2013 passbac-marc smith-node xl-sna-social media-formatted

April 10-12, Chicago, IL

Charting Collections of Social Media Connections with NodeXLMaps and reports for social media networks

April 10-12, Chicago, IL

Please silence cell phones

April 10-12, Chicago, IL

About Me

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

April 10-12, Chicago, IL

http://smrfoundation.org

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

from people

to people. 5

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Patterns are

left behind

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There are many kinds of ties…. Send, Mention, Like, Link,

http://www.flickr.com/photos/stevendepolo/3254238329

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

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“Think Link”Nodes & Edges

Is related to

A B

World Wide Web

Each contains 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

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http://byobi.com/blog/2013/03/analyzing-sql-server-object-dependencies-with-nodexl/Bill Anton (@SQLbyoBI)

HOW TO BUILD a table of all the object dependencies in a database.

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Social Networks

History: from the dawn of time!Theory and method: 1934 ->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://en.wikipedia.org/wiki/Jacob_L._Moreno

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

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Introduction to NodeXL

Like MSPaint™ for network graphs.

Communities in Cyberspace

PublicationVisualizationAnalysisContainerProviders

Network Analysis Data Flow

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

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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 &“Answer People”

Discussion startersTopic setters

Discussion peopleTopic setters

42D

iane

has

hi

gh

degr

ee

Hea

ther

has

hig

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betw

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ess

NodeXL: Network 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

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6 kinds of Twitter social media networks

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#My2K

Polarized

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#CMgrChat

In-group / Community

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Lumia

Brand / Public Topic

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#FLOTUS

Bazaar

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New York Times ArticlePaul Krugman

Broadcast: Audience + Communities

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Dell Listens/Dellcares

Support

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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 do #SQLPass and #PASSBAC look like?

SNA questions for social media:

Top 10 Vertices, Ranked by Betweenness Centrality:@sqlpass@BrentO@PaulRandal@ClerisyDatabase@SQLRockstar@jenstirrup@SQLChicken@SQLSocialite@MicrosoftBI@kekline

http://nodexlgraphgallery.org/Pages/Graph.aspx?graphID=3965

http://nodexlgraphgallery.org/Pages/Graph.aspx?graphID=3966

Top 10 Vertices, Ranked by Betweenness Centrality:@passbac@MicrosoftBI@dennylee@impetustech@sqlpass@ExtendedResults@StaciaMisner@marcorus@SQLRockstar@jenstirrup

http://nodexlgraphgallery.org/Pages/Graph.aspx?graphID=3982

Top 10 Vertices, Ranked by Betweenness Centrality:@SQLServer@eric_kavanagh@DBA_MAN@confio@DZone@SQLRockstar@YvesMulkers@BrentO@SQL_By_Joey@zymasesystems

http://nodexlgraphgallery.org/Pages/Graph.aspx?graphID=3983

Top 10 Vertices, Ranked by Betweenness Centrality:@timoreilly@hortonworks@cloudera@YvesMulkers@TDWI@IBMbigdata@eric_kavanagh@furrier@benjguin@andreisavu

http://nodexlgraphgallery.org/Pages/Graph.aspx?graphID=3984

Top 10 Vertices, Ranked by Betweenness Centrality:@neo4j@peterneubauer@emileifrem@jimwebber@DZone@Neo4jFr@al3xandru@volkantufekci@ajlopez@p3rnilla

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Central tenet Social structure emerges from the aggregate of relationships (ties) among members of a population

Phenomena of interestEmergence of cliques and clusters from patterns of relationshipsCentrality (core), periphery (isolates), betweenness

MethodsSurveys, 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

ED

F

A

CB

H

G

IC

DE

A B D E

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NodeXL: Free/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. See: http://nodexl.codeplex.com

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

NodeX

L V

ideo

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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*

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Twitter Network for “Microsoft Research”

*AFTER*

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Network Motif Simplification

Cody Dunne, University of Maryland

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NodeXL calculates network metrics and word pairs

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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.

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NodeXL Ribbon in Excel

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

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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

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What we are trying to do:Open Tools, Open Data, Open ScholarshipBuild the “Firefox of GraphML” – open tools for collecting and visualizing social media dataConnect users to network analysis – make network charts as easy as making a pie chartConnect researchers to social media data sourcesArchive: Be the “Allen Very Large Telescope Array” for Social Media data – coordinate and aggregate the results of many user’s data collection and analysisCreate open access research papers & findingsMake “collections of connections” easy for users to manage

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What we have done: Open ToolsNodeXLData providers (“spigots”)• ThreadMill Message Board• Exchange Enterprise Email• Voson Hyperlink• SharePoint• Facebook• Twitter• YouTube• Flickr

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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

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What we have done: Open Scholarship

UMD: Webshop 2011, 2012, DSST 2013: NSF, Google, Intel, YahooOther Workshops: • LINKS’13, PAWCON, Purdue,

IEEE CTS

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What we have done: Open Scholarship

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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, 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

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How you can help

Sponsor a featureSponsor workshopsSponsor a studentSchedule trainingSponsor the foundationDonate your money, code, computation, storage, bandwidth, data or employee’s timeHelp promote the work of the Social Media Research Foundation

April 10-12, Chicago, IL

Charting Collections of Social Media Connections with NodeXLMaps and reports for social media networks

84

Win a Microsoft Surface Pro!

Complete an online SESSION EVALUATION to be entered into the draw.

Draw closes April 12, 11:59pm CTWinners will be announced on the PASS BA Conference website and on Twitter.

Go to passbaconference.com/evals or follow the QR code link displayed on session signage throughout the conference venue.

Your feedback is important and valuable. All feedback will be used to improve and select sessions for future events.

April 10-12, Chicago, IL

Thank you!Diamond Sponsor Platinum Sponsor

Marc@connectedaction.net

http://www.connectedaction.nethttp://www.codeplex.com/nodexlhttp://www.twitter.com/marc_smithhttp://nodexlgraphgallery.orghttp://www.slideshare.net/Marc_A_Smithhttp://www.smrfoundation.org