Who Do We Trust to Correct Health Misinformation? Credibility of AI-Generated Fact-Checking Messages.

Authors

Won-Ki Moon, Hayoung Sally Lim

Published in

Health communication. Pages 1-13. Oct 20, 2026. Epub Oct 20, 2026.

Abstract

As artificial intelligence (AI)-generated messages are increasingly used to correct health misinformation, it remains unclear when such messages are perceived as credible and how credibility shapes subsequent information behaviors. This study examines how source labeling (AI vs. human scientists) influences the perceived credibility of health-related fact-checking messages and whether these effects depend on perceived AI objectivity and trust in science. Using a 2 × 2 between-subjects experiment with U.S. adults (N = 231), we assessed credibility perceptions and three information responses: accuracy-oriented information seeking, fact-checking message sharing, and issue-relevant information avoidance. Source labeling alone did not affect perceived credibility. Instead, credibility judgments were conditional. Perceived credibility was positively associated with message sharing and negatively associated with information avoidance but was not robustly related to accuracy-oriented information seeking. Independent of credibility, AI-labeled fact-checking messages elicited greater intentions to seek additional information. These findings highlight the conditional nature of AI credibility in health communication and underscore the importance of aligning AI-based fact-checking with audience trust orientations.

PMID:
42902507
Bibliographic data and abstract were imported from PubMed on 21 Oct 2026.

Read full publication at:
Please sign in to see all details.

Stats

1-terrible, 9-excellent. How would you rate this publication? Sign in in to submit your rating.

Post a comment

You need to be signed in to post comments. You can sign in here.

Comments

There are no comments yet.