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

TL;DR

TAIGR is a structured framework that models influencer discourse to better validate health-related claims by capturing pragmatic and argumentative structures rather than relying on flat claim analysis.

Contribution

This paper introduces TAIGR, a novel structured approach for analyzing influencer content through pragmatic inference, addressing limitations of traditional claim-centric verification methods.

Findings

TAIGR outperforms baseline methods in content validation tasks.

Modeling discourse structure improves claim validation accuracy.

Pragmatic and argumentative modeling is essential for influencer content analysis.

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.