Background: Detecting community structures in complex networks is a problem interesting to several domains. In healthcare, discovering communities may enhance the quality of web offerings for people with chronic diseases. Understanding the social dynamics and community attachments is key to predicting and influencing interaction and information flow to the right patients.
Objectives: The goal of the study is to empirically assess the extent to which we can infer meaningful community structures from implicit networks of peer interaction in online healthcare forums.
Methods: We used datasets from five online diabetes forums to design networks based on peer-interactions. A quality function based on user interaction similarity was used to assess the quality of the discovered communities to complement existing homophily measures.
Results: Results show that we can infer meaningful communities by observing forum interactions. Closely similar users tended to co-appear in the top communities, suggesting the discovered communities are intuitive. The number of years since diagnosis was a significant factor for cohesiveness in some diabetes communities.
Conclusion: Network analysis is a tool that can be useful in studying implicit networks that form in healthcare forums. Current analysis informs further work on predicting and influencing interaction, information flow and user interests that could be useful for personalizing medical social media.
Keywords
Diabetes -
social networks -
homophily -
assortativity -
community detection
References
1
Lefebvre P,
Pierson A.
The global challenge of diabetes. World hospitals and health services. The official journal of the International Hospital Federation 2004; 40 (03) 37-40. 2. Epub 2004/11/30
5
Durant KT,
McCray AT,
Safran C.
Modeling the temporal evolution of an online cancer forum. In: Proceedings of the 1st ACM International Health Informatics Symposium; Arlington, Virginia, USA. 1883042: ACM. 2010: 356-365.
6
Durant KT,
McCray AT,
Safran C.
Identifying Temporal Changes and Topics that Promote Growth Within Online Communities: A Prospective Study of Six Online Cancer Forums. International journal of computational models and algorithms in medicine 2011; 2 (02) 1-22. Epub 2011/10/25
7
Dawson SP.
Online forum discussion interactions as an indicator of student community. Australasian Journal of Educational Technology 2006; 22 (04) 495-510.
8
L’Huillier G,
Ríos SA,
Alvarez H,
Aguilera F.
Topic-based social network analysis for virtual communities of interests in the Dark Web. ACM SIGKDD Workshop on Intelligence and Security Informatics; Washington, D.C. 1938615: ACM. 2010: 1-9.
9
Dunn AG,
Westbrook JI.
Interpreting social network metrics in healthcare organisations: a review and guide to validating small networks. Soc Sci Med 2011; 72 (07) 1064-1068. Epub 2011/03/05
11
Alvarez FP,
Crepey P,
Barthelemy M,
Valleron AJ.
sispread: A software to simulate infectious diseases spreading on contact networks. Methods Inf Med 2007; 46 (01) 19-26. Epub 2007/01/17
12
Cobb NK,
Graham AL,
Abrams DB.
Social network structure of a large online community for smoking cessation. American journal of public health 2010; 100 (07) 1282-1289. Epub 2010/05/15
13
Ma X,
Chen G,
Xiao J.
Analysis of an online health social network. In: Proceedings of the 1st ACM International Health Informatics Symposium; Arlington, Virginia, USA. 1883035: ACM. 2010: 297-306.
14
Martinez-Lopez B,
Perez AM,
Sanchez-Vizcaino JM.
Social network analysis. Review of general concepts and use in preventive veterinary medicine. Transboundary and emerging diseases 2009; 56 (04) 109-120. Epub 2009/04/04
15
Luke DA,
Harris JK.
Network analysis in public health: history, methods, and applications. Annual review of public health 2007; 28: 69-93. Epub 2007/01/16
18
Fowler JH,
Christakis NA.
Dynamic spread of happiness in a large social network: longitudinal analysis over 20 years in the Framingham Heart Study. BMJ 2008; 337: a2338 Epub 2008/12/06
21
Burton S,
Morris R,
Dimond M,
Hansen J,
Giraud-Carrier C,
West J.
et al Public health community mining in YouTube. In: Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium; Miami, Florida, USA. 2110376: ACM. 2012: 81-90.
22
Bhavnani SK,
Bellala G,
Ganesan A,
Krishna R,
Saxman P,
Scott C.
et al The nested structure of cancer symptoms. Implications for analyzing co-occurrence and managing symptoms. Methods Inf Med 2010; 49 (06) 581-591. Epub 2010/11/19
23
Boutin F,
Hascoet M.
Cluster Validity Indices for Graph Partitioning. In: Proceedings of the Information Visualisation, Eighth International Conference. 1021645: IEEE Computer Society. 2004: 376-381.
25
Girvan M,
Newman ME.
Community structure in social and biological networks. Proceedings of the National Academy of Sciences of the United States of America 2002; 99 (12) 7821-7826. Epub 2002/06/13
29
Erdal BS,
Liu J,
Ding J,
Chen J,
Marsh CB,
Kamal J.
et al A Database De-identification Framework to Enable Direct Queries on Medical Data for Secondary Use. Methods Inf Med 2012; 51 (03) 229-241. Epub 2012/02/09
31
Newman ME.
Modularity and community structure in networks. Proceedings of the National Academy of Sciences of the United States of America 2006; 103 (23) 8577-8582. Epub 2006/05/26
35
Aral S,
Muchnik L,
Sundararajan A.
Distinguishing influence-based contagion from homophily-driven diffusion in dynamic networks. Proceedings of the National Academy of Sciences 2009; 106 (51) 21544-21549.
36
Akcora CG,
Carminati B,
Ferrari E.
editors Network and profile based measures for user similarities on social networks. Information Reuse and Integration (IRI), 2011 IEEE International Conference on; Aug. 3–5. 2011.
38
Pan Y,
Li D-H,
Liu J-G,
Liang J-Z.
Detecting community structure in complex networks via node similarity. Physica A: Statistical Mechanics and its Applications 2010; 389 (14) 2849-2857.
40
Frantz T,
Cataldo M,
Carley K.
Robustness of centrality measures under uncertainty: Examining the role of network topology. Computational andMathematical Organization Theory 2009; 15 (04) 303-328.