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Emergence of polarized opinions from free association networks.

ABSTRACT: We developed a method that can identify polarized public opinions by finding modules in a network of statistically related free word associations. Associations to the cue "migrant" were collected from two independent and comprehensive samples in Hungary (N₁ = 505, N₂ = 505). The co-occurrence-based relations of the free word associations reflected emotional similarity, and the modules of the association network were validated with well-established measures. The positive pole of the associations was gathered around the concept of "Refugees" who need help, whereas the negative pole associated asylum seekers with "Violence." The results were relatively consistent in the two independent samples. We demonstrated that analyzing the modular organization of association networks can be a tool for identifying the most important dimensions of public opinion about a relevant social issue without using predefined constructs.

SUBMITTER: File B

PROVIDER: S-EPMC6420605 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature

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Emergence of polarized opinions from free association networks.

File Bálint B Keczer Zsolt Z Vancsó Anna A Bőthe Beáta B Tóth-Király István I Hunyadi Márton M Ujhelyi Adrienn A Ulbert István I Góth Júlia J Orosz Gábor G

Behavior research methods 20190201 1

We developed a method that can identify polarized public opinions by finding modules in a network of statistically related free word associations. Associations to the cue "migrant" were collected from two independent and comprehensive samples in Hungary (N<sub>1</sub> = 505, N<sub>2</sub> = 505). The co-occurrence-based relations of the free word associations reflected emotional similarity, and the modules of the association network were validated with well-established measures. The positive pol ...[more]

PMID: 30094726

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Project description:Although the structural properties of online social networks have attracted much attention, the properties of the close-knit friendship structures remain an important question. Here, we mainly focus on how these mesoscale structures are affected by the local and global structural properties. Analyzing the data of four large-scale online social networks reveals several common structural properties. It is found that not only the local structures given by the indegree, outdegree, and reciprocal degree distributions follow a similar scaling behavior, the mesoscale structures represented by the distributions of close-knit friendship structures also exhibit a similar scaling law. The degree correlation is very weak over a wide range of the degrees. We propose a simple directed network model that captures the observed properties. The model incorporates two mechanisms: reciprocation and preferential attachment. Through rate equation analysis of our model, the local-scale and mesoscale structural properties are derived. In the local-scale, the same scaling behavior of indegree and outdegree distributions stems from indegree and outdegree of nodes both growing as the same function of the introduction time, and the reciprocal degree distribution also shows the same power-law due to the linear relationship between the reciprocal degree and in/outdegree of nodes. In the mesoscale, the distributions of four closed triples representing close-knit friendship structures are found to exhibit identical power-laws, a behavior attributed to the negligible degree correlations. Intriguingly, all the power-law exponents of the distributions in the local-scale and mesoscale depend only on one global parameter, the mean in/outdegree, while both the mean in/outdegree and the reciprocity together determine the ratio of the reciprocal degree of a node to its in/outdegree. Structural properties of numerical simulated networks are analyzed and compared with each of the four real networks. This work helps understand the interplay between structures on different scales in online social networks.

Dataset Information

Emergence of polarized opinions from free association networks.

Publications

Emergence of polarized opinions from free association networks.

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