@inproceedings{carabas-etal-2026-psy,
    title = "psy detectives at {S}em{E}val-2026 Task 10: {P}sy{C}o{M}ark {--} Psycholinguistic Conspiracy Marker Extraction and Detection",
    author = "Carabas, Roxana  and
      Nacu, Anamaria  and
      Isac, Lucian  and
      Gifu, Daniela",
    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-1.1/",
    pages = "1--7",
    ISBN = "979-8-89176-414-9",
    abstract = "We present our SemEval-2026 Task 10 (PsyCoMark) system that combines interpretable psycholinguistic signals with supervised neural modeling. Our approach includes (1) a marker-derived lexicon and LIWC-style ratio features built from span annotations, (2) binary Yes/No transformer baselines (RoBERTa and DeBERTa families) with optimized training configurations, and (3) a zero-shot TinyLlama-1.1B baseline for the full three-way setting (Yes/No/Can{'}t tell). Results show that marker-only features are transparent but weak, while transformer models provide substantially stronger performance; the best model, DeBERTa-v3-large, achieves 0.8339 weighted F1 on development and 0.75 weighted F1 on the competition test set. We also evaluate marker-driven heuristic relabeling of uncertain instances, which does not improve downstream performance. Overall, the submission provides a controlled, interpretable, and reproducible reference point for future work on integrating span-level psycholinguistic evidence with conspiracy detection."
}