20110719 social media research foundation-charting collections of connections
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Marc A. SmithDirectorSocial Media Research [email protected]://www.codeplex.com/nodexl
Charting Collections of Connections in
Social Media: Creating maps and
Measures with NodeXL
About Us
Introductions
Marc A. SmithDirectorSocial Media Research Foundation
[email protected]://www.smrfoundation.orghttp://www.codeplex.com/nodexlhttp://www.twitter.com/marc_smithhttp://delicious.com/marc_smith/Paper http://www.linkedin.com/in/marcasmithhttp://www.slideshare.net/Marc_A_Smithhttp://www.flickr.com/photos/marc_smithhttp://www.slideshare.net/SMRFoundation/http://www.facebook.com/marc.smith.sociologist
http://www.flickr.com/photos/library_of_congress/3295494976/sizes/o/in/photostream/
http://www.flickr.com/photos/amycgx/3119640267/
Location, Location, Location
Network of connections among “SharePoint” mentioning Twitter users
Position, Position, Position
What is social media?
A Sociological Frame:
Collective Goodsproduced through
Computer-Mediated Collective Actionformed through
Interaction Networks
What makes social media social?
• Who makes it?• Who consumes it?• Who owns it?/Who profits from it?• Who or what makes it successful?• How to harness the swarm?• How to map and understand its dynamics?
– How do people and groups vary?– Who links to whom?
• What is next for social media?
How large are the social groups producing and consuming social media?
How large and interactive are the objects produced and consumed?
Some Dimensions of Social Media
What does it mean to own a social media object?
Dyadic exchanges.Email to named
individual(s)
Committee reports to a decision
maker/reviewer
Professional services reports for decision makers
Local email list“Social” blogs
Personal social network profile page
Multiple authored specialty
publicationsGroup blogs.
Personal social networks
Professional reports to specialty groups
Value added economic data Bloomberg
Messages to discussion
groups/web board
Sole authored source code
Popular blogsNovels
Multiple authored popular media,
software
Journalism
Wikipedia PagesPopular group blogs
Collective search engine users
Market behavior
Query log optimizations
Market analysis
How large are the social groups producing and consuming social media?
Individuals
Small Groups
Large Groups
IndividualsSmall Groups
Large Groups
Producers
Consumers
Digital Object
Editing Granularity
Fine (Character/Pixel/Byte)
Medium(Object/Attribute/Track/Player)
Coarse(Document/Message/Blog Post/Photo)
Digital Object Editing
Synchronicity
Each user can directly control smallest units of content.
Each user controls medium sized blocks of content that can only indirectly alter or be altered by other user’s content in a larger shared data structure.
Each user controls a block of content, rarely edited or modified by others with only associative linkages.
Synchronous Real time Shared canvas
Virtual WorldsMultiplayer GamesReal-time networked musical jamming
Chat, IM, Twitter
Asynchronous Shared docs, images, video, audioSource codeWikipedia
Contribution to collected works (album, anthology, report section, discussion group, photosets and other collections).
EmailBlog postsLink sharingPhoto sharingDocument sharingTurn based games
Dimensions of Social Media:How large are the pieces of social media?How interactive is the rate of exchange?
Dimensions of Social Media:Who can exercise what property rights
over social media?
Author Group of authors Recipients Observers Host Public
Domain
Types of property rights
“What does it mean to own social media content?”
Create?
Copy/Paste?
Edit/Delete?
Limit access?
Revoke access?
Monitor access?
Transfer to new host?
Transfer rights to others?
Commercial exploitation?
Adjoining display rights?(can I put ads near your content when I show it to other people)?
Aggregation and secondary analysis rights?
Who owns social media content?
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.
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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/
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Source: xkcd, http://xkcd.com/386/
Motivations for contribution to computer mediated public goods
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)
http://flickr.com/photos/csb13/2178250762/
The Fan Dance of Concealment
And Exposure
• 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
Email (and more) is 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
World Wide Web
Each contains one or more social networks
Whyte, William H. 1971. City: Rediscovering the Center. New York: Anchor Books.
AnswerPerson
Signatures
DiscussionPeople
Spammer
Discussion Starter
Reply orientedDiscussion
FlameWarrior
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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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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
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
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
Social Media Research FoundationOpen Tools, Open Data, Open Scholarship
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
Now Available
Communities in Cyberspace
NodeXL map of flickr tags associated with Lipari
http://www.flickr.com/photos/marc_smith/sets/72157622437066929/
http://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 Generates “Sub-Graph” Images
NodeXL displays subgraph images along with network metadata
NodeXL allows for fine control over the display of the network
NodeXL Generates Images of Networks
NodeXL Generates Network Graph Images
NodeXL enables filtering of networks
NodeXL Generates Filtered Network Images
NodeXL Generates Overall Network Metrics
NodeXL Map of Connections Among People who Tweeted “Galway”
Social networks in Twitter among people with at least one connection to someone else who Tweeted “Obama” on January 25, 2011
Network of word pairs frequently mentions among people who Tweeted the name “Obama” on January 25, 2011
US Congressman Paul Ryan word network (January 22, 2011)
Congresswoman Michel Bachmann keyword network (January 25, 2011)
NodeXL – Next Steps
Time and dynamic networks Edge bundling, routing Aggregate groups of nodes Spigots: Wikis, Facebook, Gmail, ….? Move to the Web!
About Us
Introductions
Marc A. SmithDirectorSocial Media Research Foundation
[email protected]://www.smrfoundation.orghttp://www.codeplex.com/nodexlhttp://www.twitter.com/marc_smithhttp://delicious.com/marc_smith/Paper http://www.linkedin.com/in/marcasmithhttp://www.slideshare.net/Marc_A_Smithhttp://www.flickr.com/photos/marc_smithhttp://www.slideshare.net/SMRFoundation/http://www.facebook.com/marc.smith.sociologist
Marc A. SmithDirectorSocial Media Research [email protected]://www.codeplex.com/nodexl
Charting Collections of Connections in
Social Media: Creating maps and
Measures with NodeXL