Post on 13-May-2015
description
Marc A. SmithChief Social ScientistConnected Action Consulting Groupmarc@connectedaction.nethttp://www.connectedaction.nethttp://www.codeplex.com/nodexl
A project from the Social Media Research Foundation: http://www.smrfoundation.org
Charting Collections of Connections in Social
Media: Creating Maps and
Measures with NodeXL
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
Social Media (email, Facebook, Twitter, YouTube, and more) is all about connections
from people to people.
3
Patterns are
left behind
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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, Edit, Tag, Comment…
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
• 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
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A B D E
http://www.flickr.com/photos/marc_smith/sets/72157622437066929/
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
Now Available
Analogy: Clusters Are OccludedHard to count nodes, clusters
Separate Clusters Are More Comprehensible
Twitter Network for “Microsoft Research”*BEFORE*
Twitter Network for “Microsoft Research”*AFTER*
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
Who we arePeople 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
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!– Expand numbers of students and add a day– Support speakers and student workers
• Workshops: Purdue, Maryland, Cape Town, Yeungnam
What we have done: Open Scholarship
Facebook networkshttp://www.connectedaction.net/2010/04/25/bernie-hogans-facebook-social-network-data-provider-and-visualization-toolkit/
Twitter Networks: connections among the people who tweeted the term “Kpop” on 24 October 2011
NodeXL data import sources
Example NodeXL data importer for Twitter
NodeXL imports “edges” from social media data sources
NodeXL Automation makes analysis simple and fast
NodeXL Network Metrics
NodeXL simplifies mapping data attributes to display attributes
NodeXL displays subgraph images along with network metadata
NodeXL enables filtering of networks
NodeXL Generates Overall Network Metrics
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
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
Contact:
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
Marc A. SmithChief Social ScientistConnected Action Consulting Groupmarc@connectedaction.nethttp://www.connectedaction.nethttp://www.codeplex.com/nodexl
A project from the Social Media Research Foundation: http://www.smrfoundation.org
Charting Collections of Connections in Social
Media: Creating Maps and
Measures with NodeXL