This document discusses analyzing social media networks using NodeXL. It defines social media and lists common types. It then covers key concepts in social network analysis including nodes, edges, metrics like centrality and density. NodeXL is introduced as a tool for visualizing and analyzing social networks from data collected from sources like personal emails, Twitter, forums and YouTube. Examples of social network analyses using NodeXL are provided such as mapping corporate email communication and identifying influencers on Twitter.

1. Analyzing Social Media Networks
with NodeXL
Derek L. Hansen
dlhansen@byu.edu
AAHB Workshop

2. Center for the Advanced Study
of Communities and Information
Human-Computer
Interaction Lab

3. New Tools & Methods to Analyze
Social Experience

4. Novel Designs of TMSP Interventions

5. What is Social Media?
A set of networked technologies that
supports social interactions.
Social media is about “transforming
monologue (one-to-many) into dialog
(many-to-many).”1
1 www.webpronews.com/blogtalk/2007/06/29/the-definition-of-social-media

7. Types of Social Media
Asynchronous
Threaded Conversation
Email, Google groups, Yahoo Answers, Listservs, Stack
Overflow
Synchronous Threaded
Conversation
Instant Messaging, IRC, Skype, Google Hangouts
Collaborative Authoring Wikipedia, Wikia, Google Docs
Blogs & Podcasts Livejournal, Blogger, Twitter, Vlogs, podcasts, photo blogs
Social Sharing YouTube, Flickr, Instagram, Pinterest, Last.Fm, Delicious,
Reddit, Snapchat
Social Networking Facebook, LinkedIn, eHarmony, Ning, Ravelry
Online Markets &
Production
eBay, Amazon, craigslist, Kiva, Thraedless, TopCoder,
ePinions, Yelp
Idea Generation IdeaConnection, IdeaScale
Virtual Worlds Webkinz, World of Warcraft, Club Penguin, Second Life
Mobile-based Services Foursquare, MapMyRun, Geocaching

8. Patterns are left behind

10. Online Community Analysis

11. • 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 Theory
http://en.wikipedia.org/wiki/Social_network

12. 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
C
D
E
A B D E

13. Social Network Analysis
A systematic method for understanding relationships
between entities.
Node-Specific Metrics
• Betweenness Centrality
• Degree Centrality
• Eigenvector Centrality
• Closeness Centrality
Network-Specific Metrics
• Components
• Density

17. Personal Email Collection

18. Mapping Corporate Email Communication
Between Research Groups

19. Mapping Events with Twitter EventGraphs

20. Who is Talking about Thimerosal on Twitter?

21. #occupywallstreet
15 November 2011
#teaparty
15 November 2011
http://www.newscientist.com/blogs/onepercent/2011/11/occupy-vs-tea-party-what-their.html

22. 6 kinds of Twitter social media networks

23. [Divided]
Polarized Crowds
[Unified]
Tight Crowd
[Fragmented]
Brand Clusters
[Clustered]
Community Clusters
[In-Hub & Spoke]
Broadcast Network
[Out-Hub & Spoke]
Support Network

24. 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

25. Inferring Relationships

26. Surgery Videos on YouTube

27. Finding Theorists in Lostpedia

28. NodeXL (http://nodexl.codeplex.com)

29. Social Media Research Foundation
People Disciplines Institutions
University Faculty Computer Science
Information Technology
University of Maryland
Brigham Young University
Students HCI, CSCW Oxford Internet Institute
Industry Machine Learning Stanford University
Independent Information Visualization Microsoft Research
Researchers UI/UX Illinois Institute of
Technology
Developers Social Science/Sociology Connected Action
Network Analysis Cornell
Collective Action Morningside Analytics

30. http://nodexl.codeplex.com

31. NodeXL
Graph Gallery

32. An Iterative Process

33. Data Sources Code it Yourself
Use Free 3rd Party Tools
APIs
Scrapers
Software
Libraries
Use Corporate Tools