JOURNAL ARTICLE

The role of cable news hosts in public support for Supreme Court decisions.

  • Published In: Journal of Empirical Legal Studies, 2023, v. 20, n. 4. P. 1045 1 of 3

  • Database: Sociology Source Ultimate 2 of 3

  • Authored By: Boddery, Scott Simon; Cann, Damon; Moyer, Laura; Yates, Jeff 3 of 3

Abstract

In the current media environment, Americans increasingly tune into cable news programs with distinct ideological brands. This paper extends existing work on media source cues to coverage of the US Supreme Court, an institution which depends entirely on media outlets to communicate its rulings to the American public. We argue that the source cues associated with celebrity media personalities serve as a heuristic that helps individuals form their opinions about public policy. Using a nationwide survey experiment with over 2000 respondents, we find that commentary on Supreme Court decisions from cable news hosts affects public agreement with the Court's rulings, with key differences between how liberal and conservative respondents respond under certain conditions. While unexpected positions espoused by in‐group messengers shift the views of liberals and conservatives alike, signals from out‐group messengers yield more of an effect for conservatives than for liberals. Our results show that counter‐stereotypical (unexpected) position taking has a powerful impact on public perceptions of policy outcomes and suggest that well‐known media figures may have an important role in mitigating ideological polarization in America. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Journal of Empirical Legal Studies. 2023/12, Vol. 20, Issue 4, p1045
  • Document Type:Article
  • Subject Area:Law
  • Publication Date:2023
  • ISSN:1740-1453
  • DOI:10.1111/jels.12367
  • Accession Number:173720289
  • Copyright Statement:Copyright of Journal of Empirical Legal Studies 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.)

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