Logical fallacies are common in public communication and can mislead audiences; fallacious arguments may still appear convincing despite lacking soundness, because convincingness is inherently subjective. We present the first computational study of how emotional framing interacts with fallacies and convincingness, using large language models (LLMs) to systematically change emotional appeals in fallacious arguments. We benchmark eight LLMs on injecting emotional appeal into fallacious arguments while preserving their logical structures, then use the best models to generate stimuli for a human study. Our results show that LLM-driven emotional framing reduces human fallacy detection in F1 by 14.5% on average. Humans perform better in fallacy detection when perceiving enjoyment than fear or sadness, and these three emotions also correlate with significantly higher convincingness compared to neutral or other emotion states. Our work has implications for AI-driven emotional manipulation in the context of fallacious argumentation.

翻译：逻辑谬误在公共传播中普遍存在，可能误导受众；缺乏合理性的谬误论点仍可能显得令人信服，因为可信度本质上具有主观性。我们首次开展了关于情感框架如何与谬误及可信度相互作用的计算研究，利用大型语言模型系统性地改变谬误论点中的情感诉求。我们评估了八个大型语言模型在保持谬误论点逻辑结构的同时注入情感诉求的能力，随后使用最佳模型生成用于人类研究的刺激材料。结果显示，大型语言模型驱动的情感框架使人类谬误检测的F1值平均下降14.5%。当人类感知到愉悦情绪时，其谬误检测表现优于恐惧或悲伤情绪；相较于中性或其他情绪状态，这三种情绪还与显著更高的可信度相关。我们的研究揭示了人工智能在谬误论证语境中进行情感操控的潜在影响。