从罗马尼亚推特中识别,分析和预测与疫苗有关的有效和错误信息
Andrei Valeanu1, Dragos Paul Mihai1, Corina Andrei1
1Faculty of Pharmacy, "Carol Davila" University of Medicine and Pharmacy, Bucharest, Romania.
Frontiers in public health
|February 16, 2024
概括
关于疫苗的错误信息在推特上迅速传播,假新闻更具吸引力. 这项研究分析了1400条罗马尼亚推特,发现机器学习模型可以有效地检测出虚假的疫苗信息.
科学领域:
- 公共卫生 公共卫生
- 社交媒体分析 社交媒体分析
- 计算语言学 计算语言学
背景情况:
- 在线错误信息对公共卫生倡议,特别是疫苗接种运动构成重大威胁.
- 关于罗马尼亚语的疫苗相关在线内容的研究存在差距.
研究的目的:
- 检测和评估罗马尼亚语言的与疫苗有关的推特.
- 分析错误信息的模式,并识别社交媒体上虚假疫苗新闻的特征.
主要方法:
- 手动分类1400个罗马尼亚疫苗相关推特,分为真实,中立和假的类别.
- 使用wordcloud表示,相关性分析和机器学习算法 (支持矢量分类器) 的分析.
主要成果:
- 虚假信息推文表现出明显的词语模式和比真实或中立的推文更高的参与度 (喜欢,重新分享).
- 机器学习模型,特别是支持矢量分类器,显示出强大的预测性能 (AUC 0.744-0.843).
- 开发的预测模型准确地估计了一个推特是真实的,中性的或假的概率.
结论:
- 这项研究提供了东欧语言对疫苗错误信息的关键见解.
- 强调需要开发平台,以便快速检测在线疫苗错误信息.
- 强调提高公众对数字健康信息的认识的重要性.
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