Publication: Multi-perspectives systematic review on the applications of sentiment analysis for vaccine hesitancy
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Date
2021
Authors
Alamoodi A.H.
Zaidan B.B.
Al-Masawa M.
Taresh S.M.
Noman S.
Ahmaro I.Y.Y.
Garfan S.
Chen J.
Ahmed M.A.
Zaidan A.A.
Journal Title
Journal ISSN
Volume Title
Publisher
Elsevier Ltd
Abstract
A substantial impediment to widespread Coronavirus disease (COVID-19) vaccination is vaccine hesitancy. Many researchers across scientific disciplines have presented countless studies in favor of COVID-19 vaccination, but misinformation on social media could hinder vaccination efforts and increase vaccine hesitancy. Nevertheless, studying people's perceptions on social media to understand their sentiment presents a powerful medium for researchers to identify the causes of vaccine hesitancy and therefore develop appropriate public health messages and interventions. To the best of the authors' knowledge, previous studies have presented vaccine hesitancy in specific cases or within one scientific discipline (i.e., social, medical, and technological). No previous study has presented findings via sentiment analysis for multiple scientific disciplines as follows: (1) social, (2) medical, public health, and (3) technology sciences. Therefore, this research aimed to review and analyze articles related to different vaccine hesitancy cases in the last 11 years and understand the application of sentiment analysis on the most important literature findings. Articles were systematically searched in Web of Science, Scopus, PubMed, IEEEXplore, ScienceDirect, and Ovid from January 1, 2010, to July 2021. A total of 30 articles were selected on the basis of inclusion and exclusion criteria. These articles were formed into a taxonomy of literature, along with challenges, motivations, and recommendations for social, medical, and public health and technology sciences. Significant patterns were identified, and opportunities were promoted towards the understanding of this phenomenon. � 2021 Elsevier Ltd
Description
Public health; Social networking (online); Vaccines; Coronaviruses; Medical; Multi-perspective; Scientific discipline; Sentiment analysis; Social; Social media; Systematic Review; Technology; Vaccine hesitancy; Sentiment analysis; Human papilloma virus vaccine; measles vaccine; SARS-CoV-2 vaccine; artificial intelligence; conspiracy theory; contact examination; coronavirus disease 2019; deep learning; health promotion; human; machine learning; measles; medical technology; misinformation; natural language processing; pandemic; papillomavirus infection; population surveillance; public health message; public opinion; Review; sentiment analysis; social media; statistical analysis; systematic review; transfer of learning; vaccination; vaccination coverage; vaccine hesitancy; COVID-19; COVID-19 Vaccines; Humans; SARS-CoV-2; Sentiment Analysis; Vaccination; Vaccination Hesitancy