Automating Accuracy: Scalable Approaches to Correcting Disinformation With Artificial Intelligence on Social Media

This study investigates whether source expertise (expert vs. non-expert), use of artificial intelligence (AI; AI vs. non-AI), and the placement (debunking vs. prebunking) of a correction influence its effectiveness in reducing misperceptions and intentions to consume raw milk. Results of a pre-registered two-wave online experiment ( N 1 = 1,785, N 2 = 1,568) suggest that debunking consistently reduces misperceptions and behavioral intentions for at least 1 week, while prebunking was less effective. Expert corrections only outperform non-expert corrections in reducing misperceptions in wave 1. In general, AI cues do not significantly influence the effectiveness of a correction, offering both opportunities and challenges for organizations hoping to automate corrections.

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Publication Details

Journal
Journalism & Mass Communication Quarterly
Published
2025-09-03
DOI
https://doi.org/10.1177/10776990251359660
Citations
4
Primary Topic
Misinformation and Its Impacts
Type
article
Field-Weighted Citation Impact
16.81

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article

Automating Accuracy: Scalable Approaches to Correcting Disinformation With Artificial Intelligence on Social Media

Emily K. Vraga, Rongwei Tang, Leticia Bode, Jikai Sun et al.
4 citations
Journalism & Mass Communication Quarterly
Misinformation and Its Impacts
16.81
article

Automating Accuracy: Scalable Approaches to Correcting Disinformation With Artificial Intelligence on Social Media

Emily K. Vraga, Rongwei Tang, Leticia Bode, Jikai Sun, Yuming Fang
article en
4 citations

Abstract

This study investigates whether source expertise (expert vs. non-expert), use of artificial intelligence (AI; AI vs. non-AI), and the placement (debunking vs. prebunking) of a correction influence its effectiveness in reducing misperceptions and intentions to consume raw milk. Results of a pre-registered two-wave online experiment ( N 1 = 1,785, N 2 = 1,568) suggest that debunking consistently reduces misperceptions and behavioral intentions for at least 1 week, while prebunking was less effective. Expert corrections only outperform non-expert corrections in reducing misperceptions in wave 1. In general, AI cues do not significantly influence the effectiveness of a correction, offering both opportunities and challenges for organizations hoping to automate corrections.

Journalism & Mass Communication QuarterlyVol. 102(4)
Augsburg University (US), University of Minnesota (US), Georgetown University (US), Twin Cities Orthopedics (US)
University of Minnesota
Openalex Percentile: Top 1%
Misinformation and Its Impacts
16.81
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