DETECTING OPINION-BASED GROUPS AND POLARIZATION IN SURVEY-BASED ATTITUDE NETWORKS AND ESTIMATING QUESTION RELEVANCE

Alejandro Dinkelberg, JP O’SULLIVAN David (), Michael Quayle and Pã Draig Maccarron
Additional contact information
Alejandro Dinkelberg: MACSI, Department of Mathematics and Statistics, University of Limerick, Limerick, V94 T9PX, Ireland2Centre for Social Issues Research, University of Limerick, Limerick, V94 T9PX, Ireland
JP O’SULLIVAN David: MACSI, Department of Mathematics and Statistics, University of Limerick, Limerick, V94 T9PX, Ireland
Michael Quayle: Centre for Social Issues Research, University of Limerick, Limerick, V94 T9PX, Ireland3Department of Psychology, School of Applied Human Sciences, University of KwaZulu-Natal, Pietermaritzburg, South Africa
Pã Draig Maccarron: MACSI, Department of Mathematics and Statistics, University of Limerick, Limerick, V94 T9PX, Ireland2Centre for Social Issues Research, University of Limerick, Limerick, V94 T9PX, Ireland

Advances in Complex Systems (ACS), 2021, vol. 24, issue 02, 1-37

Abstract: 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 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 for identifying polarization can classify non-overlapping opinion-based groups even in the absence of extreme opinions.

Keywords: Attitude networks; opinion-based groups; community detection; survey analysis; polarization; data mining (search for similar items in EconPapers)
Date: 2021
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.worldscientific.com/doi/abs/10.1142/S0219525921500065
Access to full text is restricted to subscribers

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:wsi:acsxxx:v:24:y:2021:i:02:n:s0219525921500065

Ordering information: This journal article can be ordered from

DOI: 10.1142/S0219525921500065

Access Statistics for this article

Advances in Complex Systems (ACS) is currently edited by Frank Schweitzer

More articles in Advances in Complex Systems (ACS) from World Scientific Publishing Co. Pte. Ltd.
Bibliographic data for series maintained by Tai Tone Lim ().