Open Access Journal

ISSN: 2183-2463

Article | Open Access

Dynamics of Electoral Polarisation in Climate Policy Discourse: A Temporal Network Analysis

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Abstract:  Climate policy is a deeply polarised issue that intertwines ideological positions with social identities. This division is intensified by election campaigns, highlighting social identities and ideological conflicts. Previous research has shown an increase in polarisation during such campaigns and a decrease after an election. However, evidence suggests that campaigns do not significantly impact already highly polarised issues such as climate policy. Moreover, by focusing on polarisation between partisan groups, existing research often overlooks the multi-actor system of climate governance, in which diverse non-partisan actors are also central to shaping the discourse. To address these gaps, this study examines climate policy discourse on Twitter during the 2021 German federal election. It employs a temporal network analysis to compare polarisation between partisan and non-partisan groups. The findings show that the climate discourse is divided into a pro-climate camp, dominated by environmental activists, scientists, and journalists, and an anti-climate camp, dominated by right-wing bloggers and climate sceptics. This study reveals a dual dynamic in the climate policy discourse. Partisan polarisation intensified temporarily, reaching its peak during the election campaign before declining again, while the deep divide between pro- and anti-climate camps remained largely stable. The findings suggest that the polarisation during an election campaign for a highly polarised issue may differ from previous research findings. This underscores the importance of exploring non-partisan structures, as their polarisation dynamics can differ significantly from those observed within traditional partisan groups.

Keywords:  climate policy; election campaign; Germany; polarisation; public discourse; social media

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DOI: https://doi.org/10.17645/pag.10004



© Paul Drecker. This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 license (http://creativecommons.org/licenses/by/4.0), which permits any use, distribution, and reproduction of the work without further permission provided the original author(s) and source are credited.