University of Twente Student Theses


Reducing the Impact of Deepfakes

Lübbeling, Niklas (2022) Reducing the Impact of Deepfakes.

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Abstract:Deepfakes are a form of machine learning and artificial intelligence enabling the visual and auditory manipulation of video material which is mostly used to transpose individuals faces on those of others. Deepfakes can cause reputational damage, distrust or hatred towards the people being deepfaked, or can be used for political propaganda leading to polarization among those believing in these videos. Therefore, this study tested an intervention including inoculation against deepfakes to reduce the spread of deepfakes. Students between 18-29 years were randomly allocated to one of two conditions receiving either neutral information about deepfakes or negative information about the consequences of deepfakes. It was expected that students in the Inoculation present condition showed lower sharing intentions and that this effect on intentions was mediated by students’ attitudes and their perceived coping abilities. However, none of these expected relationships were supported. Moreover, different dimensions investigating why people share videos were tested. Findings show that students’ sharing intentions could be predicted by their ratings of funniness of the individual deepfakes. Although this intervention was not able to reduce people’s sharing intentions, important implications for further studies investigating the potential of inoculation on reducing the impact of deepfakes were inferred.
Item Type:Essay (Master)
Faculty:BMS: Behavioural, Management and Social Sciences
Subject:77 psychology
Programme:Psychology MSc (66604)
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