Believe it or not: A network analysis investigating how individuals embrace false and true statements during COVID-19

Journal article

Sanguk Lee, Hyesun Choung, Tai-Quan Peng, M. Lapinski, Youjin Jang, M. Turner
Communication monographs, 2024

Cite

APA Click to copy
Lee, S., Choung, H., Peng, T.-Q., Lapinski, M., Jang, Y., & Turner, M. (2024). Believe it or not: A network analysis investigating how individuals embrace false and true statements during COVID-19. Communication Monographs.

Chicago/Turabian Click to copy
Lee, Sanguk, Hyesun Choung, Tai-Quan Peng, M. Lapinski, Youjin Jang, and M. Turner. “Believe It or Not: A Network Analysis Investigating How Individuals Embrace False and True Statements during COVID-19.” Communication monographs (2024).

MLA Click to copy
Lee, Sanguk, et al. “Believe It or Not: A Network Analysis Investigating How Individuals Embrace False and True Statements during COVID-19.” Communication Monographs, 2024.

BibTeX Click to copy

@article{sanguk2024a,
  title = {Believe it or not: A network analysis investigating how individuals embrace false and true statements during COVID-19},
  year = {2024},
  journal = {Communication monographs},
  author = {Lee, Sanguk and Choung, Hyesun and Peng, Tai-Quan and Lapinski, M. and Jang, Youjin and Turner, M.}
}

Abstract

ABSTRACT As individuals make belief decisions on truths and falsehoods, a systematic organization of (mis)information emerges. In this study, we employ a network approach to illustrate how a sample of Americans share a cognitive network of false and true statements related to COVID-19. Moreover, we examine what factors are associated with the formation of misbeliefs. Findings from our US-based rolling cross-sectional survey data indicate that conservative groups exhibit a greater tendency to mix up false and true information than liberal groups. This tendency is preserved across different time points, revealing more homogenously structured information networks of conservative groups compared to liberal groups. The benefits of a cognitive network framework that integrates structural perspectives into theories are discussed.