COVID-19对中国心理健康的影响:基于情感知识的分析,增强了预训练和XGBoost算法
1School of Economics and Management, Nanjing University of Science and Technology, Nanjing, China.
Frontiers in public health
|July 24, 2023
概括
社交媒体文本数据揭示了2019年新冠病毒病 (COVID-19) 对中国公众心理健康的重大影响. 这项研究强调了社会数据.
科学领域:
- 社会科学 社会科学 社会科学
- 公共卫生 公共卫生
- 计算社会科学 计算社会科学
背景情况:
- 新冠病毒病2019 (COVID-19) 已引起全球广泛关注,影响日常生活和心理健康.
- 有限的研究存在于COVID-19流行病对人口心理健康的具体影响.
- 了解心理影响对于制定公共卫生政策至关重要.
研究的目的:
- 调查COVID-19对中国人口心理健康的影响.
- 探索社交媒体文本数据在疫情期间评估公共心理健康的实用性.
- 建立一种分析社交媒体数据的方法,以告知公共卫生战略.
主要方法:
- 从中国主要社交媒体平台新浪微博收集并过了与COVID-19相关的帖子.
- 使用单词分割处理文本数据并构建一个单词向量稀疏矩阵.
- 采用情感知识增强预训练模型 (SKEP) 来确定用户的心理状态 (积极/消极概率).
- 利用XGBoost算法分析文本数据特征与心理健康结果之间的关系.
主要成果:
- 社交文本数据有效地反映了用户的心理健康状况.
- 分析表明,COVID-19相关的社交媒体内容与公共心理状态之间存在相关性.
- 该研究通过社会数据提供了对大流行病对心理健康影响的定量见解.
结论:
- 社交媒体文本数据是监测和理解公共心理健康趋势的宝贵资源,在危机期间,如COVID-19大流行.
- 调查结果可以为制定有针对性的公共卫生干预和政策提供信息.
- 这种方法提供了一种可扩展的实时心理健康评估方法,用于大量人群.
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