@conference{towards-gsi-conference-2023,
author = "Araque, Oscar and Corniel, Mª Felipa Ledesma and Kyriaki Kalimeri",
abstract = "Understanding public narratives on contentious topics like vaccination adherence is vital for promoting cooperative behaviors. During the COVID-19 pandemic, significant polarization arose from concerns about vaccines, with misinformation and conspiracy beliefs proliferating on social media. While many studies have analyzed these narratives, the focus has largely been on English-language content. This linguistic bias limits comprehensive global insights. Our study introduces a novel multilingual approach that addresses this gap. By integrating Italian examples into a primarily English dataset, we detect vaccine-hesitant language and demonstrate the model’s adaptability to diverse linguistic data. Our findings highlight the importance of incorporating varied linguistic datasets for a more holistic understanding of global narratives on vaccine hesitancy.",
booktitle = " Italian Conference on Computational Linguistics",
editor = "CEUR",
issn = "1613-0073",
keywords = "natural language processing;machine learning",
number = "3596",
title = " {T}owards a {M}ultilingual {S}ystem for {V}accine {H}esitancy {U}sing a {D}ata {M}ixture {A}pproach",
url = "https://ceur-ws.org/Vol-3596/short1.pdf",
volume = "3596",
year = "2023",
}