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

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arxiv 2404.03021 v2 pith:ISR2YOKY submitted 2024-04-03 cs.CL cs.AI

Alexander Loth , Martin Kappes , Marc-Oliver Pahl This is my paper
classification cs.CLcs.AI
keywords fakenewsgenerativecreationsurveycurrentdetectionfollowing
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Can Humans Tell? A Dual-Axis Study of Human Perception of LLM-Generated News

    cs.CY 2026-04 conditional novelty 6.0 of 10

    Humans cannot reliably distinguish LLM-generated news from human-written news across multiple models, with domain expertise providing only modest help and fatigue reducing accuracy over time.

  2. Toward Accountable AI-Generated Content on Social Platforms: Steganographic Attribution and Multimodal Harm Detection

    cs.CV 2026-04 unverdicted novelty 4.0 of 10

    The proposed steganography-based attribution system with CLIP multimodal fusion achieves robust watermarking under distortions and 0.99 AUC-ROC for harm detection, enabling traceable AI content accountability.

  3. Generative AI and Digital Ecosystem Resilience: A Proactive Lifecycle-Based Survey

    cs.LG 2026-05 unverdicted novelty 3.0 of 10

    This survey introduces the C5 Interaction Model as a unifying taxonomy to synthesize proactive detection methods for GenAI-enabled adversarial narratives across socio-technical and computational research streams.

  4. AI-Generated Content in Cross-Domain Applications: Research Trends, Challenges and Propositions

    cs.AI 2025-09 conditional novelty 2.0 of 10

    A cross-domain vision paper that surveys AI-generated content and proposes research directions, without introducing new empirical results.

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