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检测由于澳大利亚COVID-19流行病的社区抑郁动态
Jianlong Zhou1, Hamad Zogan2, Shuiqiao Yang1
1Data Science InstituteUniversity of Technology Sydney Ultimo NSW 2007 Australia.
随着COVID-19大流行,抑郁症水平显著增加,尤其是在政府封锁之后. 这项研究分析了Twitter数据,以了解危机期间社区心理健康的动态.
科学领域:
- 公共卫生 公共卫生
- 计算社会科学 计算社会科学
- 心理健康研究 心理健康研究
背景情况:
- 随着COVID-19大流行,全球心理健康问题增加,抑郁症成为一个主要的公共卫生问题.
- 抑郁症带来了重大的个人和社会成本,影响了情绪,行为和身体健康.
- 了解抑郁动态对于应对由大流行加剧的心理健康危机至关重要.
研究的目的:
- 通过使用Twitter数据,调查由COVID-19大流行影响的社区抑郁动态.
- 开发和应用一种新的分类模型来识别社交媒体内容中的抑郁症线索.
- 分析COVID-19和相关事件,包括政府措施对抑郁水平的影响.
主要方法:
- 利用来自Twitter的用户生成内容,特别关注澳大利亚新南威尔士州的用户.
- 开发了一种新的方法,将多式特征 (情绪,主题,特定领域) 与术语频率-反向文档频率 (TF-IDF) 结合起来.
- 构建了抑郁症分类模型,从与COVID-19时期相关的推文中提取抑郁症极性.
主要成果:
- 该研究发现,在COVID-19大流行开始后,Twitter用户的抑郁水平显著增加.
- 政府实施的措施,如州封锁,与高度的抑郁症水平相关.
- 拟议的模型有效地捕获了大流行期间的抑郁症线索及其波动.
结论:
- COVID-19 流行病对社区心理健康产生了明显的负面影响,特别是增加了抑郁症.
- 社交媒体分析为健康危机期间的公共心理健康趋势提供了宝贵的见解.
- 疫情期间的政策干预需要仔细考虑其潜在的心理健康后果.
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