PDF(Pubmed)
Abstract：
An ongoing monitoring of national and subnational trajectory of COVID-19 vaccine hesitancy could offer support in designing tailored policies on improving vaccine uptake.
We aim to track the temporal and spatial distribution of COVID-19 vaccine hesitancy and confidence expressed on Twitter during the entire pandemic period in major English-speaking countries.
We collected 5,257,385 English-language tweets regarding COVID-19 vaccination between January 1, 2020, and June 30, 2022, in 6 countries-the United States, the United Kingdom, Australia, New Zealand, Canada, and Ireland. Transformer-based deep learning models were developed to classify each tweet as intent to accept or reject COVID-19 vaccination and the belief that COVID-19 vaccine is effective or unsafe. Sociodemographic factors associated with COVID-19 vaccine hesitancy and confidence in the United States were analyzed using bivariate and multivariable linear regressions.
The 6 countries experienced similar evolving trends of COVID-19 vaccine hesitancy and confidence. On average, the prevalence of intent to accept COVID-19 vaccination decreased from 71.38% of 44,944 tweets in March 2020 to 34.85% of 48,167 tweets in June 2022 with fluctuations. The prevalence of believing COVID-19 vaccines to be unsafe continuously rose by 7.49 times from March 2020 (2.84% of 44,944 tweets) to June 2022 (21.27% of 48,167 tweets). COVID-19 vaccine hesitancy and confidence varied by country, vaccine manufacturer, and states within a country. The democrat party and higher vaccine confidence were significantly associated with lower vaccine hesitancy across US states.
COVID-19 vaccine hesitancy and confidence evolved and were influenced by the development of vaccines and viruses during the pandemic. Large-scale self-generated discourses on social media and deep learning models provide a cost-efficient approach to monitoring routine vaccine hesitancy.
We aim to track the temporal and spatial distribution of COVID-19 vaccine hesitancy and confidence expressed on Twitter during the entire pandemic period in major English-speaking countries.
We collected 5,257,385 English-language tweets regarding COVID-19 vaccination between January 1, 2020, and June 30, 2022, in 6 countries-the United States, the United Kingdom, Australia, New Zealand, Canada, and Ireland. Transformer-based deep learning models were developed to classify each tweet as intent to accept or reject COVID-19 vaccination and the belief that COVID-19 vaccine is effective or unsafe. Sociodemographic factors associated with COVID-19 vaccine hesitancy and confidence in the United States were analyzed using bivariate and multivariable linear regressions.
The 6 countries experienced similar evolving trends of COVID-19 vaccine hesitancy and confidence. On average, the prevalence of intent to accept COVID-19 vaccination decreased from 71.38% of 44,944 tweets in March 2020 to 34.85% of 48,167 tweets in June 2022 with fluctuations. The prevalence of believing COVID-19 vaccines to be unsafe continuously rose by 7.49 times from March 2020 (2.84% of 44,944 tweets) to June 2022 (21.27% of 48,167 tweets). COVID-19 vaccine hesitancy and confidence varied by country, vaccine manufacturer, and states within a country. The democrat party and higher vaccine confidence were significantly associated with lower vaccine hesitancy across US states.
COVID-19 vaccine hesitancy and confidence evolved and were influenced by the development of vaccines and viruses during the pandemic. Large-scale self-generated discourses on social media and deep learning models provide a cost-efficient approach to monitoring routine vaccine hesitancy.
摘要：
背景：对COVID-19疫苗犹豫的国家和国家以下轨迹的持续监测可以为设计量身定制的政策提供支持，以提高疫苗的吸收。
目的：我们旨在追踪主要英语国家在整个大流行期间在Twitter上表达的COVID-19疫苗犹豫和信心的时空分布。
方法：我们在2020年1月1日至2022年6月30日期间，在6个国家-美国收集了5,257,385条关于COVID-19疫苗接种的英文推文，联合王国,澳大利亚,新西兰,加拿大,和爱尔兰。开发了基于Transformer的深度学习模型，将每条推文分类为接受或拒绝COVID-19疫苗接种的意图，以及认为COVID-19疫苗有效或不安全的信念。使用双变量和多变量线性回归分析了美国与COVID-19疫苗犹豫和信心相关的社会人口统计学因素。
结果：6个国家经历了类似的COVID-19疫苗犹豫和信心演变趋势。平均而言,接受COVID-19疫苗接种意向的患病率从2020年3月的44,944条推文中的71.38%下降到2022年6月的48,167条推文中的34.85%，并出现波动。从2020年3月（44,944条推文中的2.84%）到2022年6月（48,167条推文中的21.27%），认为COVID-19疫苗不安全的患病率连续上升7.49倍。COVID-19疫苗的犹豫和信心因国家而异，疫苗制造商，和一个国家内的国家。民主党和较高的疫苗信心与美国各州较低的疫苗犹豫显着相关。
结论：在大流行期间，COVID-19疫苗的犹豫和信心不断演变，并受到疫苗和病毒开发的影响。社交媒体和深度学习模型上的大规模自我生成话语为监测常规疫苗犹豫提供了一种经济高效的方法。
目的：我们旨在追踪主要英语国家在整个大流行期间在Twitter上表达的COVID-19疫苗犹豫和信心的时空分布。
方法：我们在2020年1月1日至2022年6月30日期间，在6个国家-美国收集了5,257,385条关于COVID-19疫苗接种的英文推文，联合王国,澳大利亚,新西兰,加拿大,和爱尔兰。开发了基于Transformer的深度学习模型，将每条推文分类为接受或拒绝COVID-19疫苗接种的意图，以及认为COVID-19疫苗有效或不安全的信念。使用双变量和多变量线性回归分析了美国与COVID-19疫苗犹豫和信心相关的社会人口统计学因素。
结果：6个国家经历了类似的COVID-19疫苗犹豫和信心演变趋势。平均而言,接受COVID-19疫苗接种意向的患病率从2020年3月的44,944条推文中的71.38%下降到2022年6月的48,167条推文中的34.85%，并出现波动。从2020年3月（44,944条推文中的2.84%）到2022年6月（48,167条推文中的21.27%），认为COVID-19疫苗不安全的患病率连续上升7.49倍。COVID-19疫苗的犹豫和信心因国家而异，疫苗制造商，和一个国家内的国家。民主党和较高的疫苗信心与美国各州较低的疫苗犹豫显着相关。
结论：在大流行期间，COVID-19疫苗的犹豫和信心不断演变，并受到疫苗和病毒开发的影响。社交媒体和深度学习模型上的大规模自我生成话语为监测常规疫苗犹豫提供了一种经济高效的方法。
