20120301 strata-marc smith-mapping social media networks with no coding using node xl

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 social media networks (with no coding) using NodeXL

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

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://delicious.com/marc_smith/Paper http://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

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

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• Webshop 2011: NSF, Google, Intel

– 4 Days, 45 Students, 20 Speakers– Great tweets!

• Webshop 2012! August 21-24 @UMD– Expanding numbers of students and add a day– Support speakers and student workers

• Other Workshops: ICWSM12, NetSci, HyperText12, Cape Town, Yeungnam, Italy

What we have done: Open Scholarship

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

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….

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 B

Strong ties

Weak ties

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.

World Wide Web

Each contains one or more social networks

Hubs

Bridges

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

Clusters

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

Crowds

Introduction to NodeXL

Like MSPaint™ for graphs.— the Community

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

• 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

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

http://www.flickr.com/photos/marc_smith/sets/72157622437066929/

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

Now Available

Communities in Cyberspace

Twitter Network for “Microsoft Research”*BEFORE*

Twitter Network for “Microsoft Research”*AFTER*

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 Network Metrics

NodeXL “Autofill columns” simplifies mapping data attributes to display attributes

NodeXL enables filtering of networks

NodeXL Generates Overall Network Metrics

Social Network Maps Reveal

Key influencers in any topic.

Sub-groups.

Bridges.

What we want to do: (Build the tools to) map the social web• Move NodeXL to the web:

– Node for Google Doc Spreadsheets!– WebGL Canvas

• 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

2012 Schedule: Planned Workshops

March 1 - StrataMarch 5 2012 – PAWCONJune 2012 - ICWSMJuly 2012 – Lipari School on ComplexityAugust 8, 2012 - AEJMCAugust 21, 2012 – Webshop 2012

Pending Work ItemsAutofill Group AttributeMerge Edges by AttributeModal TransformMerge WorkbooksAutomated Dynamic Filters: Time Series Analysis, contrastCaptions and LegendsUpload to Graph Gallery++: captions, workbookGraph Gallery++

User Accounts, Reporting, RSS Feeds, Network Visualization Web Canvas

Import: RDF, Wiki, SharePoint, Keyword networks from textMetrics: Triad CensusLayouts:

Force Atlas 2, Lin Log, “Bakshy Plots”, Quality MeasuresQuery-by-example search for network structures

How you can help

• Sponsor a feature• Sponsor Webshop 2012• 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!

The Social Media Research Foundation

http://www.smrfoundation.org

Backup

• Examples of social media network analysis• Sources of social network analysis material

Who is the mayor of your hashtag?

Find out at: http://netbadges.com

Who is the mayor of your hashtag?

Find out at: http://netbadges.com

http://netbadges.com

Who is the mayor of your hashtag?

Find out at: http://netbadges.com