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Covid-19 Vaccination Stance Detection Using Natural Language Processing and Machine-Learning Algorithms
CEUR Workshop Proceedings ; 3395:337-345, 2022.
Artículo en Inglés | Scopus | ID: covidwho-20243829
ABSTRACT
The coronavirus outbreak has resulted in unprecedented measures, forcing authorities to make decisions related to establishing lockdowns in areas most affected by the pandemic. Social Media have supported people during this difficult time. On November 9, 2020, when the first vaccine with an efficacy rate over 90% was announced, social media reacted and people around the world began to express their feelings about this vaccination. This paper aims to analyze the dynamics of opinion on COVID-19 vaccination, in which the civil society is highly manifested in the vaccination process. We compared classical machine learning algorithms to select the best performing classifier. 4,392 tweets were collected and analyzed. The proposed approach can help governments create and evaluate appropriate communication tools to provide clear and relevant information to the general public, increasing public confidence in vaccination campaigns. © 2022 Copyright for this paper by its authors.
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Colección: Bases de datos de organismos internacionales Base de datos: Scopus Tipo de estudio: Estudio experimental Tópicos: Vacunas Idioma: Inglés Revista: CEUR Workshop Proceedings Año: 2022 Tipo del documento: Artículo

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Colección: Bases de datos de organismos internacionales Base de datos: Scopus Tipo de estudio: Estudio experimental Tópicos: Vacunas Idioma: Inglés Revista: CEUR Workshop Proceedings Año: 2022 Tipo del documento: Artículo