用大模型分析印度牛尿治病言论的修辞套路
Dharma, Data and Deception: An LLM-Powered Rhetorical Analysis of Cow-Urine Health Claims on YouTube
- 用大模型识别100条视频中的14类说服策略
- 发现推广者多用疗效宣称和从众心理
- 适合研究网络谣言与文化议题的学者
健康伪信息仍是社交媒体最严峻的挑战之一，尤其当文化传统与科学化表述交织时。为此，我们分析了100条宣传或驳斥牛尿（gomutra）作为健康疗法的YouTube视频，聚焦权威求助、疗效宣称和阴谋论框架等修辞策略。采用GPT-4、GPT-4o、GPT-4.1、GPT-5、Gemini 2.5 Pro和Mistral Medium 3等大语言模型，基于14类说服策略的分类体系对文本进行标注。结果显示，推广者主要依赖疗效宣称与社会认同，而驳斥者则更强调权威反驳。对部分标注结果的人工评估显示90.1%的标注者一致性，验证了分类体系的可靠性。本研究推动了计算方法在虚假信息分析中的应用，展示了大模型在大规模在线文化话语研究中的潜力。
原文摘要 · Abstract (English)
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.
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