AUTHOR=Matoshi Veton , De Vuono Maria Carmela , Gaspari Roberto , Kröll Mark , Jantscher Michael , Nicolardi Sara Lucia , Mazzola Giuseppe , Rauch Manuela , Sabol Vedran , Salhofer Eileen , Mariani Riccardo 

TITLE=One size fits all: Enhanced zero-shot text classification for patient listening on social media

JOURNAL=Frontiers in Artificial Intelligence

VOLUME=Volume 7 - 2024

YEAR=2025

URL=https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2024.1397470

DOI=10.3389/frai.2024.1397470

ISSN=2624-8212

ABSTRACT=Patient-focused drug development (PFDD) represents a transformative approach that is reshaping the pharmaceutical landscape by centering on patients throughout the drug development process. Recent advancements in Artificial Intelligence (AI), especially in Natural Language Processing (NLP), have enabled the analysis of vast social media datasets, also called Social Media Listening (SML), providing insights not only into patient perspectives but also into those of other interest groups such as caregivers. In this method study, we propose an NLP framework that—given a particular disease—is designed to extract pertinent information related to three primary research topics: identification of interest groups, understanding of challenges, and assessing treatments and support systems. Leveraging external resources like ontologies and employing various NLP techniques, particularly zero-shot text classification, the presented framework yields initial meaningful insights into these research topics with minimal annotation effort.