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

Think Link! Network Insights with No Programming Skills

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

Social Media Research Foundationhttp://smrfoundation.org

Social Media Research FoundationPeople 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

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

Kodak BrownieSnap-Shot Camera

The first easy to use

point and shoot!

Crowds matter

What we have done: Open Tools

• NodeXL• Data providers (“spigots”)

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

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/

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

from people

to people.

12

Patterns are left behind

13

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!

Strong ties

Weak ties

http://www.flickr.com/photos/fullaperture/81266869/

Strength of Weak ties

“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

Social Networks

• History: from the dawn of time!

• Theory and method: 1934 ->

• 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.

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 andthe worker. Cambridge, UK: Cambridge University Press.

Location, Location, Location

Position, Position, Position

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

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

Introduction to NodeXL

Like MSPaint™ for graphs.— the Community

Now Available

Communities in Cyberspace

Network Analysis Data Flow

PublicationVisualizationAnalysisContainerProviders

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

Data Providers

Providers

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

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

Crowds

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

Dian

e has

high

de

gree

Heather has high

betweenness

NodeXLNetwork 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

#occupywallstreet15 November 2011

#teaparty15 November 2011

http://www.newscientist.com/blogs/onepercent/2011/11/occupy-vs-tea-party-what-their.html

[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?

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

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

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

[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.

Request your own network map and report

http://connectedaction.net

• 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

NodeXLFree/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

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

Nod

eXL

Vide

o

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

Twitter Network for “Microsoft Research”*BEFORE*

Twitter Network for “Microsoft Research”*AFTER*

Network Motif Simplification

Cody Dunne, University of Maryland

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)

NodeXLGraph Gallery

Scholars using NodeXL

• Communications– Katy Pearce– Itai Himelboim

• Business– Scott Dempwolf

• Humanities/Classics– Diane Cline

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

NodeXL calculates metrics about networks and content

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.

NodeXL Ribbon in Excel

NodeXL data import sources

Example NodeXL data importer for Twitter

NodeXL imports “edges” from social media data sources

NodeXL creates a list of “vertices” from imported social media edges

NodeXL displays subgraph images along with network metadata

Automate

NodeXL Automation

makes analysis simple and fast

Perform collections of common operations

with a single click

NodeXL Generates Overall Network Metrics

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

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

Think Link! Network Insights with No Programming Skills