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Networks, representing attitudinal survey data, expose the structure ofopinion-based groups. We make use of these network projections to identify thegroups reliably through community detection algorithms and to examinesocial-identity-based groups. Our goal is to present a method for revealingpolarisation and opinion-based in attitudinal surveys. This method can bebroken down into the following steps: data preparation, construction ofsimilarity-based networks, algorithmic identification of opinion-based groups,and identification of important items for community structure. We assess themethod's performance and possible scope for applying it to empirical data andto a broad range of synthetic data sets. The empirical data application pointsout possible conclusions (i.e. social-identity polarisation), whereas thesynthetic data sets mark out the method's boundaries. Next to an applicationexample on political attitude survey, our results suggest that the method worksfor various surveys but is also moderated by the efficacy of the communitydetection algorithms. Concerning the identification of opinion-based groups, weprovide a solid method to rank the item's influence on group formation and as agroup identifier. We discuss how this network approach for identifyingpolarisation can classify non-overlapping opinion-based groups even in theabsence of extreme opinions.

Detect opinion-based groups and reveal polarisation in survey data