Health misinformation remains one of the most pressing challenges on social media, particularly when cultural traditions intersect with scientific-sounding claims. These dynamics are not only global but also deeply local, manifesting in culturally specific controversies that require careful analysis. Motivated by this, we examine 100 YouTube transcripts that promote or debunk cow urine (gomutra) as a health remedy, focusing on rhetorical strategies such as appeals to authority, efficacy appeals, and conspiracy framing. We employ large language models (LLMs) including GPT-4, GPT-4o, GPT-4.1, GPT-5, Gemini 2.5 Pro, and Mistral Medium 3 to annotate transcripts using a 14-category taxonomy of persuasive tactics. Our analysis reveals that promoters predominantly rely on efficacy appeals and social proof, while debunkers emphasize authority and rebuttal. Human evaluation of a subset of annotations yielded 90.1\% inter-annotator agreement, confirming the reliability of our taxonomy and validation process. This work advances computational methods for misinformation analysis and demonstrates how LLMs can support large-scale studies of cultural discourse online.

翻译：摘要：健康误导信息仍是社交媒体面临的最严峻挑战之一，尤其是在文化传统与看似科学的论述相互交织时。这些动态不仅具有全球性，更深深植根于本土语境，常表现为需要细致分析的文化特异性争议。基于此，本研究对100份YouTube视频转录文本进行了分析，这些文本或鼓吹或驳斥牛尿（gomutra）作为健康疗法的功效，重点关注权威诉求、功效诉求及阴谋论框架等修辞策略。我们采用包含GPT-4、GPT-4o、GPT-4.1、GPT-5、Gemini 2.5 Pro及Mistral Medium 3在内的大型语言模型，依据14类说服策略分类体系对转录文本进行标注。分析显示，鼓吹者主要依赖功效诉求与社会认同，而驳斥者则更强调权威性与反驳论证。对部分标注结果的人工评估显示，注释者间一致性达90.1%，验证了本分类体系与验证流程的可靠性。本研究推动了误导信息分析的计算方法发展，并展示了大型语言模型如何支持大规模在线文化话语研究。