Many Ways to Be Fake: Benchmarking Fake News Detection Under Strategy-Driven AI Generation

• URL: http://arxiv.org/abs/2604.09514v1
• Date: Fri, 10 Apr 2026 17:36:00 GMT
• Title: Many Ways to Be Fake: Benchmarking Fake News Detection Under Strategy-Driven AI Generation
• Abstract summary: Recent advances in large language models have enabled the large-scale generation of highly fluent and deceptive news-like content.<n>These mixed-truth cases represent a realistic and consequential threat, yet they remain underrepresented in existing benchmarks.<n>We introduce MANYFAKE, a synthetic benchmark containing 6,798 fake news articles generated through multiple strategy-driven prompting pipelines.<n>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.
• Score: 6.008764313661662
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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