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  • Simulated Misinformation Susceptibility (SMISTS): Enhancing Misinformation Research with Large Language Model Simulations
Title: Simulated Misinformation Susceptibility (SMISTS): Enhancing Misinformation Research with Large Language Model Simulations
Award ID(s):
2242072
PAR ID:
10645572
Author(s) / Creator(s):
; ; ; ; ;
Publisher / Repository:
Association for Computational Linguistics
Date Published:
Page Range / eLocation ID:
2774 to 2788
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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    Recent years have seen a surge in research on why people fall for misinformation and what can be done about it. Drawing on a framework that conceptualizes truth judgments of true and false information as a signal-detection problem, the current article identifies three inaccurate assumptions in the public and scientific discourse about misinformation: (1) People are bad at discerning true from false information, (2) partisan bias is not a driving force in judgments of misinformation, and (3) gullibility to false information is the main factor underlying inaccurate beliefs. Counter to these assumptions, we argue that (1) people are quite good at discerning true from false information, (2) partisan bias in responses to true and false information is pervasive and strong, and (3) skepticism against belief-incongruent true information is much more pronounced than gullibility to belief-congruent false information. These conclusions have significant implications for person-centered misinformation interventions to tackle inaccurate beliefs. 
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