在社交媒体上公共卫生沟通的阅读等级水平估计因文本预处理而有所不同
Samuel R Mendez1, Sebastian Muñoz-Najar Galvez2, Karen M Emmons1
1Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA.
Journal of health communication
|November 8, 2025
概括
可读性公式为社交媒体上的健康传播提供了不一致的估计. 研究人员应该报告文本预处理决策,并考虑使用直接计数而不是简短的在线内容公式.
科学领域:
- 卫生沟通健康沟通
- 计算语言学 计算语言学
背景情况:
- 可读性公式被广泛用于评估健康传播材料.
- 由于文本预处理的差异,这些公式可以产生不同的估计值.
- 在计算机评估中,这种变异性往往被忽视.
研究的目的:
- 为了说明文本预处理决策对短形式在线内容可读性估计的影响.
- 强调报告预处理方法的重要性以及可读性公式的局限性.
- 为了比较与健康相关的推文的不同预处理场景.
主要方法:
- 从美国各州卫生机构 (2012-2022) 的100条推文中手动计数单词,句子和音节.
- 在7个预处理场景下应用简化测量Gobbledygook和Flesch-Kincaid公式,包括URL,标签和数字.
- 与限制性基线对比估计,不包括这些元素,使用威尔科克森签名等级测试和线性回归.
主要成果:
- 在预处理场景中发现了中等年级水平估计的显著差异.
- 在满足八年级可读性基准的推文比例上没有观察到显著差异.
- 基线估计没有完全考虑可读性得分的观察到的变化.
结论:
- 可读性公式对于评估短形式的在线内容,如推特,是不可靠的.
- 建议直接报告单词,句子和音节数量,而不是基于公式的估计.
- 进行可读性评估的敏感性分析时,建议了解公式的限制.
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