@inproceedings{kong-etal-2026-said,
    title = "From What Is Said to Why It Is Framed: Intent-Aware News Video Understanding",
    author = "Kong, Xiangzheng  and
      Luo, Minnan  and
      Wang, Wenya  and
      Wu, Jiaying  and
      Zeng, Zhi  and
      Dai, Guang",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.1945/",
    pages = "39039--39050",
    ISBN = "979-8-89176-395-1",
    abstract = "Short-form news videos increasingly shape public perception through strategic framing, yet existing verification methods largely overlook the communicative intent underlying such content. By emphasizing surface semantics, current models struggle to separate stylistic presentation from factual evidence, which leads to shortcut learning and brittle generalization. To address this limitation, we propose the Origin{--}Objective{--}Means (OOM) framework, a theory-grounded representation of communicative intent that captures creator stance, audience need activation, and communication strategy. We validate OOM through large-scale human annotation, revealing distinct and consistent lexical and structural patterns across intent dimensions. Building on this representation, we operationalize intent as an explicit semantic condition rather than a prediction target. Concretely, we introduce Intent-Guided Prompting (IGP) to condition LLM reasoning and intent-conditioned multimodal detection framework (ICMD), which injects intent into multimodal detectors via feature-wise modulation. Experiments on FakeSV and FakeTT show that modeling intent as an intermediate condition consistently improves accuracy and robustness across diverse vision{--}language backbones, while substantially reducing reliance on spurious stylistic correlations."
}