Analyzing Social Media Networks with NodeXL Marc A. Smith

Analyzing Social Media Networks with NodeXLMarc A. Smith

Chief Social ScientistConnected Action Consulting Group

marc@connectedaction.nethttp://www.connectedaction.net

http://www.codeplex.com/nodexl

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The NodeXL Project Team

About Me

Introductions

Marc A. SmithChief Social ScientistConnected Action Consulting Group

Marc@connectedaction.nethttp://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_Smith

• 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

Social Networks

• History: from the dawn of time!

• Theory and method: 1934 ->

• Jacob L. Moreno

• http://en.wikipedia.org/wiki/Jacob_L._Moreno

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

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8Two “answer people” with an emerging 3rd.

Mapping Newsgroup Social Ties

Microsoft.public.windowsxp.server.general

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Distinguishing attributes of online social roles

• Answer person– Outward ties to local

isolates– Relative absence of

triangles– Few intense ties

• Reply Magnet– Ties from local isolates often

inward only– Sparse, few triangles– Few intense ties

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Distinguishing attributes:

• Answer person– Outward ties to local

isolates– Relative absence of triangles– Few intense ties

• Discussion person– Ties from local isolates often

inward only– Dense, many triangles– Numerous intense ties

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

#WIN09

http://www.youtube.com/watch?v=0M3T65Iw3Ac

NodeXL Video

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

Import data from a variety of SNA and Social Media data sources

NodeXL Network Metrics

A minimal network can illustrate the ways different locations have different values for centrality and

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Heather has high

betweeness

NodeXLNetwork Overview Discovery and Exploration add-in for Excel 2007

NodeXL: Display nodes with subgraph images sorted by network attributes using Excel Data|Sort

“SAP” mentioning twitter users

“SAP” mentioning twitter usersSize = FollowersEdge = # relationship ties

The NodeXL project is Available via the CodePlex Open Source Project Hosting Site:http://www.codeplex.com/nodexl

Display community members sorted by network attributes using Excel Data|Sort

Summary network metrics Displayed on

“Overall Metrics” tab

Map data to display attributes

Dynamic FiltersNow feature

Metrics histograms

Import from flickr tag and user networks

Network Clusters visualization showing three Flickr tag clusters,each representing a different context for “mouse”.

Isolate clusters showing three different contexts for the “mouse” tag in Flickr: mouse animal, computer mouse, and Mickey Mouse character.

NodeXL Network of Flickr users who comment onMarc_Smith’s photos (network depth 1.5; edge weight ≥ 4).

Import data from Twitter user and term networks

NodeXL Tutorial

http://casci.umd.edu/

Book forthcoming:Analyzing social media

networks with NodeXL: Insights from a

connected world

Social media network archives

• On-going collection• Additional sources: enterprise/consumer• More metrics• Performance• Cross-platform/Web• Clustering• Time series analysis

Analyzing Social Media Networks with NodeXLMarc A. Smith

Chief Social ScientistConnected Action Consulting Group

marc@connectedaction.nethttp://www.connectedaction.net

http://www.codeplex.com/nodexl

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AnswerPerson

Signatures

DiscussionPeople

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Spammer

Discussion Starter

Reply orientedDiscussion

FlameWarrior