20120301 strata-marc smith-mapping social media networks with no coding using node xl
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Marc A. SmithChief Social ScientistConnected Action Consulting [email protected]://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
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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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About Me
Introductions
Marc A. SmithChief Social ScientistConnected Action Consulting Group
[email protected]://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
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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
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What we have done: Open Tools
• NodeXL• Data 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• 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
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What we have done: Open Scholarship
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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
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Social Media (email, Facebook, Twitter, YouTube, and more) is all about connections
from people
to people.
12
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Patterns are left behind
13
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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!
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“Think Link”Nodes & Edges
Is related to
A B
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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.
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World Wide Web
Each contains one or more social networks
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Hubs
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Bridges
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http://www.flickr.com/photos/library_of_congress/3295494976/sizes/o/in/photostream/
Clusters
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http://www.flickr.com/photos/amycgx/3119640267/
Crowds
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Introduction to NodeXL
Like MSPaint™ for graphs.— the Community
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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
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#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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• 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
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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
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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 and “Answer People”
Discussion starters, Topic setters
Discussion people, Topic setters
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http://www.flickr.com/photos/marc_smith/sets/72157622437066929/
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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
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Now Available
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Communities in Cyberspace
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Twitter Network for “Microsoft Research”*BEFORE*
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Twitter Network for “Microsoft Research”*AFTER*
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NodeXL Ribbon in Excel
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NodeXL data import sources
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Example NodeXL data importer for Twitter
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NodeXL imports “edges” from social media data sources
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NodeXL creates a list of “vertices” from imported social media edges
NodeXL displays subgraph images along with network metadata
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Automate
NodeXL Automation
makes analysis simple and fast
Perform collections of
common operations with
a single click
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NodeXL Network Metrics
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NodeXL “Autofill columns” simplifies mapping data attributes to display attributes
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NodeXL enables filtering of networks
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NodeXL Generates Overall Network Metrics
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Social Network Maps Reveal
Key influencers in any topic.
Sub-groups.
Bridges.
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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
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2012 Schedule: Planned Workshops
March 1 - StrataMarch 5 2012 – PAWCONJune 2012 - ICWSMJuly 2012 – Lipari School on ComplexityAugust 8, 2012 - AEJMCAugust 21, 2012 – Webshop 2012
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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
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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
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Thank you!
The Social Media Research Foundation
http://www.smrfoundation.org
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Backup
• Examples of social media network analysis• Sources of social network analysis material
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Who is the mayor of your hashtag?
Find out at: http://netbadges.com
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Who is the mayor of your hashtag?
Find out at: http://netbadges.com
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http://netbadges.com
Who is the mayor of your hashtag?
Find out at: http://netbadges.com