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The straw man effect: partisan misrepresentation in natural language
File(s)
13684302211014582.pdf (660.09 KB)
Published version
Author(s)
Yeomans, Michael
Type
Journal Article
Abstract
Political discourse often seems divided not just by different preferences, but by entirely different representations of the debate. Are partisans able to accurately describe their opponents’ position, or do they instead generate unrepresentative “straw man” arguments? In this research we examined an (incentivized) political imitation game, by asking partisans on both sides of the US health care debate to describe the most common arguments for and against ObamaCare. We used natural language processing algorithms to benchmark the biases and blind spots of our participants. Overall, partisans showed a limited ability to simulate their opponents’ perspective, or to distinguish genuine from imitation arguments. In general, imitations were less extreme than their genuine counterparts. Individual difference analyses suggest that political sophistication only improves the representations of one's own side, but not of an opponents' side, exacerbating the straw man effect. Our findings suggest that false beliefs about partisan opponents may be pervasive.
Date Issued
2022-10-01
Date Acceptance
2021-04-13
Citation
Group Processes and Intergroup Relations, 2022, 25 (7), pp.1905-1924
URI
DOI
ISSN
1368-4302
Publisher
SAGE Publications
Start Page
1905
End Page
1924
Journal / Book Title
Group Processes and Intergroup Relations
Volume
25
Issue
7
Copyright Statement
© The Author(s) 2021. This article is distributed under the terms of the Creative Commons Attribution 4.0 License (https://creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage).
License URL
Subjects
Social Psychology
1503 Business and Management
1701 Psychology
1702 Cognitive Sciences
Publication Status
Published
Date Publish Online
2021-07-20