在COVID-19期间跟踪16个国家的集体情绪:使用Twitter识别主要情绪事件的新方法
Apurv Chauhan1,2, Vivek Belhekar3, Surbhi Sehgal4
1Institute of Psychiatry, Psychology & Neuroscience (IoPPN), King's College London, London, United Kingdom.
Frontiers in psychology
|April 9, 2024
概括
这项研究估计了在COVID-19大流行期间使用Twitter数据的集体情绪 (CE). 它发现,在16个国家,主要的流行病宣布减少了焦虑,增加了积极的情绪.
科学领域:
- 计算社会科学 计算社会科学
- 心理学 心理学 心理学
- 社会学 社会学 社会学
背景情况:
- 集体情绪 (CEs) 对于理解社会对全球事件的反应至关重要.
- 实时估计CE带来了方法上的挑战.
- 随着COVID-19的流行,提供了一个独特的,大规模的事件来研究CE.
研究的目的:
- 利用社交媒体数据开发和验证一种用于估计国家一级集体情绪的新方法.
- 在早期的COVID-19大流行期间识别重要的社会层面的情感事件.
- 分析大流行相关公告对集体焦虑和积极情绪的影响.
主要方法:
- 在COVID-19流行病的最初120天中,利用了来自16个国家的1000多万条Twitter消息.
- 采用时间序列分析和语言调查和词汇计数 (LIWC) 词典来估计日常集体焦虑和积极情绪.
- 分析了结构性断裂的时间序列数据,表明由于外部冲击而导致集体情绪的重大转变.
主要成果:
- 在世卫组织宣布COVID-19为全球大流行后,所有16个国家都观察到集体焦虑的显著减少和积极情绪的增加.
- 经济支持和社会限制的公告也与一些国家的情绪转变有关.
- 研究结果表明,减少危机期间的不确定性会对集体情绪状态产生积极影响.
结论:
- 这项研究成功地展示了一种使用Twitter数据估计集体情绪的新方法.
- 这项研究强调了COVID-19大流行等重大全球事件对社会情感景观的深刻影响.
- 这种方法为集体心理现象和危机应对提供了宝贵的见解.
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