How deepfake quality, media literacy, and personal attitudes shape detection, liking, and social media sharing of political deepfakes

The increasing realism and accessibility of political deepfakes pose serious risks to democratic discourse by blurring the line between authentic and manipulated media. The present study examined how deepfake quality, media literacy, and attitudes toward the person depicted in the video shape individuals’ ability to detect political deepfakes, their emotional responses (liking), and behavioral intentions (social media sharing). A total of 1,124 participants from the United Kingdom, Slovenia, and Italy viewed manipulated videos about climate change and immigration that varied in quality. The results showed that high-quality deepfakes were less likely to be detected and received more positive evaluations than low-quality ones. In contrast, quality did not significantly influence sharing intention. Media literacy and attitudes toward the person in the video emerged as strong predictors across outcomes; higher media literacy improved detection and reduced liking and sharing, while more positive attitudes decreased detection and increased both liking and sharing. Mediation analyses demonstrated that liking partially mediated the link between detection and sharing intention. Furthermore, moderated mediation models revealed that this indirect effect was stronger among individuals with high media literacy (for climate change videos) and favorable attitudes toward the video’s subject (for both topics). Overall, the study highlights the need for interventions that address not only detection skills but also emotional and motivational susceptibility to persuasive synthetic media. Enhancing media literacy and implementing platform-level friction mechanisms may help curb the spread of political deepfakes online.