2010 june - personal democracy forum - marc smith - mapping political social media crowds
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Transcript of 2010 june - personal democracy forum - marc smith - mapping political social media crowds
Mapping the shape of political social media crowdsMarc A. Smith
Chief Social ScientistConnected Action Consulting Group
[email protected]://www.connectedaction.net
http://www.codeplex.com/nodexl
A project from the Social Media Research Foundation: http://www.smrfoundation.org
http://www.flickr.com/photos/library_of_congress/3295494976/sizes/o/in/photostream/
http://www.flickr.com/photos/amycgx/3119640267/
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_Smith
Location, Location, Location
ecommNetwork of connections among “ecomm” mentioning Twitter users
Position, Position, Position
Social Networks
• History: from the dawn of time!
• Theory and method: 1934 ->
• Jacob L. Moreno
• http://en.wikipedia.org/wiki/Jacob_L._Moreno
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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• 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
Centralities http://en.wikipedia.org/wiki/Centrality
• Degree• Closeness• Betweenness• Eigenvector
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). [Local copy]
Experts and “Answer People”
Discussion starters, Topic setters
Discussion people, Topic setters
Friends, foes, and fringe: norms and structure in political discussion networks.
Proceedings of the 2006 International Conference on Digital Government Research.
John Kelly, Danyel Fisher, and Marc Smith.
Himelboim, Itai, Eric Gleave, and Marc Smith. 2009. Discussion catalysts in online political discussions: Content importers and conversation starters. The Journal of Computer-Mediated Communication, Vol. 14.
Introduction to NodeXL
NodeXL: Network Overview, Discovery and Exploration for Excel
Leverage spreadsheet for storage of edge and vertex data
http://www.codeplex.com/nodexl
Social Media Research Foundationhttp://smrfoundation.org
Social Media Research Foundation
Open Tools
Open Data
Open Scholarship
A minimal network can illustrate the ways different locations have different values for centrality and
degreeDi
ane h
as h
igh
degr
ee
Heather has high
betweeness
NodeXLNetwork Overview Discovery and Exploration add-in for Excel 2007
Forthcoming, Summer 2010
Communities in Cyberspace
Import from multiple social media network
sources
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NodeXLFree/Open Social Network Analysis add-in for Excel 2007 makes graph theory as
easy as a bar chart, integrated analysis of social media sources.http://nodexl.codeplex.com
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Scott Golder (@redlog) is a graduate student in Sociology at Cornell University. He was previously a researcher at HP Labs, and holds an A.B. in Linguistics with Computer Science from Harvard University and an M.S. in Media Arts and Sciences from the MIT Media Laboratory. His research interests broadly include network and social identity effects online, which he has examined in a variety of environments including usenet, online poker, social bookmarking and social network services. His website is www.redlog.net.
Vladimir Barash (@vlad43210) is a graduate student in Information Science at Cornell University. He holds a BA in Cognitive Science from Yale University. His research interests include social media, online communities and diffusion, and his thesis topic is on the structural properties of diffusion in social networks. His websited is www.vlad43210.com
Arlen SpecterFollowing: 348 Followers: 8704
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Tuesday 18 May4:00pm
Arlen SpecterFollowing: 348 Followers: 8704
Tweets: 580
Joe SestakFollowing: 3845 Followers: 3631
Tweets: 763
Tuesday 18 May4:00pm
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_Smith
Social Media Research Foundationhttp://smrfoundation.org