COVID-19 vaccine hesitancy: a social media analysis using deep learning

Serge Nyawa (), Dieudonné Tchuente () and Samuel Fosso-Wamba ()
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Serge Nyawa: TBS Business School
Dieudonné Tchuente: TBS Business School
Samuel Fosso-Wamba: TBS Business School

Annals of Operations Research, 2024, vol. 339, issue 1, No 19, 477-515

Abstract: Abstract Hesitant attitudes have been a significant issue since the development of the first vaccines—the WHO sees them as one of the most critical global health threats. The increasing use of social media to spread questionable information about vaccination strongly impacts the population’s decision to get vaccinated. Developing text classification methods that can identify hesitant messages on social media could be useful for health campaigns in their efforts to address negative influences from social media platforms and provide reliable information to support their strategies against hesitant-vaccination sentiments. This study aims to evaluate the performance of different machine learning models and deep learning methods in identifying vaccine-hesitant tweets that are being published during the COVID-19 pandemic. Our concluding remarks are that Long Short-Term Memory and Recurrent Neural Network models have outperformed traditional machine learning models on detecting vaccine-hesitant messages in social media, with an accuracy rate of 86% against 83%.

Keywords: Deep learning; Neural network; LSTM; Text classification; Vaccine hesitancy; COVID-19; Twitter (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10479-022-04792-3

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