Disinformation is an escalating global threat, making it essential to understand its content, dissemination, and evolution. To confront this challenge, researchers have begun grouping related false claims into broader disinformation narratives, which can be tracked across cultures, time periods, and media sources. Analyzing these narratives provides critical insights for developing more effective countermeasures. To this end, we introduce DiNO: Disinformation Narrative Observer, a novel method designed to extract disinformation narratives from news articles. We applied DiNO to news articles on the Ukraine War, COVID-19 and Migration, sourced from disinformation-prone outlets as well as a reputable source. We evaluated the narratives extracted by DiNO by measuring how well their topics and stances aligned with a recognized disinformation narratives dataset. DiNO outperforms competitive narrative mining approaches, including Relatio and CaNarEx, achieving a 41%–44% improvement in topical alignment and a 30%–41% improvment in stance alignment.

Arkadiusz Modzelewski, Witold Sosnowski, Tiziano Labruna, Adam Wierzbicki, Giovanni Da San Martino. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2025.

Disinformation poses a significant threat to democratic societies, public health, and national security.To address this challenge, factchecking 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 groundtruth disinformation narratives from the EUD-isinfoTest 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.

Food computing is currently fast-growing into an innovative area of knowledge extraction. However, benchmarks for information extraction from semi-structured data, especially when dealing with more complex relations, are scarce in this domain. In this paper, we introduce a benchmark aimed at information extraction of complex entities to support ingredient substitution tasks. Firstly, we present a new dataset – called TASTEset – for fine-grained recognition of food entities in culinary recipes. Secondly, we provide complex entity annotations for substitution on top of the fine-grained entity mentions, which we carefully prepared. We share the dataset and the tasks to encourage progress on more in-depth and complex information extraction from recipes.

We introduce a new loss function based on cross entropy and SoftTriple loss, TripleEntropy, to improve classification performance for fine-tuning general knowledge pre-trained language models.This loss function can improve the robust RoBERTa baseline model fine-tuned with cross-entropy loss by about 0.02-2.29 percentage points.Thorough tests on popular datasets using our loss function indicate a steady gain.The fewer samples in the training dataset, the higher gain-thus, for smallsized dataset, it is about 0.71 percentage points, for mediumsized-0.86 percentage points, for large-0.20 percentage points, and for extra-large 0.04 percentage points.