In recent years, Automatic Fact Checking has become a crucial tool in combating fake news, leveraging AI to verify the accuracy of information. Despite significant advancements, most datasets and models are predominantly available in English, posing challenges for other languages. This paper presents an Italian resource based on the dataset made available in the FEVER evaluation campaign, created to train and evaluate fact-checking models in Italian. The dataset comprises approximately 240k examples, with over 2k test examples manually validated. Additionally, we fine-tuned a state-of-the-art LLM, namely LLaMA3, on both the original English and translated Italian datasets, demonstrating that fine-tuning significantly improves model performance. Our results suggest that the fine-tuned models achieve comparable accuracy in both languages, highlighting the value of the proposed resource.

This paper introduces a novel framework to harness Large Language Models (LLMs) for Epidemic Intelligence, focusing on identifying and categorizing emergent socio-political phenomena within health crises, with a spotlight on the COVID-19 pandemic. Our approach diverges from traditional methods, such as Topic Models, by providing explicit support to analysts through the identification of distinct thematic areas and the generation of clear, actionable statements for each topic. This supports a Zero-shot Classification mechanism, enabling effective matching of news articles to fine-grain topics without the need for model fine-tuning. The framework is designed to be as transparent as possible, producing linguistically informed insights to make the analysis more accessible to analysts who may not be familiar with every subject matter of inherently emerging phenomena. This process not only enhances the precision and relevance of the extracted Epidemic Intelligence but also fosters a collaborative environment where system linguistic abilities and the analyst’s domain expertise are integrated.