@inproceedings{peng-gehao-2026-zhangpeng-semeval,
    title = "zhangpeng at {S}em{E}val-2026 Task 10: {P}sy{C}o{M}ark - Psycholinguistic Conspiracy Marker Extraction and Detection",
    author = "Peng, Zhang  and
      Gehao, Lu",
    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-1.107/",
    pages = "755--760",
    ISBN = "979-8-89176-414-9",
    abstract = "We describe our system for SemEval-2026 Task 10 on psycholinguistic conspiracy marker extraction and conspiracy detection from English texts. The shared task consists of two subtasks: (1) extracting conspiracy-related markers{---}actor, action, effect, victim, and evidence{---}evaluated using an overlap-based macro F1-score, and (2) detecting conspiracy content as a binary text classification problem evaluated using macro-averaged F1-score. Our approach relies on fine-tuning pre-trained transformer encoders, including multilingual DistilBERT variants and DeBERTa-v3, without using external corpora or data augmentation techniques. Experimental results show that our best models achieve a macro-F1 score of 0.1476 for Subtask{\textasciitilde}1 and a Weighted-F1 score of 0.7267 for Subtask{\textasciitilde}2. These results show that simple fine-tuning of pre-trained models provides a strong baseline for both marker extraction and conspiracy detection."
}