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

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Nakshatri, Nishanth Sridhar, Caplan, Eylon, Pujari, Rajkumar, Goldwasser, Dan
Format:	Preprint
Published:	2026
Subjects:	Computation and Language
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

_version_	1866918310325518336
author	Nakshatri, Nishanth Sridhar Caplan, Eylon Pujari, Rajkumar Goldwasser, Dan
author_facet	Nakshatri, Nishanth Sridhar Caplan, Eylon Pujari, Rajkumar Goldwasser, Dan
contents	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.
format	Preprint
id	arxiv_https___arxiv_org_abs_2601_20032
institution	arXiv
publishDate	2026
record_format	arxiv
spellingShingle	TAIGR: Towards Modeling Influencer Content on Social Media via Structured, Pragmatic Inference Nakshatri, Nishanth Sridhar Caplan, Eylon Pujari, Rajkumar Goldwasser, Dan Computation and Language 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.
title	TAIGR: Towards Modeling Influencer Content on Social Media via Structured, Pragmatic Inference
topic	Computation and Language
url	https://arxiv.org/abs/2601.20032