Scaling API-first – The story of a global engineering organization
20110719 social media research foundation-charting collections of connections
1. Charting Collections of Connections in Social Media: Creating maps and Measures with NodeXL Marc A. Smith Director Social Media Research Foundationmarc@smrfoundation.org http://www.codeplex.com/nodexl
2. About Us Introductions Marc A. Smith Director Social Media Research Foundation Marc@smrfoundation.org http://www.smrfoundation.org http://www.codeplex.com/nodexl http://www.twitter.com/marc_smith http://delicious.com/marc_smith/Paper http://www.linkedin.com/in/marcasmith http://www.slideshare.net/Marc_A_Smith http://www.flickr.com/photos/marc_smith http://www.slideshare.net/SMRFoundation/ http://www.facebook.com/marc.smith.sociologist
6. Network of connections among “SharePoint” mentioning Twitter users Position, Position, Position
7. What is social media? A Sociological Frame: Collective Goodsproduced through Computer-Mediated Collective Action formed through Interaction Networks
8. 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?
9. Some Dimensions of Social Media How large are the social groups producing and consuming social media? How large and interactive are the objects produced and consumed? What does it mean to own a social media object?
10. Producers Individuals How large are the social groups producing and consuming social media? Small Groups Consumers Large Groups Individuals Small Groups Large Groups
11. Dimensions of Social Media: How large are the pieces of social media? How interactive is the rate of exchange?
12. Who owns social media content? Dimensions of Social Media: Who can exercise what property rights over social media?
13. 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. 13
14. 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
15. Common goods that require controlled consumption http://flickr.com/photos/himalayan-trails/275941886/
16. Common goods that require collective contribution http://flickr.com/photos/jose1jose2jose3/241450368/
17. 17 Motivations for contribution to computer mediated public goods Source: xkcd, http://xkcd.com/386/
18. 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)
19. The Fan Dance of Concealment And Exposure http://flickr.com/photos/csb13/2178250762/
20. 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
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22. “actor” on which relationships act; 1-mode versus 2-mode networks
54. 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 Can I date you? Are you my friend? We met at a conference and it seemed like the thing to do. no yes I kind of like you I really like you I like you I feel socially obligated to link to you I know you I beat you on Xbox Live Hi, Mom I have fake alter egos
56. 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
58. 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, CMU http://www.sciencenews.org/view/generic/id/330731/title/Information_flow_can_reveal_dirty_deeds Networks reveal patterns
59. 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
62. NodeXLNetwork Overview Discovery and Exploration add-in for Excel 2007/2010 Heather has high betweenness Diane has high degree A minimal network can illustrate the ways different locations have different values for centrality and degree
96. Charting Collections of Connections in Social Media: Creating maps and Measures with NodeXL Marc A. Smith Director Social Media Research Foundationmarc@smrfoundation.org http://www.codeplex.com/nodexl
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.
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
