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.2016 Jan 5;113(1):92-7.
doi: 10.1073/pnas.1509433112. Epub 2015 Nov 23.

Corporate funding and ideological polarization about climate change

Affiliations

Corporate funding and ideological polarization about climate change

Justin Farrell. Proc Natl Acad Sci U S A..

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.

Keywords: climate change; computational social science; funding; polarization; politics.

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Conflict of interest statement

The author declares no conflict of interest.

Figures

Fig. 1.
Fig. 1.
Which organizations produced discourse? Nodes in this network are organizations. The color of the node indicates whether they produced a text, and whether they received corporate funding. The shape of the node indicates the type of organization. Graphed using the Fruchterman–Reingold algorithm. The data are subsetted by decade based on the year an organization was founded, giving a sense of younger versus older organizations. Attention should be paid to the full model (year = 2013), which includes all nodes and ties, and is the network on which the significant measures were calculated.
Fig. 2.
Fig. 2.
Structural topic model results from 40,785 documents, including the topic label and the top 15 words associated with each. The topic proportions indicate the proportion of the corpus that belongs to each topic.
Fig. 3.
Fig. 3.
Graphing positive correlations between topics from Fig. 2. Topics near each other, and with a tie, indicate that they are more likely to be discussed within a document. Node sizes correspond to the topic proportions from Fig. 2 and are graphed using the Fruchterman–Reingold algorithm. Colored ellipses were added after graphing to point the reader toward the emergence of four distinct clusters.
Fig. 4.
Fig. 4.
The influence of corporate funding on themes within the climate change contrarian movement, 1993–2013. They axis indicates how much a topic was written about. The red line represents the prevalence of the topic in the texts of contrarian organizations who received money, and the black line represents the prevalence of the topic for contrarian organizations who did not receive money. Interaction plots of all other topics are provided inSI Appendix.
See this image and copyright information in PMC

References

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