@inproceedings{robertson-liang-2025-ncl,
    title = "{NCL}-{AR} at {S}em{E}val-2025 Task 7: A Sieve Filtering Approach to Refute the Misinformation within Harmful Social Media Posts",
    author = "Robertson, Alex  and
      Liang, Huizhi(elly)",
    editor = "Rosenthal, Sara  and
      Ros{\'a}, Aiala  and
      Ghosh, Debanjan  and
      Zampieri, Marcos",
    booktitle = "Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/corrections-2025-08/2025.semeval-1.44/",
    pages = "308--313",
    ISBN = "979-8-89176-273-2",
    abstract = "In this paper, we propose a sieve filtering-based approach that can retrieve facts to invalidate claims made in social media posts. The fact filters are initially coarse-grained, based on the original language of the social media posts, and end with fine-grained filters based on the exact time frame in which the posts were uploaded online. This streamlined approach achieved a 0.883 retrieval success rate in the monolingual task while maintaining a scalable efficiency level of processing a social media post per 0.07 seconds."
}