TAIGR: Towards Modeling Influencer Content on Social Media via Structured, Pragmatic Inference
This addresses the challenge of assessing health influencer narratives for public belief formation, representing an incremental advance in content verification by focusing on pragmatic structure.
The paper tackles the problem of verifying health influencer content by proposing TAIGR, a structured framework that models discourse through takeaway identification, argumentation graphs, and probabilistic inference, showing that this approach is necessary for accurate validation compared to flat claim-based methods.
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.