Using Social Network Analysis to Understand         Web 2.0 Communications                    Sam Stewart, Syed Sibte Raza...
For more information on visualization tool:       Stewart S. and Sibte Raza Abidi S. (2011).       UNDERSTANDING MEDICINE ...
IntroductionExperiential Healthcare Knowledge     Experiential knowledge exists in a variety of modalities           clini...
IntroductionMedicine 2.0     Our researching investigates the use of Web 2.0 tools in     facilitating experiential knowle...
IntroductionProject Outline     This project focuses on the online communication patterns of     the Pediatric Pain Mailin...
Methods                              MethodsSam Stewart (Dal)             SNA and Med 2.0   September 18, 2011   6 / 29
MethodsSocial Network Analysis     The objective of SNA is to understand the underlying social     structure of a communic...
Methods1 vs 2 Mode Data    Traditional network analysis is on 1-mode data           1 set of actors, edges are the relatio...
Methods   CentralityCentrality     Centrality measures provide insight into the most important     actors in the network  ...
Methods   CentralityDegree Centrality     Degree centrality is simply measured as the number of ties an     actor has     ...
Methods     CentralityDegree Results     There are actors that are quite active in the network     With max normalized 2-M...
Methods   CentralityCloseness Centrality     An actor is “close” if they are within a few steps of every other     member ...
Methods   CentralityBetweenness Centrality     Betweenness centrality is a measure of how important a node is     as a hub...
Methods   CentralityCentrality Conclusions     The centrality measures indicate a healthy and active network            Lo...
Methods   Centrality1-Mode Degree 2-Mode Degree Closeness             Betweenness167           121           167          ...
Methods   Subgroup AnalysisSubgroup Analysis     With 700 users and over 13 000 messages on the network, there     is too ...
Methods   Subgroup AnalysisStructural Equivalence     Structural equivalence helps identify nodes that occupy similar     ...
5      3                                       146 102 101 45          158156                      32                     ...
Methods   Subgroup AnalysisAnalyzing the Blockmodel     We are interested in the communication patterns both within     an...
Methods   Subgroup AnalysisSam Stewart (Dal)              SNA and Med 2.0    September 18, 2011   20 / 29
Methods   Subgroup AnalysisSam Stewart (Dal)              SNA and Med 2.0    September 18, 2011   21 / 29
Methods   Subgroup AnalysisStructural Equivalence Results     The structural equivalence results have isolated two potenti...
Methods   VECoNVisualizing Social Networks     The objective of the VECoN system is            To provide the users with a...
Methods   VECoNSam Stewart (Dal)              SNA and Med 2.0   September 18, 2011   24 / 29
Methods   VECoNCurrent VECoN Status    The visualization is in its early stages           Node layout needs to be fixed    ...
ConclusionConclusions     Experiential healthcare knowledge is vital     Web 2.0 technologies provide tools for sharing kn...
ConclusionFuture Work    Research is currently being conducted to apply these methods to    a discussion forum    Need to ...
ConclusionAcknowledgementThis work is carried out with the aid of a grant from the InternationalDevelopment Research Centr...
Conclusion                                 Questions?Sam Stewart (Dal)                 SNA and Med 2.0   September 18, 201...
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Using Social Network Analysis to Understand Web 2.0 Communications

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Using Social Network Analysis to Understand Web 2.0 Communications

  1. 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, 2011Sam Stewart (Dal) SNA and Med 2.0 September 18, 2011 1 / 29
  2. 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: 10.5220/0003167100700079Sam Stewart (Dal) SNA and Med 2.0 September 18, 2011 2 / 29
  3. 3. IntroductionExperiential 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. 4. IntroductionMedicine 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. 5. IntroductionProject 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. 6. Methods MethodsSam Stewart (Dal) SNA and Med 2.0 September 18, 2011 6 / 29
  7. 7. MethodsSocial 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. 8. Methods1 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. 9. Methods CentralityCentrality 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. 10. Methods CentralityDegree 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. 11. Methods CentralityDegree 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. 12. Methods CentralityCloseness 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. 13. Methods CentralityBetweenness 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. 14. Methods CentralityCentrality 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. 15. Methods Centrality1-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 096Sam Stewart (Dal) SNA and Med 2.0 September 18, 2011 15 / 29
  16. 16. Methods Subgroup AnalysisSubgroup 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. 17. Methods Subgroup AnalysisStructural 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. 18. 5 3 146 102 101 45 158156 32 53 71Sam Stewart (Dal) 105 104 100 93 103 15 20 74 111 77 60 42169 55 212 210 56 141 9 224 226 227 223 220 222 57 64 122 221 225 181 170 230 193 186 47 234 171 233 232 231 229 217 218 35116 54 7 99 Methods 31 132 137 155 59 1 61 154 16 67 198 150 197 69 44 182 78 72 73 68 70 46 88 98 133 204 135 17 43 136 140 199 37 19 26 27 33 36 187 236 28 81 82 8029 114 2 4 6 134 107 52 126 85129 41 106 228 174 24 180 177 162 145 Subgroup Analysis 144 143 142 163 131 66 179SNA and Med 2.0 62 63 191 97 84 213 216 90 123 167 124 51 209 128 115 172 173 8 185 113 30 10 11 160 147 12 215 237 188 190 168 151 138 184 112 183 201 15791 202 34 1339 49 21965 211 176 121 110 25 89 75 14 21 40 194 166 195 96 94 95 109 119 152 118 153 76 175 120 159 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 92September 18, 201118 / 29
  19. 19. Methods Subgroup AnalysisAnalyzing 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. 20. Methods Subgroup AnalysisSam Stewart (Dal) SNA and Med 2.0 September 18, 2011 20 / 29
  21. 21. Methods Subgroup AnalysisSam Stewart (Dal) SNA and Med 2.0 September 18, 2011 21 / 29
  22. 22. Methods Subgroup AnalysisStructural 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. 23. Methods VECoNVisualizing 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. 24. Methods VECoNSam Stewart (Dal) SNA and Med 2.0 September 18, 2011 24 / 29
  25. 25. Methods VECoNCurrent 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. 26. ConclusionConclusions 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. 27. ConclusionFuture 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. 28. ConclusionAcknowledgementThis 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. Sam Stewart (Dal) SNA and Med 2.0 September 18, 2011 28 / 29
  29. 29. Conclusion Questions?Sam Stewart (Dal) SNA and Med 2.0 September 18, 2011 29 / 29

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