对COVID-19错误/错误信息叙述的趋势分析 - 一项为期3年的研究
Bonka Kotseva1, Irene Vianini1, Nikolaos Nikolaidis2
1Piksel S.r.l., Ispra, VA, Italy.
PloS one
|November 17, 2023
概括
这项研究分析了来自未经验证的来源的COVID-19错误信息,确定了12个不断发展的超级叙事. 这些发现揭示了真实事件往往是如何被误导的,这使得几乎可以实时监控健康虚假信息.
科学领域:
- 数字健康数字健康
- 信息科学 信息科学 信息科学
- 公共卫生 公共卫生
背景情况:
- 随着COVID-19大流行,出现了一场严重的传染病,其特点是广泛的错误信息.
- 未经验证的消息来源在扩大有关病毒和公共卫生措施的虚假或误导性叙述中发挥了作用.
研究的目的:
- 识别和分类COVID-19错误信息的主要叙述.
- 使用机器学习自动化错误信息叙述的分类.
- 分析这些叙述的时间和地理传播.
主要方法:
- 分析了来自460个未经验证来源的58,625篇文章 (2020年1月至2022年12月).
- 针对错误信息叙述的多层次代码书的开发.
- 训练基于变压器的模型用于自动叙事分类.
- 使用机器翻译和聚类来处理文章.
主要成果:
- 确定了12个不同的COVID-19错误信息超级叙述,这些错误信息在三年内演变.
- 证明错误信息往往源于对现实世界事件的歪曲或脱离背景.
- 建立一个接近实时监控健康虚假信息的过程.
结论:
- 一种系统的方法可以有效地识别和跟踪COVID-19错误信息叙述.
- 开发的方法提供了一个监测其他关键主题虚假信息的框架.
- 了解叙事演变是打击信息流行病的关键.
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