Antonio Scaiella


2024

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Semi-Automatic Topic Discovery and Classification for Epidemic Intelligence via Large Language Models
Federico Borazio | Danilo Croce | Giorgio Gambosi | Roberto Basili | Daniele Margiotta | Antonio Scaiella | Martina Del Manso | Daniele Petrone | Andrea Cannone | Alberto M. Urdiales | Chiara Sacco | Patrizio Pezzotti | Flavia Riccardo | Daniele Mipatrini | Federica Ferraro | Sobha Pilati
Proceedings of the Second Workshop on Natural Language Processing for Political Sciences @ LREC-COLING 2024

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.