Exploring and Monitoring the Reasons for Hesitation with COVID-19 Vaccine Based on Social-Platform Text and Classification Algorithms
| Autor: | Jingfang Liu, Caiying Lu, Shuangjinhua Lu |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
vaccine hesitant
medicine.medical_specialty text classification Coronavirus disease 2019 (COVID-19) Leadership and Management Computer science Health Policy Public health Health Informatics Logistic regression Data science Article Herd immunity Support vector machine Naive Bayes classifier Statistical classification Health Information Management Pandemic medicine Medicine COVID-19 vaccine |
| Zdroj: | Healthcare Volume 9 Issue 10 Healthcare, Vol 9, Iss 1353, p 1353 (2021) |
| ISSN: | 2227-9032 |
| DOI: | 10.3390/healthcare9101353 |
| Popis: | (1) Background: The COVID-19 pandemic is globally rampant, and it is the common goal of all countries to eliminate hesitation in taking the COVID-19 vaccine and achieve herd immunity as soon as possible. However, people are generally more hesitant about the COVID-19 vaccine than about other conventional vaccines, and exploring the specific reasons for hesitation with the COVID-19 vaccine is crucial. (2) Methods: this paper selected text data from a social platform to conduct qualitative analysis of the text to structure COVID-19 vaccine hesitancy reasons, and then conducted semiautomatic quantitative content analysis of the text through a supervised machine-learning method to classify them. (3) Results: on the basis of a large number of studies and news reports on vaccine hesitancy, we structured 12 types of the COVID-19 vaccine hesitancy reasons. Then, in the experiment, we conducted comparative analysis of three classifiers: support vector machine (SVM), logistic regression (LR), and naive Bayes classifier (NBC). Results show that the SVM classification model with TF-IDF and SMOTE had the best performance. (4) Conclusions: our study structured 12 types of COVID-19 vaccine hesitancy reasons through qualitative analysis, filling in the gaps of previous studies. At the same time, this work provides public health institutions with a monitoring tool to support efforts to mitigate and eliminate COVID-19 vaccine hesitancy. |
| Databáze: | OpenAIRE |
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