@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://preview.aclanthology.org/ingest-acl/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."
}