@inproceedings{borazio-etal-2024-semi,
    title = "Semi-Automatic Topic Discovery and Classification for Epidemic Intelligence via Large Language Models",
    author = "Borazio, Federico  and
      Croce, Danilo  and
      Gambosi, Giorgio  and
      Basili, Roberto  and
      Margiotta, Daniele  and
      Scaiella, Antonio  and
      Del Manso, Martina  and
      Petrone, Daniele  and
      Cannone, Andrea  and
      Urdiales, Alberto M.  and
      Sacco, Chiara  and
      Pezzotti, Patrizio  and
      Riccardo, Flavia  and
      Mipatrini, Daniele  and
      Ferraro, Federica  and
      Pilati, Sobha",
    editor = "Afli, Haithem  and
      Bouamor, Houda  and
      Casagran, Cristina Blasi  and
      Ghannay, Sahar",
    booktitle = "Proceedings of the Second Workshop on Natural Language Processing for Political Sciences @ LREC-COLING 2024",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.politicalnlp-1.8",
    pages = "68--84",
    abstract = "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.",
}

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.
Borazio, Federico Croce, Danilo Gambosi, Giorgio Basili, Roberto Margiotta, Daniele Scaiella, Antonio Del Manso, Martina Petrone, Daniele Cannone, Andrea Urdiales, Alberto M. Sacco, Chiara Pezzotti, Patrizio Riccardo, Flavia Mipatrini, Daniele Ferraro, Federica Pilati, Sobha
Semi-Automatic Topic Discovery and Classification for Epidemic Intelligence via Large Language Models