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This content will become publicly available on April 6, 2027

Title: TAIGR: Towards Modeling Influencer Content on Social Media via Structured, Pragmatic Inference
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.  more » « less
Award ID(s):
2135573
PAR ID:
10675731
Author(s) / Creator(s):
; ; ;
Publisher / Repository:
Accepted to the 64th Annual Meeting of the Association for Computational Linguistics ACL 2026.
Date Published:
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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