@inproceedings{fariha-etal-2026-cuet320,
    title = "{CUET}320 at {S}em{E}val-2026 Task 10: Few-Shot Large Language Models for Psycholinguistic Marker Extraction and Conspiracy Detection",
    author = "Fariha, Faozia  and
      Khan, Lamia  and
      Chowdhury, Madiha Ahmed  and
      Ahmed, Kawsar  and
      Hoque, Mohammed Moshiul",
    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-1.432/",
    pages = "3495--3507",
    ISBN = "979-8-89176-414-9",
    abstract = "Conspiracy theories widely spread on social media and can harm society by increasing mistrust, vaccine hesitancy, and political radicalization. However, most automated detection systems have traditionally relied on topic-specific classifiers, which often struggle to generalize across domains and provide little explanation for why a text is considered conspiratorial. To address these limitations, this paper explores various LLMs on the SemEval-2026 Task 10: psycholinguistic conspiracy marker extraction and binary conspiracy detection from Reddit submission statements. Specifically, we adopt a training-free few-shot prompting approach using different instruction-tuned large language models under a variety of few-shot settings (k in {\{}0,1,5,10,15, 20{\}}). Within this framework, the proposed prompting strategy incorporates psychology-informed instructions to guide the models in identifying conspiracy-related signals. As a result, the presented system achieves an F1 score of 0.21 for marker extraction and 0.81 for conspiracy detection, ranking 16th out of 30 teams in Subtask{\textasciitilde}1 and 36th out of 52 in Subtask{\textasciitilde}2 without any task-specific fine-tuning. These results suggest that psycholinguistically grounded prompting can support interpretable conspiracy analysis; however, challenges remain in identifying implicit markers."
}