@inproceedings{takahashi-2026-hidetsune,
    title = "Hidetsune at {S}em{E}val-2026 Task 10: A Systematic Exploration of Training and Inference Strategies for Detecting Conspiracy Beliefs",
    author = "Takahashi, Hidetsune",
    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-1.26/",
    pages = "176--181",
    ISBN = "979-8-89176-414-9",
    abstract = "This paper describes a system developed for SemEval-2026 Task 10 Subtask 2, which focuses on identifying conspiracy beliefs expressed in Reddit comments. The study begins with a comparative analysis of language models fine-tuned on the task data. In addition to fine-tuning, multiple auxiliary techniques were examined, including instruction-based prompting, data augmentation via back-translation, and loss function methods designed to address label imbalance. In the final stage, the inference behavior was further examined by varying the decision threshold applied to the softmax output probabilities. The results highlight how choices made during model selection, training, and inference collectively affect performance, offering empirical insights into the challenges of conspiracy belief detection in social media contexts."
}