Mapping social media networks with no coding using NodeXL - presented at Strata 2012

2. Mapping social
media networks
(with no coding)
using NodeXL
A project from the Social Media Research Foundation: http://www.smrfoundation.org

3. Social Media Research Foundation
http://smrfoundation.org

4. Social Media Research Foundation
People Disciplines Institutions
University Computer Science University of Maryland
Faculty
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

5. About Me
Introductions
Marc A. Smith
Chief Social Scientist
Connected Action Consulting Group
Marc@connectedaction.net
http://www.connectedaction.net
http://www.codeplex.com/nodexl
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

6. What we are trying to do:
Open Tools, Open Data, Open Scholarship
• Build the “Firefox of GraphML” – open tools for
collecting and visualizing social media data
• Connect users to network analysis – make
network charts as easy as making a pie chart
• Connect researchers to social media data sources
• Archive: Be the “Allen Very Large Telescope Array”
for Social Media data – coordinate and aggregate
the results of many user’s data collection and
analysis
• Create open access research papers & findings
• Make “collections of connections” easy for users
to manage

7. What we have done: Open Tools
• NodeXL
• Data providers (“spigots”)
– ThreadMill Message Board
– Exchange Enterprise Email
– Voson Hyperlink
– SharePoint
– Facebook
– Twitter
– YouTube
– Flickr

8. What we have done: Open Data
• NodeXLGraphGallery.org
– User generated collection
of network graphs,
datasets and annotations
– Collective repository for
the research community
– Published collections of
data from a range of social
media data sources to help
students and researchers
connect with data of
interest and relevance

9. What we have done: Open Scholarship

10. What we have done: Open Scholarship

11. We envision hundreds of NodeXL data collectors around the world collectively
generating a free and open archive of social media network snapshots on a
wide range of topics.
http://msnbcmedia.msn.com/i/msnbc/Components/Photos/071012/071012_telescope_hmed_3p.jpg

12. Social Media
(email, Facebook, Twitter,
YouTube, and more)
is all about
connections
from people
to people.
12

13. Patterns are
left behind
13

14. There are many kinds of ties….
Like, Link, Reply, Rate, Review, Favorite, Friend, Follow, Forward, Edit, Tag, Comment, Check-in…
http://www.flickr.com/photos/stevendepolo/3254238329

15. “Think Link”
Nodes & Edges
Is related to
A B

16. Strong ties

17. Weak ties

18. Social
Networks
• History:
from the
dawn of
time!
• Theory and
method:
1934 ->
• Jacob L.
Moreno
• http://en.wiki
pedia.org/wiki
/Jacob_L._Mor
eno
Jacob Moreno’s early social network diagram of positive and negative relationships among members of a football
team.
Originally published in Moreno, J. L. (1934). Who shall survive? Washington, DC: Nervous and Mental Disease
Publishing Company.

19. Each contains one or more
social networks
World Wide Web

21. Hubs

22. Bridges

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

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

26. Like MSPaint™ for graphs.
— the Community
Introduction to NodeXL

27. NodeXL
Network Overview Discovery and Exploration add-in for Excel 2007/2010
A minimal network can
illustrate the ways different
locations have different values
for centrality and degree

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

29. Social Network Theory
http://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),
Source: Richards, W.
– betweenness (1986). The NEGOPY
• Methods network analysis
program. Burnaby, BC:
– Surveys, interviews, observations, Department of
Communication, Simon
log file analysis, computational Fraser University. pp.7-
analysis of matrices 16
(Hampton &Wellman, 1999; Paolillo, 2001; Wellman, 2001)

30. SNA 101
• Node
A
– “actor” on which relationships act; 1-mode versus 2-mode networks
• Edge
B – Relationship connecting nodes; can be directional
C • Cohesive Sub-Group
– Well-connected group; clique; cluster A B D E
• Key Metrics
– Centrality (group or individual measure)
D • Number of direct connections that individuals have with others in the group (usually look at
incoming connections only)
E • 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)
F G • # shortest paths between each node pair that a node is on
• Measure at the individual node level
• Node roles
– Peripheral – below average centrality C
H – Central connector – above average centrality D
I – Broker – above average betweenness E

31. 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

33. http://www.flickr.com/photos/marc_smith/sets/72157622437066929/

34. NodeXL
Free/Open Social Network Analysis add-in for Excel 2007/2010 makes graph
theory as easy as a pie chart, with integrated analysis of social media sources.
http://nodexl.codeplex.com

35. Now Available

36. Communities
in Cyberspace

37. Twitter Network for “Microsoft Research”
*BEFORE*

38. Twitter Network for “Microsoft Research”
*AFTER*

39. NodeXL Ribbon in Excel

40. NodeXL data import sources

41. Example NodeXL data importer for Twitter

42. NodeXL imports “edges” from social media data sources

43. NodeXL displays subgraph images along with network metadata
NodeXL creates a list of “vertices” from imported social media edges

44. Perform
collections of
common
operations with
NodeXL a single click
Automation
makes analysis
simple and fast

45. NodeXL Network Metrics

46. NodeXL “Autofill columns” simplifies mapping data attributes to display attributes

48. NodeXL enables filtering of networks

49. NodeXL Generates Overall Network Metrics

56. Social Network Maps Reveal
Key influencers in any topic.
Sub-groups.
Bridges.

57. What we want to do:
(Build the tools to) map the social web
• Move NodeXL to the web:
– Node for Google Doc Spreadsheets!
– WebGL Canvas
• Connect to more data sources of interest:
– RDF, MediaWikis, Gmail, NYT, Citation Networks
• Solve hard network manipulation UI problems:
– Modal transform, Time series, Automated layouts
• Grow and maintain archives of social media network data sets for
research use.
• Improve network science education:
– Workshops on social media network analysis
– Live lectures and presentations
– Videos and training materials

58. 2012 Schedule: Planned Workshops
March 1 - Strata
March 5 2012 – PAWCON
June 2012 - ICWSM
July 2012 – Lipari School on Complexity
August 8, 2012 - AEJMC
August 21, 2012 – Webshop 2012

59. Pending Work Items
Autofill Group Attribute
Merge Edges by Attribute
Modal Transform
Merge Workbooks
Automated Dynamic Filters: Time Series Analysis, contrast
Captions and Legends
Upload to Graph Gallery++: captions, workbook
Graph Gallery++
User Accounts, Reporting, RSS Feeds,
Network Visualization Web Canvas
Import: RDF, Wiki, SharePoint, Keyword networks from text
Metrics: Triad Census
Layouts:
Force Atlas 2, Lin Log, “Bakshy Plots”, Quality Measures
Query-by-example search for network structures

60. How you can help
• Sponsor a feature
• Sponsor Webshop 2012
• Sponsor a student
• Schedule training
• Sponsor the foundation
• Donate your money, code, computation, storage,
bandwidth, data or employee’s time
• Help promote the work of the Social Media
Research Foundation

61. Thank you!
The Social Media Research Foundation
http://www.smrfoundation.org

62. Backup
• Examples of social media network analysis
• Sources of social network analysis material

63. Who is the mayor of your hashtag?
Find out at: http://netbadges.com

64. Who is the mayor of your hashtag?
Find out at: http://netbadges.com

65. Who is the mayor of your hashtag?
http://netbadges.com
Find out at: http://netbadges.com