@inproceedings{pan-2026-aaa,
    title = "{AAA} at {S}em{E}val-2026 Task 10: Psycholinguistic Conspiracy Marker Extraction and Detection",
    author = "Pan, Xintong",
    editor = "Kochmar, Ekaterina  and
      Ghosh, Debanjan  and
      North, Kai  and
      Komachi, Mamoru",
    booktitle = "Proceedings of the 20th {I}nternational {W}orkshop on {S}emantic {E}valuation (2026)",
    month = jul,
    year = "2026",
    address = "San Diego, California, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.semeval-1.250/",
    pages = "1988--1991",
    ISBN = "979-8-89176-414-9",
    abstract = "This article presents our study on task 10: Psycholinguistic conspiracy marker extraction and detection, which includes token-level extraction tasks and sentence-level conspiracy detection tasks. Focusing on conspiracy theory texts on social media, this paper proposes a classification method that combines semantic encoding with large language model reasoning and generation. Semantic features are extracted using DeBERTa-v3, and explanatory reasoning text is generated through ConspEmoLLM-v2. The two are then combined for classification, thereby enhancing the model{'}s ability to recognize implicit conspiratorial logic. For the extraction subtask, this paper provides systematic comparison results of several mainstream pre-trained models, mainly conducting baseline model comparisons and performance analysis."
}