JOURNAL ARTICLE

Visual overload: The influence of broadcast social media visuals on televised debate viewing outcomes.

  • Published In: Journal of Visual Political Communication, 2023, v. 10, n. 2. P. 151 1 of 3

  • Database: Art Source Ultimate 2 of 3

  • Authored By: Jennings, Freddie J.; Bouchillon, Brandon; Bramlett, Josh C.; Eubanks, Austin D.; Stewart, Patrick A.; Miller, Jason M. 3 of 3

Abstract

The article examines the impact of incorporating on-screen Twitter content during the 2015 and 2016 CBS U.S. presidential primary debates on viewers’ cognitive processing, knowledge acquisition, persuasion, and political polarization. Using a mixed-method approach—including content analysis of tweets shown during the debates and an eye-tracking study of viewers watching the 2015 Democratic primary debate—the research finds that social media visuals distracted viewers from candidate messages, reducing their learning about policies and decreasing agreement with candidates’ positions. Additionally, exposure to Twitter feeds increased affective polarization between Democrats and Republicans, though it did not significantly affect differentiation among Democratic candidates. The study suggests that the inclusion of social media information during televised debates may overwhelm viewers’ cognitive capacity, thereby undermining democratic engagement and recommends simplifying debate visuals to enhance voter comprehension and attitude formation.

Additional Information

  • Source:Journal of Visual Political Communication. 2023/07, Vol. 10, Issue 2, p151
  • Document Type:Article
  • Subject Area:Communication and Mass Media
  • Publication Date:2023
  • ISSN:2633-3732
  • DOI:10.1386/jvpc_00029_1
  • Accession Number:175868578
  • Copyright Statement:Copyright of Journal of Visual Political Communication is the property of Intellect Ltd. 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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