20110719 social media research foundation-charting collections of connections
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  • http://www.flickr.com/photos/library_of_congress/3295494976/sizes/o/in/photostream/
  • http://www.flickr.com/photos/amycgx/3119640267/
  • 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

20110719 social media research foundation-charting collections of connections 20110719 social media research foundation-charting collections of connections Presentation Transcript

  • 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
  • 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
  • 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 Action
    formed 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?
  • 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?
  • Producers
    Individuals
    How large are the social groups producing and consuming social media?
    Small Groups
    Consumers
    Large Groups
    Individuals
    Small Groups
    Large Groups
  • Dimensions of Social Media:
    How large are the pieces of social media?
    How interactive is the rate of exchange?
  • Who owns social media content?
    Dimensions of Social Media:
    Who can exercise what property rights over social media?
  • 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
  • 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/
  • 17
    Motivations for contribution to computer mediated public goods
    Source: xkcd, http://xkcd.com/386/
  • 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)
  • The Fan Dance of
    Concealment
    And
    Exposure
    http://flickr.com/photos/csb13/2178250762/
  • 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
  • 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
    A
    B
    C
    A
    B
    D
    E
    D
    E
    G
    F
    C
    D
    H
    I
    E
  • Email (and more) is from people to people
    22
  • Patterns are left behind
    23
  • There are many kinds of ties….
    http://www.flickr.com/photos/stevendepolo/3254238329
  • Each contains one or more social networks
    World Wide Web
  • Whyte, William H. 1971. City: Rediscovering the Center. New York: Anchor Books.
  • AnswerPersonSignatures
    Discussion
    People
  • Discussion Starter
    Spammer
    Reply orientedDiscussion
    Flame
    Warrior
    30
  • Youse.
    Y’all.
    Yes, youse.
    31
  • 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
  • 33
  • 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
  • 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, CMU
    http://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 Foundation
    Open Tools, Open Data, Open Scholarship
  • Social Media Research Foundationhttp://smrfoundation.org
  • 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
  • Now Available
  • Communities in Cyberspace
  • NodeXL map of flickr tags associated with Lipari
  • http://vimeo.com/21088958
  • 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. 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
  • 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