2026-05-01T23:45:10Z https://datacatalogue.cessda.eu/oai-pmh/v0/oai

84bc723369c11790f94399d6ca99335db9e55d5a85dc1b3a86ea21d38ea51bb4 2025-06-17T03:16:15Z language:enopenaire_data

DDI study level documentation for study 10.7802/2467 Data & Code: Using Deepfakes for Experiments in the Social Sciences – A Pilot Study Data & Code: Using Deepfakes for Experiments in the Social Sciences – A Pilot Study Data & Code: Using Deepfakes for Experiments in the Social Sciences – A Pilot Study 10.7802/246710.7802/246710.7802/246710.7802/2467 Eberl, Andreas Eberl, Andreas Kühn, Juliane Kühn, Juliane Wolbring, Tobias Wolbring, Tobias [Financial support from the Emerging Talents Initiative (ETI) from the University of Erlangen-Nuremberg] GESIS Data Archive for the Social SciencesGESIS Datenarchiv für Sozialwissenschaften The advent of deepfakes – the manipulation of audio records, images and videos based on deep learning techniques – has important implications for science and society. Current studies focus primarily on the detection and dangers of deepfakes. In contrast, less attention is paid to the potential of this technology for substantive research – particularly as an approach for controlled experimental manipulations in the social sciences. In this paper, we aim to fill this research gap and argue that deepfakes can be a valuable tool for conducting social science experiments. To demonstrate some of the potentials and pitfalls of deepfakes, we conducted a pilot study on the effects of physical attractiveness on student evaluations of teachers. To this end, we created a deepfake video varying the physical attractiveness of the instructor as compared to the original video and asked students to rate the presentation and instructor. First, our results show that social scientists without special knowledge in computational science can successfully create a credible deepfake within reasonable time. Student ratings of the quality of the two videos were comparable and students did not detect the deepfake. Second, we use deepfakes to examine a substantive research question: whether there are differences in the ratings of a physically more and a physically less attractive instructor. Our suggestive evidence points towards a beauty penalty. Thus, our study supports the idea that deepfakes can be used to introduce systematic variations into experiments while offering a high degree of experimental control. Finally, we discuss the feasibility of deepfakes as an experimental manipulation and the ethical challenges of using deepfakes in experiments. This is the provision of the data and code. Keywords: deepfakes, face swap, deep learning, experiment, physical attractiveness, student evaluations of teachersThe advent of deepfakes – the manipulation of audio records, images and videos based on deep learning techniques – has important implications for science and society. Current studies focus primarily on the detection and dangers of deepfakes. In contrast, less attention is paid to the potential of this technology for substantive research – particularly as an approach for controlled experimental manipulations in the social sciences. In this paper, we aim to fill this research gap and argue that deepfakes can be a valuable tool for conducting social science experiments. To demonstrate some of the potentials and pitfalls of deepfakes, we conducted a pilot study on the effects of physical attractiveness on student evaluations of teachers. To this end, we created a deepfake video varying the physical attractiveness of the instructor as compared to the original video and asked students to rate the presentation and instructor. First, our results show that social scientists without special knowledge in computational science can successfully create a credible deepfake within reasonable time. Student ratings of the quality of the two videos were comparable and students did not detect the deepfake. Second, we use deepfakes to examine a substantive research question: whether there are differences in the ratings of a physically more and a physically less attractive instructor. Our suggestive evidence points towards a beauty penalty. Thus, our study supports the idea that deepfakes can be used to introduce systematic variations into experiments while offering a high degree of experimental control. Finally, we discuss the feasibility of deepfakes as an experimental manipulation and the ethical challenges of using deepfakes in experiments. This is the provision of the data and code. Keywords: deepfakes, face swap, deep learning, experiment, physical attractiveness, student evaluations of teachers The experiment was embedded in an online bachelor course at Friedrich-Alexander University Erlangen-Nuremberg with 39 students. After one instruction for all respondents, the students were randomly assigned into one of two groups (one treatment group and one control group). VollerhebungVollerhebung Web-based experimentExperiment.WebBasedWeb-basiertes ExperimentExperiment.WebBased Restricted Access - To get access to the research data, the original data depositor's consent is needed.Eingeschränkter Zugang - Für den Zugang zu den Forschungsdaten muss eine Einwilligung der Datengebenden eingeholt werden. http://dbkapps.gesis.org/dbkoai/oai.asp oai:dbk.gesis.org:SDN/10.7802_2467 2023-03-11 ddi:codebook:2_5