Threats, Emotions, and Affective Polarization.

  • Published In: Political Psychology, 2023, v. 44, n. 6. P. 1337 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Renström, Emma A.; Bäck, Hanna; Carroll, Royce 3 of 3

Abstract

Why do some individuals feel hostility and express bias against supporters of other political parties? Drawing on intergroup threat theory, we examine the role of emotions as a mechanism by which perceived threats against the ingroup are a source of increased affective polarization. In two survey experiments performed in the multiparty contexts of Sweden (N = 505) and Germany (N = 776), we manipulated intergroup threat using simulated online media, presenting participants with content related to immigration, and measured affective polarization using ratings of ingroup and outgroup supporter traits and feeling thermometers. Compared to a control condition, the threatening content evoked fear, anxiety, and anger among participants. However, only when individuals reacted to the threatening content with anger was increased affective polarization observed, in line with research showing that anger is a high‐arousal emotion related to an increased reliance on stereotypes. We conclude that individuals distance themselves from supporters of opposing political parties when they perceive a threat to their ingroup and subsequently react with anger. The findings contribute to the literature on affective polarization by stressing the role of emotional reactions to intergroup threat. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Political Psychology. 2023/12, Vol. 44, Issue 6, p1337
  • Document Type:Article
  • Subject Area:Economics
  • Publication Date:2023
  • ISSN:0162-895X
  • DOI:10.1111/pops.12899
  • Accession Number:173586357
  • Copyright Statement:Copyright of Political Psychology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Looking to go deeper into this topic? Look for more articles on EBSCOhost.