@inproceedings{sosnowski-etal-2025-dinam,
    title = "{D}i{N}a{M}: Disinformation Narrative Mining with Large Language Models",
    author = "Sosnowski, Witold  and
      Modzelewski, Arkadiusz  and
      Skorupska, Kinga  and
      Wierzbicki, Adam",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-main.1537/",
    pages = "30212--30239",
    ISBN = "979-8-89176-332-6",
    abstract = "Disinformation poses a significant threat to democratic societies, public health, and national security. To address this challenge, fact-checking experts analyze and track disinformation narratives. However, the process of manually identifying these narratives is highly time-consuming and resource-intensive. In this article, we introduce DiNaM, the first algorithm and structured framework specifically designed for mining disinformation narratives. DiNaM uses a multi-step approach to uncover disinformation narratives. It first leverages Large Language Models (LLMs) to detect false information, then applies clustering techniques to identify underlying disinformation narratives. We evaluated DiNaM{'}s performance using ground-truth disinformation narratives from the EUDisinfoTest dataset. The evaluation employed the Weighted Chamfer Distance (WCD), which measures the similarity between two sets of embeddings: the ground truth and the predicted disinformation narratives. DiNaM achieved a state-of-the-art WCD score of 0.73, outperforming general-purpose narrative mining methods by a notable margin of 16.4{--}24.7{\%}. We are releasing DiNaM{'}s codebase and the dataset to the public."
}