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.
COVID-19; stance classification; Twitter; vaccine; Learning algorithms; Machine learning; Natural language processing systems; Social networking (online); Vaccines; Civil society; Communication tools; Coronaviruses; Forcings; Language processing; Machine learning algorithms; Natural languages; Social media
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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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