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在Twitter上关于心理健康的情绪 - 在COVID-19大流行之前和期间
Felix Beierle1,2, Rüdiger Pryss2, Akiko Aizawa1
1National Institute of Informatics, Tokyo 101-8430, Japan.
Healthcare (Basel, Switzerland)
|November 14, 2023
概括
这项研究分析了COVID-19大流行之前和期间关于心理健康和抑郁症的推文. 调查结果显示,意识和积极情绪增加,机器学习准确地检测到公共话语的变化.
科学领域:
- 计算社会科学 计算社会科学
- 公共卫生信息学 公共卫生信息学
- 自然语言处理自然语言处理.
背景情况:
- COVID-19 疫情对全球公共卫生和心理健康产生了重大影响.
- 现有的关于公众对心理健康情绪的研究往往依赖于有限的调查数据,或者只关注流行病时期.
- 需要对全球重大事件之前和期间关于心理健康和抑郁症的公共话语进行比较分析.
研究的目的:
- 在COVID-19大流行之前和期间,分析和比较Twitter上关于#心理健康和#抑郁症的公众情绪.
- 为了识别随着时间的推移与这些标签相关的主题流行和情绪的变化.
- 评估机器学习分类器在检测与心理健康相关的在线话语转变方面的有效性.
主要方法:
- 采集并分析了Twitter数据 (大流行前8.5个月和大流行期间8.5个月) 使用#心理健康和#抑郁症标签.
- 使用隐性迪里克莱特分配 (LDA) 用于主题建模和LIWC,VADER和NRC用于情绪分析.
- 利用三个机器学习分类器 (TF-IDF,情绪值,BERT) 来区分大流行前和期间的推文.
主要成果:
- 主题建模表明,用户主要使用#心理健康和#抑郁症标签进行宣传活动.
- 整体情绪分析显示,主要是积极的情绪,在#MentalHealth的推文中,人们经常在疫情期间表达感谢.
- 在区分疫情前和疫情期间的推特中,BERT分类器实现了最高的准确性 (81%的#心理健康,79%的#抑郁症).
结论:
- 机器学习,情绪分析和主题建模为了解公众对心理健康的情绪提供了强大的框架.
- 该研究成功地确定了COVID-19大流行期间与心理健康和抑郁症相关的在线话语的转变.
- 这些方法为公众的看法提供了有价值的见解,可以应用于各种主题和数据源进行情绪分析.
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