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.
Authors
- Emily K. Vraga (ORCID: https://orcid.org/0000-0002-3016-3869)
- Rongwei Tang (ORCID: https://orcid.org/0009-0009-9913-9603)
- Leticia Bode (ORCID: https://orcid.org/0000-0002-9859-2918)
- Jikai Sun
- Yuming Fang
Institutions
- Augsburg University (US)
- University of Minnesota (US)
- Georgetown University (US)
- Twin Cities Orthopedics (US)
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
Funders
- University of Minnesota