@inproceedings{dang-2026-dangphuduy,
    title = "dangphuduy at {S}em{E}val-2026 Task 10: Span-based Conspiracy Marker Extraction and Emotion-Aware Detection via Gated Fusion",
    author = "Dang, Phu Duy",
    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-1.111/",
    pages = "793--802",
    ISBN = "979-8-89176-414-9",
    abstract = "Conspiracy theories on social media pose significantsocietal risks, making it essential todetect both conspiracy-related content and thetextual spans that serve as conspiracy markers.In this work, we propose two effective methodsto address these challenges. For markerextraction, we develop a span-based slidingwindow framework that improves efficiencyand accuracy by focusing on localized context.In addition, inspired by the distinctive emotionalpatterns in conspiracy texts, we designa dynamic gating mechanism to integrate emotionaland semantic representations. We evaluateour methods on the SemEval 2026 Task 10,where our team (dangphuduy) achieved competitiveresults, ranking 4th in Task 1 (SpanExtraction) and 3rd in Task 2 (Conspiracy Detection).Experimental results demonstrate thatboth proposed methods significantly enhancemodel performance."
}