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

arXiv: https://arxiv.org/abs/2404.03021 · HTML: https://mlsanity.com/paper/2404.03021/

## 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 (most cited first, 20 of 20)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Sparks of Artificial General Intelligence: Early experiments with GPT-4](https://mlsanity.com/paper/2303.12712/) (2023)
- [Fake News Detection on Social Media: A Data Mining Perspective](https://mlsanity.com/paper/1708.01967/) (2017)
- [The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits](https://mlsanity.com/paper/2402.17764/) (2024)
- [FakeNewsNet: A Data Repository with News Content, Social Context and Spatialtemporal Information for Studying Fake News on Social Media](https://mlsanity.com/paper/1809.01286/) (2018)
- [A Comprehensive Survey of AI-Generated Content (AIGC): A History of Generative AI from GAN to ChatGPT](https://mlsanity.com/paper/2303.04226/) (2023)
- [Dissecting Contextual Word Embeddings: Architecture and Representation](https://mlsanity.com/paper/1808.08949/) (2018)
- [AMMUS : A Survey of Transformer-based Pretrained Models in Natural Language Processing](https://mlsanity.com/paper/2108.05542/) (2021)
- [Beyond News Contents: The Role of Social Context for Fake News Detection](https://mlsanity.com/paper/1712.07709/) (2017)
- [ChatGPT is not all you need. A State of the Art Review of large Generative AI models](https://mlsanity.com/paper/2301.04655/) (2023)
- [A Survey on Natural Language Processing for Fake News Detection](https://mlsanity.com/paper/1811.00770/) (2018)
- [GenAI Against Humanity: Nefarious Applications of Generative Artificial Intelligence and Large Language Models](https://mlsanity.com/paper/2310.00737/) (2023)
- [Network-based Fake News Detection: A Pattern-driven Approach](https://mlsanity.com/paper/1906.04210/) (2019)
- [Toward General Design Principles for Generative AI Applications](https://mlsanity.com/paper/2301.05578/) (2023)
- [Combating Disinformation in a Social Media Age](https://mlsanity.com/paper/2007.07388/) (2020)
- [Two Stage Transformer Model for COVID-19 Fake News Detection and Fact Checking](https://mlsanity.com/paper/2011.13253/) (2020)
- [The Age of Synthetic Realities: Challenges and Opportunities](https://mlsanity.com/paper/2306.11503/) (2023)
- [Topic-Preserving Synthetic News Generation: An Adversarial Deep Reinforcement Learning Approach](https://mlsanity.com/paper/2010.16324/) (2020)

## Built on by (most cited first, 12 of 12)

- [Network-informed Prompt Engineering against Organized Astroturf Campaigns under Extreme Class Imbalance](https://mlsanity.com/paper/2501.11849/) (2025)
- [Can VLMs Detect and Localize Fine-Grained AI-Edited Images?](https://mlsanity.com/paper/2505.15644/) (2025)
- [DRES: Fake news detection by dynamic representation and ensemble selection](https://mlsanity.com/paper/2509.16893/) (2025)
- [Enhancing Ground-to-Aerial Image Matching for Visual Misinformation Detection Using Semantic Segmentation](https://mlsanity.com/paper/2502.06288/) (2025)
- [The Psychology of Falsehood: A Human-Centric Survey of Misinformation Detection](https://mlsanity.com/paper/2509.15896/) (2025)
- [Can Humans Tell? A Dual-Axis Study of Human Perception of LLM-Generated News](https://mlsanity.com/paper/2604.03755/) (2026)
- [Toward Accountable AI-Generated Content on Social Platforms: Steganographic Attribution and Multimodal Harm Detection](https://mlsanity.com/paper/2604.10460/) (2026)
- [AI-Generated Content in Cross-Domain Applications: Research Trends, Challenges and Propositions](https://mlsanity.com/paper/2509.11151/) (2025)
- [BAID: A Benchmark for Bias Assessment of AI Detectors](https://mlsanity.com/paper/2512.11505/) (2025)
- [Generative AI and Digital Ecosystem Resilience: A Proactive Lifecycle-Based Survey](https://mlsanity.com/paper/2606.00136/) (2026)
- [Interrupting the Chain: Human Perception of AI-Generated Disinformation Through a Kill Chain Lens](https://mlsanity.com/paper/2608.21389/) (2026)
- [Harmful Content Generation in Text-to-Image Models: Capabilities and Moderation Limitations](https://mlsanity.com/paper/2610.06503/) (2026)

## 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.