20110128 connected action-node xl-sea of connections

Finding direction in a sea of connection:
Mapping networks and
Social media
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.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

About You
Introductions
Organization
Interest in networks
Technical skills
Social media usage
Data sets
Questions you want networks to help answer

http://www.flickr.com/photos/library_of_congress/3295494976/sizes/o/in/photostream/

http://www.flickr.com/photos/amycgx/3119640267/

Collaboration 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
A
B
C
A
B
D
E
D
E
G
F
C
D
H
I
E

Location, Location, Location

Network of connections among “SharePoint” mentioning Twitter users
Position, Position, Position

Most “between” people in the Network of connections among “SharePoint” Twitter users

There are many kinds of ties….
http://www.flickr.com/photos/stevendepolo/3254238329

“Think Link”Nodes & Edges
Is related to
B
A
In and Out Degree

Is related to
Edits
B
A
Shares membership
Ties of different types

Is related to
Edits
Person
Document
Shares membership
Nodesof different types

Collections of ConnectionsCentralities
Degree
Closeness
Betweenness
Eigenvector
http://en.wikipedia.org/wiki/Centrality

Each contains one or more social networks
World Wide Web

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

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

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

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

Tuesday 18 May
4:00pm
Arlen Specter
Following: 348
Followers: 8704
Tweets: 580
Joe Sestak
Following: 3845
Followers: 3631
Tweets: 763

Social media looks like...
http://www.cmu.edu/joss/content/articles/volume8/Welser/

Which contains subgraphs

That result in “Badges” Markers of social status
Thanks to 3ones.com

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

Badges on Comments
Thanks to 3ones.com

Badging Events in Recent Comments Stream
Thanks to 3ones.com

User Badge Widget
Thanks to 3ones.com

Badge Types
Basic Badges:
Popular: people who are connected to many other people
Networker: people who span widely across the community, connecting many
Influential: people who are connected to the highly connected people
Advanced badges include:
Answer Person: people who have provided brief replies to many low frequency contributors
Agenda Setter: people who introduce topics that attract many repliers
Question Person: people who ask questions that get answered by Answer People
Discussion Person: people who connect to many people who also connect to each other
Eclectic: people who connect to a wide range of content
Newcomers get badges of their own: "Newest Bridge Builder, Newest Discussion person"
Mayor of Topics: Long term contributors in each role get recognized: Senior Bridge Builder, Senior Discussion Person"
Bridge Builder: people who connect with the most diverse collection of others.

Intended Results
Badges from your site's Activity Stream
Automated reward and marker system for content creators
Increase engagement
Increase trust
Increase credibility
Decrease attrition
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. Smith
Chief Social Scientist
Connected Action Consulting Group
Marc@connectedaction.net
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

Finding direction in a sea of connection:
Mapping networks and
Social media
Marc A. Smith
Chief Social ScientistConnected Action Consulting Group
marc@connectedaction.net
A project from the Social Media Research Foundation: http://www.smrfoundation.org

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20110128 connected action-node xl-sea of connections

by Marc Smith on Feb 01, 2011

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20110128 connected action-node xl-sea of connections — Presentation Transcript