Using Social Network Analysis to Understand Web 2.0 Communications

Using Social Network Analysis to Understand

Web 2.0 Communications

Sam Stewart, Syed Sibte Raza Abidi

NICHE Research GroupFaculty of Computer Science

Dalhousie University, Halifax, Canada

[email protected]/∼sstewart

September 18, 2011

Sam Stewart (Dal) SNA and Med 2.0 September 18, 2011 1 / 29

For more information on visualization tool:

I Stewart S. and Sibte Raza Abidi S. (2011).UNDERSTANDING MEDICINE 2.0 - Social NetworkAnalysis and the VECoN System. In Proceedings of theInternational Conference on Health Informatics, pages70-79. DOI: 10.5220/0003167100700079


Introduction

Experiential Healthcare Knowledge

Experiential knowledge exists in a variety of modalities

I clinical case studies, problem-based discussions betweenclinicians, experience-based insights, diagnostic heuristics ...

There are key issues facing the use of this knowledge inhealthcare

I How to formulate a community of practitioners to createthis knowledge?

I How to extract and share this knowledge?

I How to assign value to the knowledge being shared,especially with respect to clinical decision making?


Introduction

Medicine 2.0

Our researching investigates the use of Web 2.0 tools infacilitating experiential knowledge sharing, translation andvalidation

Web 2.0 tools: online discussion forums, medical mailing lists,blogs, social networking websites, ...

Provide virtual communities for knowledge exchange andknowledge validation

We want to explore the knowledge sharing dynamics of web 2.0communities

I We will do this using Social Network Analysis (SNA)


Introduction

Project Outline

This project focuses on the online communication patterns ofthe Pediatric Pain Mailing List (PPML)

I 700 pediatric pain practitioners from around the world sharetheir clinical experiences and seek advice

Not a strong example of web 2.0 data

I Structurally, mailing list data and discussion forum data arevery similar

I Already a strong community between the members (bothprofessionally and on the mailing list)


Methods

Methods


Methods

Social Network Analysis

The objective of SNA is to understand the underlying socialstructure of a communication network

It leverages principles of graph theory to represent people andthe ties between them

It focuses on analyzing the structures that emerge out ofrelations between actors, rather than the attributes of actorsthemselves


Methods

1 vs 2 Mode Data

Traditional network analysis is on 1-mode data

I 1 set of actors, edges are the relations between them

This project studies 2-mode networks

I 2 types of actors, and the ties are between types

I Our data links a user to a thread if that user communicatedon 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 modedata

I A valued link exists between two users for how manythreads they communicated on together


Methods Centrality

Centrality

Centrality measures provide insight into the most importantactors in the network

We used three different centrality measures

I Degree

I Closeness

I Betweenness

They will provide both user level information about the mostimportant users, along with general network level information


Methods Centrality

Degree Centrality

Degree centrality is simply measured as the number of ties anactor has

Degree can be normalized to a [0,1] scale by dividing it by itsmaximum

Results:

Actor 2M Deg Norm121 42 0.1772167 41 0.1730066 36 0.1519055 35 0.1477170 31 0.1308

Actor 1M Deg Norm167 85 0.3602170 75 0.3178066 67 0.2839128 66 0.2797055 59 0.2500


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 oneactor that is present in all the threads

The 1-mode degrees are slightly higher: the most active usershave communicated with ≈ 36% of the other users

Distribution of two−mode Degrees

two−mode degree

Fre

quen

cy

0 10 20 30 40

050

100

150

Distribution of Actor Degrees

Actor degree

Fre

quen

cy

0 20 40 60 80

020

4060

8010

012

0


Methods Centrality

Closeness Centrality

An actor is “close” if they are within a few steps of every othermember of the network

A network with high closeness values means that informationcan propagate through the network quickly

Actor Closeness167 0.5915170 0.5742128 0.5579066 0.5540055 0.5527

Closeness in Actor Network

Closeness

Fre

quen

cy

0.25 0.30 0.35 0.40 0.45 0.50 0.55 0.60

020

4060


Methods Centrality

Betweenness Centrality

Betweenness centrality is a measure of how important a node isas a hub of information

Low betweenness scores mean that no-one controls theinformation flow through the network

Actor Betweenness167 0.107170 0.093066 0.080128 0.063035 0.063

Distribution of Actor Betweenness scores

Normalized Betweenness

Fre

quen

cy

0.00 0.02 0.04 0.06 0.08 0.10 0.12 0.14

050

100

150


Methods Centrality

Centrality Conclusions

The centrality measures indicate a healthy and active network

I Low degree and betweenness scores indicate that there isnot a single user or set of users dominating the network

I High closeness scores indicate that users are all closelyconnected 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 userspresent


Methods Centrality

1-Mode Degree 2-Mode Degree Closeness Betweenness167 121 167 167170 167 170 170066 066 128 066128 055 066 128055 170 055 035056 035 035 179184 148 184 020035 179 121 121020 184 042 184121 020 020 266179 128 056 055042 224 045 056254 102 015 015224 146 179 224045 015 077 096


Methods Subgroup Analysis

Subgroup Analysis

With 700 users and over 13 000 messages on the network, thereis 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 betweengroups

Also called cluster analysis, there are a number of methods fordetermining the clusters

I Going to look at structural equivalence



Structural Equivalence

Structural equivalence helps identify nodes that occupy similarroles in the network

Two nodes are structurally equivalent if they both contain allthe same ties

True structural equivalence is rare, so we measure approximateequivalence 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 (ablockmodel)



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Hierarchical Clustering of the Actor Network



Analyzing the Blockmodel

We are interested in the communication patterns both withinand between blocks

The best partitioning of the actors breaks the network into onelarge group and two small groups

The image matrix presents the communication densities betweenand within the three blocks

B1 B2 B3B1(n=199) 0.04497 0.08124 0.07538

B2(n=18) 0.08124 0.92157 0.12778B3(n=20) 0.07538 0.12778 1.00000

Two small networks have very high densities, and somecommunication between them, the large group has low density,and little communication with the two other groups







Structural Equivalence Results

The structural equivalence results have isolated two potentialsubgroups of interest in the network

I Dataset only contains names and email addresses: nothingto differentiate between two groups

I Investigation of common threads amongst the blocksrevealed nothing

I Full survey of the group could reveal common groupattributes (research ongoing)

Could also investigate clustering directly from the two-modenetwork


Methods VECoN

Visualizing Social Networks

The objective of the VECoN system is

I To provide the users with an overview of the structure ofthe mailing list

I To provide SNA results to the users with the hope ofimproving their knowledge translation practices

I To provide a novel network navigation tool

Is not an analysis system

I Many great network analysis tools exist: UCINET andNetdraw, GUESS, Gephi, SocialAction, R, ...

I Goal is to provide end users with a graph visualization toaccompany their traditional network navigation methods


Methods VECoN


Methods VECoN

Current VECoN Status

The visualization is in its early stages

I Node layout needs to be fixed

I Clustering needs to be improved

I More centrality measures need to be added

I Connection to the actual conversations needs to beimplemented

The project demonstrates the potential for graph-basedvisualizations to improve the navigation and understanding ofcommunication networks from a user’s point of view


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 communicationnetworks function


Conclusion

Future Work

Research is currently being conducted to apply these methods toa discussion forum

Need to quantify contribution to the conversation (is currently abinary measure)

Develop knowledge seekers and knowledge sharers

Rollout the visualization tool to users


Conclusion

Acknowledgement

This work is carried out with the aid of a grant from the InternationalDevelopment Research Centre, Ottawa, Canada.

The authors would like to acknowledge Dr. Allen Finley for hiscontributions to the PPML and his ongoing support of this research.


Conclusion

Questions?


Education

Using Social Network Analysis to Understand Web 2.0 Communications