Corporate funding and ideological polarization about climate change

TLDR

The study aims to show how corporate and political actors shape climate‑change polarization and proposes future research using large‑scale textual analysis and social‑network integration. Using a computational analysis of 164 organizations and 40,785 climate‑change texts from 1993‑2013, the authors model the dynamics of polarization in the United States. Corporate funding is linked to higher production of polarizing texts and to the thematic framing and prevalence of those texts over time, confirming long‑standing theories of climate‑change politics and broader ideological polarization.

Abstract

Drawing on large-scale computational data and methods, this research demonstrates how polarization efforts are influenced by a patterned network of political and financial actors. These dynamics, which have been notoriously difficult to quantify, are illustrated here with a computational analysis of climate change politics in the United States. The comprehensive data include all individual and organizational actors in the climate change countermovement (164 organizations), as well as all written and verbal texts produced by this network between 1993-2013 (40,785 texts, more than 39 million words). Two main findings emerge. First, that organizations with corporate funding were more likely to have written and disseminated texts meant to polarize the climate change issue. Second, and more importantly, that corporate funding influences the actual thematic content of these polarization efforts, and the discursive prevalence of that thematic content over time. These findings provide new, and comprehensive, confirmation of dynamics long thought to be at the root of climate change politics and discourse. Beyond the specifics of climate change, this paper has important implications for understanding ideological polarization more generally, and the increasing role of private funding in determining why certain polarizing themes are created and amplified. Lastly, the paper suggests that future studies build on the novel approach taken here that integrates large-scale textual analysis with social networks.