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开发和验证VaxConcerns:疫苗担忧和错误信息的分类与众筹可行性
Rickard Stureborg1, Jenna Nichols2, Bhuwan Dhingra1
1Department of Computer Science, Duke University, Durham, NC, USA.
Vaccine
|March 23, 2024
概括
我们开发了VaxConcerns,这是一个新的分类系统,用于对疫苗的担忧和错误信息进行分类. 这种易于使用的框架显示了专家和非专家之间的高度一致,有助于疫苗研究.
科学领域:
- 公共卫生 公共卫生
- 沟通研究 沟通研究
- 信息科学 信息科学 信息科学
背景情况:
- 在线反疫苗媒体经常传播错误信息,并表达了具体的担忧.
- 现有的对疫苗问题的分类框架缺乏广泛的适用性和易用性.
- 了解疫苗犹驱动因素对于公共卫生倡议至关重要.
研究的目的:
- 引入VaxConcerns,这是一个新的,用户友好的分类系统,用于对疫苗的担忧和错误信息进行分类.
- 与现有方法相比,评估VaxConcerns分类学的可靠性和性能.
- 为了证明分类学在分析反疫苗内容转移方面的实用性.
主要方法:
- 基于反疫苗话语中的共同主题,开发了VaxConcerns分类法.
- 通过群众资源工作人员的数据注释和专家协议评估对VaxConcerns的评估.
- 与使用非专家用户的现有分类学相比,VaxConcerns业绩的比较.
- 在COVID-19大流行期间对抗疫苗内容变化的概念验证分析.
主要成果:
- VaxConcerns是一个易于教授的分类学,产生高质量的数据注释.
- 该分类学在专家中表现出高的评级者间可靠性.
- 当非专家注释者使用时,VaxConcerns的表现优于现有分类学.
- 该研究确定了COVID-19大流行期间抗疫内容的变化.
结论:
- VaxConcerns为分类与疫苗相关的担忧和错误信息提供了一个强大的和可访问的框架.
- 该分类的易用性使其在学术和医疗领域之外的采用更加容易.
- 瓦克斯康塞恩有很大的潜力进行纵向研究,追踪疫苗犹和错误信息的趋势.
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