Detect opinion-based groups and reveal polarisation in survey data

Network visualisation, drawn from attitudinal survey data, exposes 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 polarisation.Our goal is to present a method for revealing polarisation in attitudinal surveys. This method can be broken down into the following steps: data preparation, construction of similarity-based net-works, algorithmic identification of opinion-based groups, and identification of item importance for community structure. We examine the method's performance and possible scope through 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 marks 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 moderated 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 to identifying polarization can classify non-overlapping opinion-based groups even in the absence of extreme opinions.