Abstract
Opinion clustering arises from the collective behavior of a social network. We apply an Opinion Dynamics model to investigate opinion cluster formation in the presence of community structure. Opinion clustering is influenced by the properties of individuals (nodes) and network topology. We determine the sensitivity of opinion cluster formation to changes in node tolerance levels through parameter sweeps. We investigate the effect of network community structure through rewiring the network to lower the community structure. Tolerance variation modifies the effects of community structure on opinion clustering: higher values of tolerance lead to less distinct opinion clustering. Community structure is found to inhibit network wide clusters from forming. We claim that advancing understanding of the role of community structure in social networks can help lead to more informed and effective public health policy.
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References
Smith, K.P., Christakis, N.A.: Social networks and health. Annual Review of Sociology 34, 405–429 (2008)
Glass, L.M., Glass, R.J.: Social contact networks for the spread of pandemic influenza in children and teenagers. BMC Public Health 8(1), 61 (2008)
Christakis, N.A., Fowler, J.H.: The spread of obesity in a large social network over 32 years. New England Journal of Medicine 357(4), 370 (2007)
Moore, T.W., Finley, P.D., Linebarger, J.M., Outkin, A.V., Verzi, S.J., Brodsky, N.S., Cannon, D.C., Zagonel, A.A., Glass, R.J.: Extending Opinion Dynamics to Model Public Health Problems and Analyze Public Policy Interventions. In: Eighth International Conference on Complex Systems, Quincy, MA (2011)
Fowler, J.H., Christakis, N.A.: Dynamic spread of happiness in a large social network: longitudinal analysis over 20 years in the Framingham Heart Study. British Medical Journal 337(dec04 2), a2338 (2008)
Girvan, M., Newman, M.E.J.: Community structure in social and biological networks. Proceedings of the National Academy of Sciences 99(12), 7821–7826 (2002)
Granovetter, M.: The strength of weak ties: A network theory revisited. Sociological Theory 1, 201–233 (1983)
McPherson, M., Smith-Lovin, L., Cook, J.M.: Birds of a feather: Homophily in social networks. Annual Review of Sociology 27(1), 415–444 (2001)
Colbaugh, R., Glass, K.: Predictive analysis for social diffusion: The role of network communities. Arxiv preprint arXiv:0912.5242 (2009)
Cartwright, D., Harary, F.: Structural Balance: A Generalization of Heider’s Theory. Psychological Review 63(5) (1956)
Castellano, C., Fortunato, S., Loreto, V.: Statistical physics of social dynamics. Reviews of Modern Physics 81(2), 591–646 (2009)
Weisbuch, G., Deffuant, G., Amblard, F., Nadal, J.P.: Meet, discuss, and segregate! Complexity 7(3), 55–63 (2002)
Deffuant, G.: Comparing extremism propagation patterns in continuous opinion models. Journal of Artificial Societies and Social Simulation 9(3) (2006)
Moore, T., Finley, P., Hammer, R., Glass, R.: Opinion dynamics in gendered social networks: An examination of female engagement teams in Afghanistan. Social Computing, Behavioral-Cultural Modeling and Prediction, 69–77 (2012)
Ballerini, M., Cabibbo, N., Candelier, R., Cavagna, A., Cisbani, E., Giardina, I., Lecomte, V., Orlandi, A., Parisi, G., Procaccini, A.: Interaction ruling animal collective behavior depends on topological rather than metric distance: Evidence from a field study. Proceedings of the National Academy of Sciences 105(4), 1232–1237 (2008)
Ben-Jacob, E., Cohen, I., Gutnick, D.L.: Cooperative organization of bacterial colonies: From genotype to morphotype. Annual Reviews in Microbiology 52(1), 779–806 (1998)
Gautrais, J., Jost, C., Theraulaz, G.: Key behavioural factors in a self-organised fish school model. Annales Zoologici Fennici 45, 415–428 (2008)
Moussaïd, M., Perozo, N., Garnier, S., Helbing, D., Theraulaz, G.: The walking behaviour of pedestrian social groups and its impact on crowd dynamics. PloS One 5(4), e10047 (2010)
Salathé, M., Bonhoeffer, S.: The effect of opinion clustering on disease outbreaks. Journal of The Royal Society Interface 5(29), 1505–1508 (2008)
Schelling, T.C.: Dynamic models of segregation. Journal of Mathematical Sociology 1(2), 143–186 (1971)
Kozma, B., Barrat, A.: Consensus formation on adaptive networks. Phys. Rev. EÂ 77(1) (January 2008)
Scott, J.: Social Network Analysis: A Handbook. Sage Publications Ltd., London (2000)
Christakis, N.A., Fowler, J.H.: The collective dynamics of smoking in a large social network. New England Journal of Medicine 358(21), 2249 (2008)
Ester, M., Kriegel, H.P., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining, vol. 1996, pp. 226–231 (1996)
Erdős, P., Rényi, A.: On the evolution of random graphs. Magyar Tud. Akad. Mat. Kutató Int. Közl 5, 17–61 (1960)
Newman, M.E.J., Girvan, M.: Finding and evaluating community structure in networks. Physical Review EÂ 69(2), 026113 (2004)
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Hammer, R.J., Moore, T.W., Finley, P.D., Glass, R.J. (2013). The Role of Community Structure in Opinion Cluster Formation. In: Glass, K., Colbaugh, R., Ormerod, P., Tsao, J. (eds) Complex Sciences. Complex 2012. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 126. Springer, Cham. https://doi.org/10.1007/978-3-319-03473-7_11
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DOI: https://doi.org/10.1007/978-3-319-03473-7_11
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