Researchers demonstrate how social networks and opinions interlink and evolve

How communities interlink within opinion networks has significant bearing on social systems’ evolution and degrees of tolerance. Researchers have intensively modeled the networks since the 1990s, but the results have not satisfactorily matched collected data representations. Recently, researchers from Singapore and China put together a new model for explicating opinion diversity and adaptive networks. They explain their work in Chaos: An Interdisciplinary Journal of Nonlinear Science.

Lead author Yu Yi says that they built from earlier opinion formation models that focus on consensus formation, mutation (random opinion changes, for example, caused by religious conversion or intensive immigration) and rewiring, which describes linkage breaks ensuing from disagreement. These earlier models often characterize opinion communities as cutting themselves off from dissenters, whereas real world sources suggest significantly maintained or reconfigured connections. Yi and colleagues kept the three fundamental focuses but innovatively combined them to converge in one model.

Yi says for simulation he used the C language, which enabled for quicker computation. For theorizing, whereas earlier models did not consider the correlation of opinions with neighbors’ opinions, the Chaos paper’s theory included this point. Yi explains that adding this opinion correlation helped in significant ways, including more precisely describing the evolution process.

Not only did the theory and simulation correlate, Yi says that they came up with results that better matched real opinion networks characteristics, for example, the maintenance of significant ties with dissenting groups and bell-curve opinion distributions within communities that have significant bearing on opinion networking.

Yi adds that their new model is a more general framework for computing opinion formation and adaptive networks, and that this model’s computations also take into account the rewiring, consensus and mutation speeds, which significantly impact the opinion networks evolution.

Source: “Opinion diversity and community formation in adaptive networks,” by Y. Yu, G. Xiao, G. Li, W. P. Tay, and H. F. Teoh, Chaos: An Interdisciplinary Journal of Nonlinear Science (2017). The article can be accessed at https://doi.org/10.1063/1.4989668 .