Many Ways to Be Fake: Benchmarking Fake News Detection Under Strategy-Driven AI Generation
Recent advances in large language models (LLMs) have enabled the large-scale generation of highly fluent and deceptive news-like content. While prior work has often treated fake news detection as a binary classification problem, modern fake news increasingly arises through human-AI collaboration, where strategic inaccuracies are embedded within otherwise accurate and credible narratives. These mixed-truth cases represent a realistic and consequential threat, yet they remain underrepresented in existing benchmarks. To address this gap, we introduce MANYFAKE, a synthetic benchmark containing 6,798 fake news articles generated through multiple strategy-driven prompting pipelines that capture...
| Authors | Xinyu Wang, Sai Koneru, Wenbo Zhang et al. |
|---|---|
| Primary category | cs.CL |
| Published | 10 April 2026 |
| Last updated at source | 10 April 2026 |
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