流行病相关内容中的语言模式:对COVID-19,约束和麻疹数据集的比较分析
Mkululi Sikosana1, Sean Maudsley-Barton1, Oluwaseun Ajao1
1Department of Computing and Mathematics, Manchester Metropolitan University, Manchester, United Kingdom.
Frontiers in artificial intelligence
|October 1, 2025
概括
在线健康错误信息显示可读性较低,并且使用更多基于恐惧的语言而不是事实内容. 识别这些语言特征有助于在危机期间检测健康错误信息.
科学领域:
- 计算语言学 计算语言学
- 公共卫生传播 公共卫生传播
- 数字话语分析 数字话语分析
背景情况:
- 区分健康错误信息与事实上的在线沟通对于公共卫生至关重要.
- 与流行病相关的在线话语在识别虚假叙述方面提出了独特的挑战.
- 有效的公共卫生信息需要了解错误信息的语言特征.
研究的目的:
- 在流行病背景下调查区分健康错误信息与事实沟通的语言特征.
- 识别可检测的健康错误信息的语言标记.
- 在卫生危机期间为公共卫生信息传递策略提供信息.
主要方法:
- 计算语言分析了三个体:COVID-19虚假叙述,一般的COVID-19内容和与相关的帖子.
- 检查可读性,修辞标记和说服性的语言.
- 对错信息与事实沟通之间的差异进行比较分析.
主要成果:
- 在COVID-19错误信息中,可读性得分明显较低.
- 与事实内容相比,虚假信息包含与恐惧相关的和有说服力的术语的频率是事实内容的两倍多.
- 错误的信息显示了极少使用惊呼号,与更具情感性的麻疹内容不同.
结论:
- 错误的健康信息可能采用复杂的修辞风格,带有情感线索,以增强所感知的可信度.
- 识别的语言指标可以帮助检测数字健康错误信息.
- 调查结果为网络媒体环境中的危机沟通模型和公共卫生策略提供了信息.
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