mlsanityData to 14 Oct 2026

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

arXiv 2404.03021 ↗First posted 3 Apr 2024cs.CL cs.AIcited by 12

Not on the map. Why?

Abstract (arXiv)

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 facts. Cited by 12 papers in the map (data through 14 Oct 2026). Builds on 20 of its 71 references that are in the map. First posted on arXiv 3 Apr 2024.

Builds on

20 of 71 references.

References · 20

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  5. 2017Fake News Detection on Social Media: A Data Mining Perspectivecited by 336
  6. 2024The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bitscited by 308
  7. 2018FakeNewsNet: A Data Repository with News Content, Social Context and Spatialtemporal Information for Studying Fake News on Social Mediacited by 285
  8. 2023A Comprehensive Survey of AI-Generated Content (AIGC): A History of Generative AI from GAN to ChatGPTcited by 224
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  11. 2017Beyond News Contents: The Role of Social Context for Fake News Detectioncited by 91
  12. 2023ChatGPT is not all you need. A State of the Art Review of large Generative AI modelscited by 86
8 more
  1. 2018A Survey on Natural Language Processing for Fake News Detectioncited by 67
  2. 2023GenAI Against Humanity: Nefarious Applications of Generative Artificial Intelligence and Large Language Modelscited by 42
  3. 2019Network-based Fake News Detection: A Pattern-driven Approachcited by 33
  4. 2023Toward General Design Principles for Generative AI Applicationscited by 21
  5. 2020Combating Disinformation in a Social Media Agecited by 17
  6. 2020Two Stage Transformer Model for COVID-19 Fake News Detection and Fact Checkingcited by 15
  7. 2023The Age of Synthetic Realities: Challenges and Opportunitiescited by 15
  8. 2020Topic-Preserving Synthetic News Generation: An Adversarial Deep Reinforcement Learning Approachcited by 6

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 7; 2026: 5.

12 papers.

  1. 2025Network-informed Prompt Engineering against Organized Astroturf Campaigns under Extreme Class Imbalancecited by 4
  2. 2025Can VLMs Detect and Localize Fine-Grained AI-Edited Images?cited by 4
  3. 2025DRES: Fake news detection by dynamic representation and ensemble selectioncited by 3
  4. 2025Enhancing Ground-to-Aerial Image Matching for Visual Misinformation Detection Using Semantic Segmentationcited by 1
  5. 2025The Psychology of Falsehood: A Human-Centric Survey of Misinformation Detectioncited by 1
  6. 2026Can Humans Tell? A Dual-Axis Study of Human Perception of LLM-Generated Newscited by 1
  7. 2026Toward Accountable AI-Generated Content on Social Platforms: Steganographic Attribution and Multimodal Harm Detectioncited by 1
  8. 2025AI-Generated Content in Cross-Domain Applications: Research Trends, Challenges and Propositionscited by 0
  9. 2025BAID: A Benchmark for Bias Assessment of AI Detectorscited by 0
  10. 2026Generative AI and Digital Ecosystem Resilience: A Proactive Lifecycle-Based Surveycited by 0
  11. 2026Interrupting the Chain: Human Perception of AI-Generated Disinformation Through a Kill Chain Lenscited by 0
  12. 2026Harmful Content Generation in Text-to-Image Models: Capabilities and Moderation Limitationscited by 0