.

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

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.

Ø The “perfect” tweet:
.@Theirname #Theirhashtag
News about your brand using
their words http://your.site
#Yourhashtag

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!

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

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)

[Divided]

Polarized
Crowds

[Uniﬁed]

Tight
Crowd

[Fragmented]
Brand
Clusters

[Clustered]

Communi8es

[In-‐Hub
&
Spoke]

Broadcast

[Out-‐Hub
&
Spoke]

Support

[Low probability]
Find bridge users.
Encourage shared
material.
[Low probability]
Get message out to
disconnected
communities.
[Possible transition]
Draw in new participants.
Regularly create content.
Reply to multiple users.
[Undesirable transition]
Remove bridges,
highlight divisions.
[Low probability]
Get message out to
disconnected
communities.
[High probability]
Draw in new participants.
Increase density of
connections in two
groups.
[Low probability]
Dramatically increase
density of connections.
[High probability]
Increase retention, build
connections.
Increase density of
connections in two
groups.
[Low probability]
Increase population,
reduce connections.
Increase density of
connections in two
groups.
[Low probability]
[Low probability]
Get message out to
disconnected
communities.
connections.
[High probability]
Increase reply rate, reply
to multiple users.
Increase density of
connections in two
groups.
[Low probability]
Get message out to
disconnected
communities.
[High probability]
connections.
[High probability]
Increase publication of
new content and
regularly create content.

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

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

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

Marc Smith - Charting Collections of Connections in Social Media: Creating Maps and Measures with NodeXL

Marc Smith - Charting Collections of Connections in Social Media: Creating Maps and Measures with NodeXL

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Marc Smith - Charting Collections of Connections in Social Media: Creating Maps and Measures with NodeXL