Data from the UC1 Experiments on the perception of deepfake videos and their effects on attitude

The data generated during UC1experiments that examined whether deepfakes can influence attitudes toward climate change and immigration, two highly polarized and politically relevant issues, and whether these effects depend on video quality, perceived trustworthiness (as measured with our previously developed Perceived Deepfake Trustworthiness Questionnaire). The dataset contributes to a more comprehensive understanding of how artifically generated content affects cognition, emotion, behavioral intentions, and attitudes. It also addresses both the potential risks and possible constructive uses of deepfakes, highlighthing the conditions under which the influence of artifically generated content is most pronounced, and identify individual differences that shape people’s vulnerability or resilience to manipulated media. This knowledge is critical for developing evidence-based approaches to mitigate the risks associated with malicious deepfakes while exploring their potential for positive applications. Relevant References: Plohl, N., Mlakar, I., Aquilino, L., Bisconti, P., & Smrke, U. (2024). Development and Validation of the perceived deepfake trustworthiness questionnaire (PDTQ) in three languages. International Journal of Human–Computer Interaction, 41(11), 6786 –6803. https://doi.org/10.1080/10447318.2024.2384821 Plohl, N., Mlakar, I., Aquilino, L., Brienza, M., Bisconti, P., & Smrke, U. (2025a). The moderating role of perceived trustworthiness in explaining the attitudinal effects of political deepfakes. Social Science Research Network. https://doi.org/10.2139/ssrn.5351533 Plohl, N., Mlakar, I., Aquilino, L., Brienza, M., Bisconti, P., & Smrke, U. (2025b). How deepfake quality, media literacy, and personal attitudes shape detection, liking, and social media sharing of political deepfakes. PsyArXiv. https://doi.org/10.31234/osf.io/knvby_v1