Immunizing the Public Against AI-Generated Disinformation

Testing the Effects of Inoculation Mode and Issue Attitude on Inoculation Likelihood of Political Deepfakes

Bibliographic Data

ID21643719
AuthorsBingbing Zhang (0000-0002-8356-456X, University of Iowa, corresponding author), Sang Jung Kim (0000-0001-5861-728X, University of Iowa), Sang Jeong Kim (0000-0001-8931-3713, University of Iowa), Alex Scott (0000-0003-0366-8404, University of Iowa)
Year2025
Volume102
Issue4
Pages1102-1134
Publication date2025-12-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueJournalism & Mass Communication Quarterly (JOURNAL)
Journal identifiersISSN: 1077-6990 • E-ISSN: 2161-430X
PublisherSAGE Publications (PUBLISHER • US)
DOI10.1177/10776990251357949
OpenAlexW4416841962
LanguageEN
Citations received1
References cited55

Political deepfakes are considered detrimental to democracy by eroding public trust and distorting communication. Scholars have advocated for inoculation strategies to counter deepfakes, yet they have found that individuals’ partisan attitudes can undermine the effects of inoculation. Guided by inoculation theory and motivated reasoning theory, we conducted a 3 (Inoculation Mode: Passive vs. Active vs. No Inoculation) × 2 (Deepfake Attack: Pro-Attitudinal vs. Counter-Attitudinal) between-subjects experiment. Results show that inoculation increases deepfake awareness, intention to debunk deepfakes, and information-seeking behaviors, while reducing the perceived credibility of deepfake messages. However, exposure to counter-attitudinal deepfakes led to greater agreement with embedded disinformation

Credibility · Democracy · Inoculation · Politics · Public opinion · Misinformation and Its Impacts · Social Media and Politics · Vaccine Coverage and Hesitancy

Tools

Open DOI