@inproceedings{nakshatri-etal-2026-taigr,
title = "{TAIGR}: Towards Modeling Influencer Content on Social Media via Structured, Pragmatic Inference",
author = "Nakshatri, Nishanth Sridhar and
Caplan, Eylon and
Pujari, Rajkumar and
Goldwasser, Dan",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.1868/",
pages = "40224--40248",
ISBN = "979-8-89176-390-6",
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 3 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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%0 Conference Proceedings
%T TAIGR: Towards Modeling Influencer Content on Social Media via Structured, Pragmatic Inference
%A Nakshatri, Nishanth Sridhar
%A Caplan, Eylon
%A Pujari, Rajkumar
%A Goldwasser, Dan
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F nakshatri-etal-2026-taigr
%X 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 3 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.
%U https://aclanthology.org/2026.acl-long.1868/
%P 40224-40248
Markdown (Informal)
[TAIGR: Towards Modeling Influencer Content on Social Media via Structured, Pragmatic Inference](https://aclanthology.org/2026.acl-long.1868/) (Nakshatri et al., ACL 2026)
ACL