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Marc A. SmithChief Social ScientistConnected Action Consulting [email protected]://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
[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
Location, Location, Location
Network of connections among “SharePoint” mentioning Twitter users
Position, Position, Position
Hardin, Garrett. 1968/1977. “The tragedy of the commons.” Science 162: 1243-48. Pp. 16-30 in Managing the Commons, edited by G. Hardin and J. Baden. San Francisco: Freeman.
Wellman, Barry. 1997. “An electronic group is virtually a social network.” In S. Kiesler (Ed.), The Culture of the Internet. Hillsdale, NJ: Lawrence Erlbaum.
5
Collective Action Dilemma Theory
• Central tenet– Individual rationality leads to collective disaster
• Phenomena of interest– Provision and/or sustainable consumption of collective
resources– Public Goods, Common Property, "Free Rider” Problems,
Tragedies– Signaling intent
• Methods– Surveys, interviews, participant observation, log file analysis,
computer modeling
(Axelrod, 1984; Hess, 1995; Kollock & Smith, 1996)
Community Computer Mediated Collective Action
Common goods that require controlled consumption
http://flickr.com/photos/himalayan-trails/275941886/
Common goods that require collective contribution
http://flickr.com/photos/jose1jose2jose3/241450368/
Interactionist Sociology
• Central tenet– Focus on the active effort of
accomplishing interaction• Phenomena of interest
– Presentation of self – Claims to membership– Juggling multiple (conflicting) roles– Frontstage/Backstage – Strategic interaction– Managing one’s own and others’ “face”
• Methods– Ethnography and participant observation
(Goffman, 1959; Hall, 1990)
Whyte, William H. 1971. City: Rediscovering the Center. New York: Anchor Books.
Youse.Y’all.
Yes, youse.
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Are you my friend?
yes no
I like you I really like youI kind of like you
I feel socially obligated to link to youI know you
I wish I knew you I like your picture You are cool
I was paid to link to you I want your reflected glory
Everybody else links to you I’d vote for you
We met at a conference and it seemed like the thing to do.
Can I date you?
I beat you on Xbox Live Hi, Mom I have fake alter egos
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Tag Ecologies I
Adamic et al. WWW 2008
HUB-AND-SPOKE OF DECEIT: When Enron employees communicated about legitimate projects, e-mails were reciprocal and information was shared widely (right), but communications about an illicit project (left) reveal a sparse network with a central, informed clique and isolated external players.Brandy Aven, CMUhttp://www.sciencenews.org/view/generic/id/330731/title/Information_flow_can_reveal_dirty_deeds
Networks reveal patterns
Social Media (email, Facebook, Twitter, YouTube, and more) is all about connections
from people to people.
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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, Forward, Edit, Tag, Comment, Check-in…
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
20111122-NodeXL-Twitter-mwa11 OR mwa2011 OR mwebafrica OR mobilewebafrica
20111121-NodeXL-Twitter-#occupywallstreet
#occupywallstreet15 November 2011
#teaparty15 November 2011
http://www.newscientist.com/blogs/onepercent/2011/11/occupy-vs-tea-party-what-their.html
20111122-NodeXL-Twitter-malema
Won Soon Park, New Mayor of Seoul Korea
• 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
http://www.flickr.com/photos/marc_smith/sets/72157622437066929/
20110315-NodeXL-Twitter-sxsw graph
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
AnswerPerson
Signatures
DiscussionPeople
Spammer
Discussion Starter
Reply orientedDiscussion
FlameWarrior
39
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/
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
20111122-NodeXL-Twitter-poib
20111122-NodeXL-Twitter-zuma
Social Network Maps Reveal
Key influencers in any topic.
Sub-groups.
Bridges.
Why didn’t you just say so?
Identify the key influencers in any topic.Netbadges awards badges to influential people and web sites on the Internet. We analyze the social network of connections among all the
people or web sites that gather around a topic, issue, or interest and award badges to key people and sites, highlighting their recent
content.
Contact:
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
Cape Town28 November 2011
Protea HotelBreakwater Lodge,
Waterfront
Johannesburg30 November 2011
Gordon Instituteof Business Science,
Illovo
UpcomingFull-day social media network analysis workshops
This Friday – Hands on with NodeXL
Next week
Marc Smith Walter Pike
Marc A. SmithChief Social ScientistConnected Action Consulting [email protected]://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
