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

ACL ARR 2026 January Submission9098 Authors

Keywords: Influencer Discourse, Structured Inference, Social Media Discourse 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 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.

Paper Type: Long

Research Area: Discourse, Pragmatics, and Reasoning

Research Area Keywords: Modeling influencer discourse, pragmatic inference and reasoning, discourse-level inference

Contribution Types: Model analysis & interpretability, NLP engineering experiment, Data analysis

Languages Studied: English

Submission Number: 9098