The document describes using social network analysis to understand communications on a pediatric pain mailing list with 700 members. Key findings include: 1) Centrality measures showed no single user dominated discussions, but some were more active. Closeness scores indicated members were well-connected. 2) Structural equivalence clustering divided members into one large group and two small subgroups with high internal communication. The nature of the subgroups was unclear. 3) The VECoN visualization tool was developed to display social network analysis results to mailing list members to provide insight into communication patterns.

1. Using Social Network Analysis to Understand
Web 2.0 Communications
Sam Stewart, Syed Sibte Raza Abidi
NICHE Research Group
Faculty of Computer Science
Dalhousie University, Halifax, Canada
sam.stewart@dal.ca
web.cs.dal.ca/∼sstewart
September 18, 2011
Sam Stewart (Dal) SNA and Med 2.0 September 18, 2011 1 / 29

2. For more information on visualization tool:
Stewart S. and Sibte Raza Abidi S. (2011).
UNDERSTANDING MEDICINE 2.0 - Social Network
Analysis and the VECoN System. In Proceedings of the
International Conference on Health Informatics, pages
70-79. DOI: 10.5220/0003167100700079
Sam Stewart (Dal) SNA and Med 2.0 September 18, 2011 2 / 29

3. Introduction
Experiential Healthcare Knowledge
Experiential knowledge exists in a variety of modalities
clinical case studies, problem-based discussions between
clinicians, experience-based insights, diagnostic heuristics ...
There are key issues facing the use of this knowledge in
healthcare
How to formulate a community of practitioners to create
this knowledge?
How to extract and share this knowledge?
How to assign value to the knowledge being shared,
especially with respect to clinical decision making?
Sam Stewart (Dal) SNA and Med 2.0 September 18, 2011 3 / 29

4. Introduction
Medicine 2.0
Our researching investigates the use of Web 2.0 tools in
facilitating experiential knowledge sharing, translation and
validation
Web 2.0 tools: online discussion forums, medical mailing lists,
blogs, social networking websites, ...
Provide virtual communities for knowledge exchange and
knowledge validation
We want to explore the knowledge sharing dynamics of web 2.0
communities
We will do this using Social Network Analysis (SNA)
Sam Stewart (Dal) SNA and Med 2.0 September 18, 2011 4 / 29

5. Introduction
Project Outline
This project focuses on the online communication patterns of
the Pediatric Pain Mailing List (PPML)
700 pediatric pain practitioners from around the world share
their clinical experiences and seek advice
Not a strong example of web 2.0 data
Structurally, mailing list data and discussion forum data are
very similar
Already a strong community between the members (both
professionally and on the mailing list)
Sam Stewart (Dal) SNA and Med 2.0 September 18, 2011 5 / 29

6. Methods
Methods
Sam Stewart (Dal) SNA and Med 2.0 September 18, 2011 6 / 29

7. Methods
Social Network Analysis
The objective of SNA is to understand the underlying social
structure of a communication network
It leverages principles of graph theory to represent people and
the ties between them
It focuses on analyzing the structures that emerge out of
relations between actors, rather than the attributes of actors
themselves
Sam Stewart (Dal) SNA and Med 2.0 September 18, 2011 7 / 29

8. Methods
1 vs 2 Mode Data
Traditional network analysis is on 1-mode data
1 set of actors, edges are the relations between them
This project studies 2-mode networks
2 types of actors, and the ties are between types
Our data links a user to a thread if that user communicated
on that thread
Because many SNA methods are designed for 1-mode networks,
it is necessary to create a 1-mode network out of our two mode
data
A valued link exists between two users for how many
threads they communicated on together
Sam Stewart (Dal) SNA and Med 2.0 September 18, 2011 8 / 29

9. Methods Centrality
Centrality
Centrality measures provide insight into the most important
actors in the network
We used three different centrality measures
Degree
Closeness
Betweenness
They will provide both user level information about the most
important users, along with general network level information
Sam Stewart (Dal) SNA and Med 2.0 September 18, 2011 9 / 29

10. Methods Centrality
Degree Centrality
Degree centrality is simply measured as the number of ties an
actor has
Degree can be normalized to a [0,1] scale by dividing it by its
maximum
Results:
Actor 2M Deg Norm Actor 1M Deg Norm
121 42 0.1772 167 85 0.3602
167 41 0.1730 170 75 0.3178
066 36 0.1519 066 67 0.2839
055 35 0.1477 128 66 0.2797
170 31 0.1308 055 59 0.2500
Sam Stewart (Dal) SNA and Med 2.0 September 18, 2011 10 / 29

11. Methods Centrality
Degree Results
There are actors that are quite active in the network
With max normalized 2-Mode degree of 17.7%, there is not one
actor that is present in all the threads
The 1-mode degrees are slightly higher: the most active users
have communicated with ≈ 36% of the other users
Distribution of two−mode Degrees Distribution of Actor Degrees
120
100
150
80
Frequency
Frequency
100
60
40
50
20
0
0
0 10 20 30 40 0 20 40 60 80
two−mode degree Actor degree
Sam Stewart (Dal) SNA and Med 2.0 September 18, 2011 11 / 29

12. Methods Centrality
Closeness Centrality
An actor is “close” if they are within a few steps of every other
member of the network
A network with high closeness values means that information
can propagate through the network quickly
Closeness in Actor Network
Actor Closeness
167 0.5915
60
170 0.5742 Frequency
40
128 0.5579 20
066 0.5540
055 0.5527
0
0.25 0.30 0.35 0.40 0.45 0.50 0.55 0.60
Closeness
Sam Stewart (Dal) SNA and Med 2.0 September 18, 2011 12 / 29

13. Methods Centrality
Betweenness Centrality
Betweenness centrality is a measure of how important a node is
as a hub of information
Low betweenness scores mean that no-one controls the
information flow through the network
Distribution of Actor Betweenness scores
Actor Betweenness
150
167 0.107
170 0.093
100
Frequency
066 0.080
50
128 0.063
035 0.063
0
0.00 0.02 0.04 0.06 0.08 0.10 0.12 0.14
Normalized Betweenness
Sam Stewart (Dal) SNA and Med 2.0 September 18, 2011 13 / 29

14. Methods Centrality
Centrality Conclusions
The centrality measures indicate a healthy and active network
Low degree and betweenness scores indicate that there is
not a single user or set of users dominating the network
High closeness scores indicate that users are all closely
connected to one another
Note that the same actors are near the top of each group
Though they don’t dominate the network, there are power users
present
Sam Stewart (Dal) SNA and Med 2.0 September 18, 2011 14 / 29

15. Methods Centrality
1-Mode Degree 2-Mode Degree Closeness Betweenness
167 121 167 167
170 167 170 170
066 066 128 066
128 055 066 128
055 170 055 035
056 035 035 179
184 148 184 020
035 179 121 121
020 184 042 184
121 020 020 266
179 128 056 055
042 224 045 056
254 102 015 015
224 146 179 224
045 015 077 096
Sam Stewart (Dal) SNA and Med 2.0 September 18, 2011 15 / 29

16. Methods Subgroup Analysis
Subgroup Analysis
With 700 users and over 13 000 messages on the network, there
is too much information to present all messages at once
The idea of subgroup analysis is to group similar actors together,
and only study the communications within groups, or between
groups
Also called cluster analysis, there are a number of methods for
determining the clusters
Going to look at structural equivalence
Sam Stewart (Dal) SNA and Med 2.0 September 18, 2011 16 / 29

17. Methods Subgroup Analysis
Structural Equivalence
Structural equivalence helps identify nodes that occupy similar
roles in the network
Two nodes are structurally equivalent if they both contain all
the same ties
True structural equivalence is rare, so we measure approximate
equivalence using Hamming/Euclidean distance
Develop a similarity matrix between all users
If we cluster users together hierarchically we create a dendogram
Cutting the dendogram results in disparate clusters (a
blockmodel)
Sam Stewart (Dal) SNA and Med 2.0 September 18, 2011 17 / 29

18. 5 3
146 102 101 45 158156
32
53 71
Sam Stewart (Dal)
105
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103 15
20
74 111
77 60 42169 55
212
210 56 141 9
224 226
227
223
220
222
57 64
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221 225 181 170
230 193 186 47
234 171
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232
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217
218 35116
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Methods
31 132
137 155 59 1
61
154 16
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198 150 197 69
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88 98 133 204
135 17
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85129
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228 174 24
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145
Subgroup Analysis
144
143 142 163 131
66 179
SNA and Med 2.0
62
63
191 97
84
213
216 90
123
167
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209 128 115
172
173
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185 113 30
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201 15791
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89 75
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152 118
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83 164
117 22
Hierarchical Clustering of the Actor Network
200
125 4858
148
149
207
208
178
192 189 130
87
86 79 38
161
214 23
108 50 18 165
203
127 139 235
196
206 205
92
September 18, 2011
18 / 29

19. Methods Subgroup Analysis
Analyzing the Blockmodel
We are interested in the communication patterns both within
and between blocks
The best partitioning of the actors breaks the network into one
large group and two small groups
The image matrix presents the communication densities between
and within the three blocks
B1 B2 B3
B1(n=199) 0.04497 0.08124 0.07538
B2(n=18) 0.08124 0.92157 0.12778
B3(n=20) 0.07538 0.12778 1.00000
Two small networks have very high densities, and some
communication between them, the large group has low density,
and little communication with the two other groups
Sam Stewart (Dal) SNA and Med 2.0 September 18, 2011 19 / 29

20. Methods Subgroup Analysis
Sam Stewart (Dal) SNA and Med 2.0 September 18, 2011 20 / 29

21. Methods Subgroup Analysis
Sam Stewart (Dal) SNA and Med 2.0 September 18, 2011 21 / 29

22. Methods Subgroup Analysis
Structural Equivalence Results
The structural equivalence results have isolated two potential
subgroups of interest in the network
Dataset only contains names and email addresses: nothing
to differentiate between two groups
Investigation of common threads amongst the blocks
revealed nothing
Full survey of the group could reveal common group
attributes (research ongoing)
Could also investigate clustering directly from the two-mode
network
Sam Stewart (Dal) SNA and Med 2.0 September 18, 2011 22 / 29

23. Methods VECoN
Visualizing Social Networks
The objective of the VECoN system is
To provide the users with an overview of the structure of
the mailing list
To provide SNA results to the users with the hope of
improving their knowledge translation practices
To provide a novel network navigation tool
Is not an analysis system
Many great network analysis tools exist: UCINET and
Netdraw, GUESS, Gephi, SocialAction, R, ...
Goal is to provide end users with a graph visualization to
accompany their traditional network navigation methods
Sam Stewart (Dal) SNA and Med 2.0 September 18, 2011 23 / 29

24. Methods VECoN
Sam Stewart (Dal) SNA and Med 2.0 September 18, 2011 24 / 29

25. Methods VECoN
Current VECoN Status
The visualization is in its early stages
Node layout needs to be fixed
Clustering needs to be improved
More centrality measures need to be added
Connection to the actual conversations needs to be
implemented
The project demonstrates the potential for graph-based
visualizations to improve the navigation and understanding of
communication networks from a user’s point of view
Sam Stewart (Dal) SNA and Med 2.0 September 18, 2011 25 / 29

26. Conclusion
Conclusions
Experiential healthcare knowledge is vital
Web 2.0 technologies provide tools for sharing knowledge,
establishing virtual communities of practice
It is vital that we understand how these communities function
SNA provides tools for understand how online communication
networks function
Sam Stewart (Dal) SNA and Med 2.0 September 18, 2011 26 / 29

27. Conclusion
Future Work
Research is currently being conducted to apply these methods to
a discussion forum
Need to quantify contribution to the conversation (is currently a
binary measure)
Develop knowledge seekers and knowledge sharers
Rollout the visualization tool to users
Sam Stewart (Dal) SNA and Med 2.0 September 18, 2011 27 / 29

28. Conclusion
Acknowledgement
This work is carried out with the aid of a grant from the International
Development Research Centre, Ottawa, Canada.
The authors would like to acknowledge Dr. Allen Finley for his
contributions to the PPML and his ongoing support of this research.
Sam Stewart (Dal) SNA and Med 2.0 September 18, 2011 28 / 29

29. Conclusion
Questions?
Sam Stewart (Dal) SNA and Med 2.0 September 18, 2011 29 / 29