Marc A. SmithChief Social ScientistConnected Action Consulting Groupmarc@connectedaction.nethttp://www.connectedaction.nethttp://nodexl.codeplex.com/

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

Mapping and Measuring Connections

About Me

Introductions

Marc A. SmithChief Social ScientistConnected Action Consulting Group

Marc@connectedaction.nethttp://www.connectedaction.nethttp://www.codeplex.com/nodexlhttp://www.twitter.com/marc_smithhttp://www.flickr.com/photos/marc_smithhttp://www.facebook.com/marc.smith.sociologisthttp://www.linkedin.com/in/marcasmithhttp://www.slideshare.net/Marc_A_Smithhttp://www.smrfoundation.org

• 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 Theoryhttp://en.wikipedia.org/wiki/Social_network

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

CD

E

A B D E

OF

Crowds matter

Kodak BrownieSnap-Shot Camera

The first easy to use

point and shoot!

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

Crowds

Social Media (email, Facebook, Twitter, YouTube, and more) is all about connections

from people

to people.

10

Patterns are left behind

11

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…

Internet Verbs!

“Think Link”Nodes & Edges

Is related to

A BIs related to

Is related to

“Think Link”Nodes & Edges

Is related to

A BIs related to

Is related to

World Wide Web

Social media must contain one or more

social networks

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 social media data sources

Location, Location, Location

Position, Position, Position

Mapping and Measuring Connections with

Like MSPaint™ for graphs.— the Community

Now Available

Communities in Cyberspace

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

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

What we have done: Open Tools

• NodeXL• Data providers (“spigots”)

– ThreadMill Message Board– Exchange Enterprise Email– Voson Hyperlink– SharePoint– Facebook– Twitter– YouTube– Flickr

NodeXL Ribbon in Excel

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

What we have done: Open Scholarship

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

Network Analysis Data Flow

PublicationVisualizationAnalysisContainerProviders

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

Data Providers

Providers

Example NodeXL data importer for Twitter

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

Data Container

Container

Data Analysis

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

Analysis

Data Visualization

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

Visualization

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

Data Publication

Publication

Social Network Maps Reveal

Key influencers in any topic.

Sub-groups.

Bridges.

Hubs

Bridges

Islands

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

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

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

[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

#My2K

Polarized

#CMgrChat

In-group / Community

Lumia

Brand / Public Topic

#FLOTUS

Bazaar

New York Times ArticlePaul Krugman

Broadcast: Audience + Communities

Dell Listens/Dellcares

Support

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?

Top 10 Vertices@tnwconference@shingy@aral@patrick@jarnoduursma@sarahmarshall@boris@briansolis@technifista@qadabraplatform

Most central:@bitpay@coindesk@tuurdemeester@bitgiveorg@allthingsbtc@ihavebitcoins@btcmarketsnews@sp0rkyd0rky@hermetec@redditbtc

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

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

[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

[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.

C. Scott Dempwolf, PhD

Research Assistant Professor & Director

UMD - Morgan State Center for Economic Development

What is Social Network Analysis? How is it useful for the humanities?

1. New framework for analysis2. Data visualization allows new perspectives – less linear, more comprehensive

Social Network Analysis and Ancient HistoryDiane H. Cline, Ph.D.University of Cincinnati

Strategies for social media engagement based on social media network analysis

Request your own network map and report

http://connectedaction.net

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

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

Thank you!

Marc A. SmithChief Social ScientistConnected Action Consulting Groupmarc@connectedaction.nethttp://www.connectedaction.nethttp://nodexl.codeplex.com/

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

Mapping and Measuring Connections