Blessing or curse? A survey on the Impact of Generative AI on Fake News

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Authors: Alexander Loth, Martin Kappes, Marc-Oliver Pahl

Published: 2024-04-03 19:14:45+00:00

AI Summary

This survey paper comprehensively examines the research and practical applications of Generative AI in both creating and detecting fake news. It synthesizes current findings across various topic clusters, including enabling technologies, fake news creation, detection methods, and the emerging impact of deepfakes.

Abstract

Fake news significantly influence our society. They impact consumers, voters, and many other societal groups. While Fake News exist for a centuries, Generative AI brings fake news on a new level. It is now possible to automate the creation of masses of high-quality individually targeted Fake News. On the other end, Generative AI can also help detecting Fake News. Both fields are young but developing fast. This survey provides a comprehensive examination of the research and practical use of Generative AI for Fake News detection and creation in 2024. Following the Structured Literature Survey approach, the paper synthesizes current results in the following topic clusters 1) enabling technologies, 2) creation of Fake News, 3) case study social media as most relevant distribution channel, 4) detection of Fake News, and 5) deepfakes as upcoming technology. The article also identifies current challenges and open issues.

Key findings

The research landscape has evolved from focusing on foundational Generative AI models to exploring their applications in fake news creation and detection. Significant advancements have been made in deepfake technology and detection, highlighting an ongoing arms race. Ethical considerations and mitigation strategies are increasingly emphasized, highlighting the need for a multi-faceted approach to combating misinformation.

Approach

The paper employs a structured literature review approach, systematically analyzing and synthesizing existing research on Generative AI's role in fake news. This involves identifying key studies, extracting relevant data, and synthesizing the findings to identify trends, challenges, and gaps in the field.

Datasets

UNKNOWN

Model(s)

BERT, GPT, Transformers, GANs, VAEs, XLM-RoBERTa, exBAKE

Author countries

Germany, France

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