2010 sept - mobile web africa - marc smith - says who - mapping social media crowds
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Says who? Mapping the shape of social media crowds Marc A. Smith Chief Social Scientist Connected Action Consulting Group [email protected] http://www.connectedaction.net http://www.codeplex.com/nodexl project from the Social Media Research Foundation : http:// www.smrfou
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Presentation to the 2010 Mobile Web Africa conference in Johannesburg, South Africa, September 29, 2010.
Transcript of 2010 sept - mobile web africa - marc smith - says who - mapping social media crowds
Says who? Mapping the shape of 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
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_Smithhttp://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• 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
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
A BIn and Out Degree
World Wide Web
Each contains one or more social networks
Collections of ConnectionsCentralities
• Degree• Closeness• Betweenness• Eigenvector
http://en.wikipedia.org/wiki/Centrality
Dian
e has
high
de
gree
Heather has high
betweenness
NodeXLNetwork Overview Discovery and Exploration add-in for Excel 2007
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
• 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
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
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 FoundationOpen Tools, Open Data, Open Scholarship
Now Available
Communities in Cyberspace
Import from multiple social media network
sources
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
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
2010 - May - 7 - NodeXL - twitter climate change
Bernie Hogan is a Research Fellow at the Oxford Internet Institute at the University of Oxford. Bernie's work focuses on the process of networking, or maintaining connections with other people. His dissertation focused on the use of multiple media for networking while his current research on Facebook looks at the complexities of networking with multiple groups on a single site.
Facebook “ego” networks
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
Tweets: 580
Joe SestakFollowing: 3845 Followers: 3631
Tweets: 763
Tuesday 18 May4:00pm
Arlen SpecterFollowing: 348 Followers: 8704
Tweets: 580
Joe SestakFollowing: 3845 Followers: 3631
Tweets: 763
Tuesday 18 May4:00pm
Social media looks like...
http://www.cmu.edu/joss/content/articles/volume8/Welser/
Which contains subgraphs
Social Media NetworkBadges
Connected Action badges allow publishers and community developers to encourage the community engagement they value by rewarding the user behaviors they desire.
Network Based Game Mechanics for Social Media
How badges shape behavior:
> Status markers
> Aspirational targets
> Volume and location rewards: longer posts, more prominently located
Questions we answer
• Who contributes the most effectively?Resulting in most pageviews
• Who connects the most?Resulting in new users/visits/cross-pollination of content
• Who answers the questions?Adding authoritativeness to your community discussions
• Who starts the conversations?Resulting in new engagement, increased time spent and pageviews
• How to encourage more of this behavior?Resulting in more of the same
• Basic Badges:o Popular: people who are connected to many other people o Networker: people who span widely across the community, connecting manyo Influential: people who are connected to the highly connected people
• Advanced badges include:o Answer Person: people who have provided brief replies to many low
frequency contributorso Agenda Setter: people who introduce topics that attract many replierso Question Person: people who ask questions that get answered by Answer
Peopleo Discussion Person: people who connect to many people who also connect to
each othero Eclectic: people who connect to a wide range of content o Newcomers get badges of their own: "Newest Bridge Builder, Newest
Discussion person"o Mayor of Topics: Long term contributors in each role get recognized: Senior
Bridge Builder, Senior Discussion Person"o Bridge Builder: people who connect with the most diverse collection of
others.
Badge Types
Badges can grant: Rewards!
Version 1• Conditional formatting• Skip moderation queues
Version 2• More flagging power• More liking power
Intended Results
• Badges from your site's Activity Stream• Automated reward and marker system
for content creatorso Increase engagemento Increase trusto Increase credibilityo Decrease attritiono Increase pageviews and visits
Summary: SNA tells you:
• Macro:– What is the “shape” of the crowd?– Are there sub-groups/clusters?
• Micro:– Who is at the “center”? – Who is at the “edge”?– Who is the “bridge”?
Contact:
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_Smithhttp://www.smrfoundation.org
Says who? Mapping the shape of 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
