Many Ways to Be Fake: Benchmarking Fake News Detection Under Strategy-Driven AI Generation
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866910119698104320 |
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| author | Wang, Xinyu Koneru, Sai Zhang, Wenbo Zheng, Wenliang Ranjan, Saksham Rajtmajer, Sarah |
| author_facet | Wang, Xinyu Koneru, Sai Zhang, Wenbo Zheng, Wenliang Ranjan, Saksham Rajtmajer, Sarah |
| contents | 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 many ways fake news can be constructed and refined. Using this benchmark, we evaluate a range of state-of-the-art fake news detectors. Our results show that even advanced reasoning-enabled models approach saturation on fully fabricated stories, but remain brittle when falsehoods are subtle, optimized, and interwoven with accurate information. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_09514 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Many Ways to Be Fake: Benchmarking Fake News Detection Under Strategy-Driven AI Generation Wang, Xinyu Koneru, Sai Zhang, Wenbo Zheng, Wenliang Ranjan, Saksham Rajtmajer, Sarah Computation and Language Human-Computer Interaction 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 many ways fake news can be constructed and refined. Using this benchmark, we evaluate a range of state-of-the-art fake news detectors. Our results show that even advanced reasoning-enabled models approach saturation on fully fabricated stories, but remain brittle when falsehoods are subtle, optimized, and interwoven with accurate information. |
| title | Many Ways to Be Fake: Benchmarking Fake News Detection Under Strategy-Driven AI Generation |
| topic | Computation and Language Human-Computer Interaction |
| url | https://arxiv.org/abs/2604.09514 |