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    <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.</abstract>
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%0 Conference Proceedings
%T DiNaM: Disinformation Narrative Mining with Large Language Models
%A Sosnowski, Witold
%A Modzelewski, Arkadiusz
%A Skorupska, Kinga
%A Wierzbicki, Adam
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F sosnowski-etal-2025-dinam
%X 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.
%U https://aclanthology.org/2025.emnlp-main.1537/
%P 30212-30239

Markdown (Informal)

[DiNaM: Disinformation Narrative Mining with Large Language Models](https://aclanthology.org/2025.emnlp-main.1537/) (Sosnowski et al., EMNLP 2025)

• DiNaM: Disinformation Narrative Mining with Large Language Models (Sosnowski et al., EMNLP 2025)

ACL

• Witold Sosnowski, Arkadiusz Modzelewski, Kinga Skorupska, and Adam Wierzbicki. 2025. DiNaM: Disinformation Narrative Mining with Large Language Models. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 30212–30239, Suzhou, China. Association for Computational Linguistics.