Social Media Spillover: Attitude-Inconsistent Tweets Reduce Memory for Subsequent Information

Reese Butterfuss, Tracy Arner, Danielle S. McNamara, Laura K. Allen

Research output: Contribution to conferencePaperpeer-review

Abstract

Social media users are generally exposed to information that is predominantly consistent with their attitudes and beliefs (i.e., filter bubbles), which can increase polarization and decrease understanding of complex and controversial topics. One potential approach to mitigating the negative consequences of filter bubbles is intentional exposure to information that is inconsistent with attitudes. However, it is unclear how exposure to attitude-inconsistent information in social media contexts influences memory for controversial information. To fill this gap, this study examines the effects of presenting participants (n = 96) with Twitter content on a controversial topic (i.e., labor unions) that was either pro-union, anti-union, or neutral. Participants then read a media article including both pro-union and anti-union information. Participants who saw Twitter content that was inconsistent with their prior attitudes regarding labor unions recalled less of the article content compared to those who saw Twitter content that was consistent with their prior attitudes. The findings suggest that Twitter users’ memory for information related to controversial topics may not benefit from exposure to messages outside their filter bubble.

Original languageEnglish (US)
Pages2430-2436
Number of pages7
StatePublished - 2021
Externally publishedYes
Event43rd Annual Meeting of the Cognitive Science Society: Comparative Cognition: Animal Minds, CogSci 2021 - Virtual, Online, Austria
Duration: Jul 26 2021Jul 29 2021

Conference

Conference43rd Annual Meeting of the Cognitive Science Society: Comparative Cognition: Animal Minds, CogSci 2021
Country/TerritoryAustria
CityVirtual, Online
Period7/26/217/29/21

Keywords

  • comprehension
  • filter bubble
  • memory
  • Social media

ASJC Scopus subject areas

  • Cognitive Neuroscience
  • Artificial Intelligence
  • Computer Science Applications
  • Human-Computer Interaction

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