Networks, representing attitudinal survey data, expose the structure of opinion-based groups.
We make use of these network projections to identify the groups reliably through community
detection algorithms and to examine social-identity-based groups. Our goal is to present a
method for revealing polarization and opinion-based groups in attitudinal surveys. This method
can be broken down into the following steps: data preparation, construction of similarity-based
networks, algorithmic identification of opinion-based groups, and identification of important
items for community structure. We assess the method's performance and possible scope for
applying it to empirical data and to a broad range of synthetic data sets. The empirical data
application points out possible conclusions (i.e. social-identity polarization), whereas the synthetic data sets mark out the method's boundaries. Next to an application example on political
attitude survey, our results suggest that the method works for various surveys but is also mod erated by the efficacy of the community detection algorithms. Concerning the identification of
opinion-based groups, we provide a solid method to rank the item's influence on group formation
and as a group identifier. We discuss how this network approach for identifying polarization can
classify non-overlapping opinion-based groups even in the absence of extreme opinions.
Funding
Study on Aerodynamic Characteristics Control of Slender Body Using Active Flow Control Technique