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Says who? Mapping the shape of social media crowds Marc A. Smith Chief Social ScientistConnected Action Consulting Group marc@connectedaction.net http://www.connectedaction.net http://www.codeplex.com/nodexl A project from the Social Media Research Foundation: http://www.smrfoundation.org
About Me Introductions Marc A. Smith Chief Social Scientist Connected Action Consulting Group Marc@connectedaction.net http://www.connectedaction.net http://www.codeplex.com/nodexl http://www.twitter.com/marc_smith http://delicious.com/marc_smith/Paper http://www.flickr.com/photos/marc_smith http://www.facebook.com/marc.smith.sociologist http://www.linkedin.com/in/marcasmith http://www.slideshare.net/Marc_A_Smith http://www.smrfoundation.org
http://www.flickr.com/photos/library_of_congress/3295494976/sizes/o/in/photostream/
http://www.flickr.com/photos/amycgx/3119640267/
Telecom networks are social networks
SNA 101 ,[object Object]
“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 betweennessA B C A B D E D E G F C D H I E
Location, Location, Location
Network of connections among “MWA2010” mentioning Twitter users Position, Position, Position
Most “between” people in the Network of connections among “MWA2010” Twitter users
“Think Link”Nodes & Edges Is related to B A In and Out Degree
Each contains one or more social networks World Wide Web
Collections of ConnectionsCentralities Degree Closeness Betweenness Eigenvector http://en.wikipedia.org/wiki/Centrality
NodeXLNetwork Overview Discovery and Exploration add-in for Excel 2007 Heather has high betweenness Diane has high degree A minimal network can illustrate the ways different locations have different values for centrality and degree
Social Networks History: from the dawn of time! Theory and method: 1934 -> Jacob L. Moreno http://en.wikipedia.org/wiki/Jacob_L._Moreno
Social Network Theoryhttp://en.wikipedia.org/wiki/Social_network 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
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 people, Topic setters Discussion starters, 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.
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 Foundation Open Tools, Open Data, Open Scholarship
Social Media Research Foundationhttp://smrfoundation.org
Now Available
Communities in Cyberspace
Import from multiple social media networksources
http://www.youtube.com/watch?v=0M3T65Iw3Ac NodeXL Video
NodeXL Free/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
Twitter “Pigspotter” – September 28, 2010
Twitter “Malema” – September 28, 2010
Twitter “Palin” – September 28, 2010
Twitter Vodacom – September 29, 2010
Twitter Worldcup – September 24, 2010
Twitter #TCOT – September 24, 2010
2010 - April - 21 - NodeXL - Twitter - teaparty followers scaled
2010 - May - 7 - NodeXL - twitter global warming

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2010 sept - mobile web africa - marc smith - says who - mapping social media crowds

Editor's Notes

  1. http://www.flickr.com/photos/library_of_congress/3295494976/sizes/o/in/photostream/
  2. http://www.flickr.com/photos/amycgx/3119640267/
  3. A tutorial on analyzing social media networks is available from: casci.umd.edu/NodeXL_TeachingDifferent positions within a network can be measured using network metrics.
  4. 2010 - April - 21 - NodeXL - Twitter - teaparty followers scaled
  5. 2010 - May - 7 - NodeXL - twitter global warming
  6. 2010 - May - 7 - NodeXL - twitter climate change