@inproceedings{zhu-2026-jia,
    title = "{JIA} at {S}em{E}val-2026 Task 10: A Dual-Track System with {BERT}-based Encoders and {LLM}s for Conspiracy Analysis",
    author = "Zhu, Jiayue",
    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-1.174/",
    pages = "1332--1339",
    ISBN = "979-8-89176-414-9",
    abstract = "This paper presents a dual-track system for conspiracy theory detection and psycholinguistic marker extraction. We evaluate multiple architectures, including DistilBERT, BERT-Base, DeBERTa-V3, RoBERTa, and instruction-tuned Qwen2.5 models. Qwen2.5-14B (full-shot) achieves the best performance with a Weighted F1-score of 0.80 in the detection task. Marker extraction remains challenging: while the fine-tuned LLM performs best on ``Actors,'' its limited generalization in categories such as ``Evidence'' and ``Effect'' highlights persistent semantic ambiguity."
}