TAIGR: Towards Modeling Influencer Content on Social Media via Structured, Pragmatic Inference

• URL: http://arxiv.org/abs/2601.20032v1
• Date: Tue, 27 Jan 2026 20:12:57 GMT
• Title: TAIGR: Towards Modeling Influencer Content on Social Media via Structured, Pragmatic Inference
• Authors: Nishanth Sridhar Nakshatri, Eylon Caplan, Rajkumar Pujari, Dan Goldwasser,
• Abstract summary: Claim-centric verification methods struggle to capture the pragmatic meaning of influencer discourse.<n>We propose a structured framework designed to analyze influencer discourse, which operates in three stages.<n>We show that accurate validation requires modeling the discourse's pragmatic and argumentative structure.
• Score: 19.35061674485291
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Health influencers play a growing role in shaping public beliefs, yet their content is often conveyed through conversational narratives and rhetorical strategies rather than explicit factual claims. As a result, claim-centric verification methods struggle to capture the pragmatic meaning of influencer discourse. In this paper, we propose TAIGR (Takeaway Argumentation Inference with Grounded References), a structured framework designed to analyze influencer discourse, which operates in three stages: (1) identifying the core influencer recommendation--takeaway; (2) constructing an argumentation graph that captures influencer justification for the takeaway; (3) performing factor graph-based probabilistic inference to validate the takeaway. We evaluate TAIGR on a content validation task over influencer video transcripts on health, showing that accurate validation requires modeling the discourse's pragmatic and argumentative structure rather than treating transcripts as flat collections of claims.

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