@inproceedings{chakraborty-etal-2026-csecu,
    title = "{CSECU}-{DSG} at {S}em{E}val-2026 Task 10: Fine-Tuning {D}e{BERT}a Transformer Model for Conspiracy Detection",
    author = "Chakraborty, Debashish  and
      Tabassum, Sumaiya  and
      Ibnath, Sabrina  and
      Chy, Abu Nowshed",
    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-1.153/",
    pages = "1122--1127",
    ISBN = "979-8-89176-414-9",
    abstract = "Conspiracy detection aims to determine whether a social media post expresses belief in conspiracy theories. This task is essential for understanding harmful online discourse and mitigating the spread of misinformation. However, detecting conspiracy beliefs is challenging due to subtle psycholinguistic cues and the strong contextual dependency of such claims. To address these challenges, SemEval-2026 Task 10 introduced a shared task named PsyCoMark. In this paper, we describe our approach to Subtask 2, which focuses on detecting conspiracy beliefs. We propose a transformer-based classification approach using a fine-tuned DeBERTa-v3-base model to detect conspiracy beliefs in Reddit comments. Each post is processed as a single input sequence. To address class imbalance and improve generalization, we employ class-weighted cross-entropy loss with label smoothing during training. Our approach achieves competitive performance, ranked ninth among participating teams. The findings demonstrate that fine-tuned transformer models effectively capture contextual and psycholinguistic patterns in conspiracy-related discourse and achieve competitive performance compared to other systems."
}