TY - JOUR
T1 - Multi-perspectives systematic review on the applications of sentiment analysis for vaccine hesitancy
AU - Alamoodi, A. H.
AU - Zaidan, B. B.
AU - Al-Masawa, Maimonah
AU - Taresh, Sahar M.
AU - Noman, Sarah
AU - Ahmaro, Ibraheem Y. Y.
AU - Garfan, Salem
AU - Chen, Juliana
AU - Ahmed, M. A.
AU - Zaidan, A. A.
AU - Albahri, O. S.
AU - Aickelin, Uwe
AU - Thamir, Noor N.
AU - Fadhil, Julanar Ahmed
AU - Salahaldin, Asmaa
PY - 2021/12
Y1 - 2021/12
N2 - 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.
AB - 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.
KW - Medical
KW - Sentiment analysis
KW - Social
KW - Technology
KW - Vaccine hesitancy
UR - http://www.scopus.com/inward/record.url?scp=85117894540&partnerID=8YFLogxK
U2 - 10.1016/j.compbiomed.2021.104957
DO - 10.1016/j.compbiomed.2021.104957
M3 - Review article
C2 - 34735945
AN - SCOPUS:85117894540
SN - 0010-4825
VL - 139
SP - 1
EP - 18
JO - Computers in Biology and Medicine
JF - Computers in Biology and Medicine
M1 - 104957
ER -