Generative Models and Deepfake Technology: A Qualitative Research on the Intersection of Social Media and Political Manipulation

Abstract:

Generative technologies, notably deepfakes, have gained prominence for their ability to create synthetic content that closely resembles reality. However, there is a noticeable gap in academic literature regarding analysing these technologies' risks and specific applications, particularly in the context of social media and political discourse. This motivates conducting this research to address these gaps and provide comprehensive insights into the challenges and opportunities posed by deepfake technology in contemporary society. The research methodology involved a systematic literature search across various online databases, including Web of Science Core Collection, Scopus, IEEE Explore, and ScienceDirect. Publications were selected based on relevance to the study topic, publication date (2018-2023), and document type criteria. The findings highlight the alarming potential of deepfake technology to manipulate public perception, exacerbate political polarization, and undermine democratic processes. Deepfakes emerge as a potent form of disinformation dissemination, often politically manipulated to create false content and erode trust in political institutions. In conclusion, this paper underscores the urgent need for regulatory, ethical, and technological strategies to address the challenges posed by deepfake technology in the digital age. Investment in technological solutions for content detection, digital literacy enhancement, and regulatory measures is imperative to mitigate the negative impact of generative technologies on political affairs.