Dharma, Data and Deception: An LLM-Powered Rhetorical Analysis of Cow-Urine Health Claims on YouTube
Sheza Munir, Ratna Kandala, Anamta Khan, Deepti, Joyojeet Pal

TL;DR
This study uses large language models to analyze the rhetorical strategies in YouTube videos promoting or debunking cow urine as a health remedy, revealing distinct persuasive tactics.
Contribution
It introduces a taxonomy-based annotation method using LLMs to analyze health misinformation and cultural discourse at scale.
Findings
Promoters rely on efficacy appeals and social proof.
Debunkers emphasize authority and rebuttal strategies.
Human evaluation shows 90.1% agreement, validating the approach.
Abstract
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…
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