通过Twitter进行COVID-19主题建模的深度学习:Alpha,Delta和Omicron
Janhavi Lande1, Arti Pillay2, Rohitash Chandra3
1Department of Physics, Indian Institute of Technology Guwahati, Guwahati, Assam, India.
PloS one
|August 1, 2023
概括
深度学习主题建模揭示了印度不断演变的COVID-19主题,从Alpha到Omicron波. 调查结果与新闻媒体相关,为流行病管理和社会影响提供了见解.
科学领域:
- 计算语言学计算语言学
- 公共卫生信息学 公共卫生信息学
- 社会科学 社会科学 社会科学
背景情况:
- 深度学习方法为主题建模提供了新的方法.
- 了解人类在流行病等极端事件期间的行为至关重要.
- COVID-19 (冠状病毒疾病2019) 对心理,社会和文化产生了深远的影响.
研究的目的:
- 应用先进的深度学习语言模型用于印度COVID-19相关数据的主题建模.
- 分析不同流行病浪潮中关键主题的演变,从阿尔法变种到Omicron变种.
- 在特定时期调查确定主题和流行的新闻媒体之间的相关性.
主要方法:
- 利用基于深度学习的突出的语言模型进行主题建模.
- 分析了一组数据集,涵盖了印度COVID-19大流行病的进展.
- 检查的主题转移和连续性跨不同的变体波.
主要成果:
- 识别了重叠的主题,如治理,疫苗接种和跨COVID-19波的流行病管理.
- 在疫情期间发现了政治,社会和经济领域新出现的新问题.
- 发现主要提取主题和当代新闻媒体报道之间存在强烈的相关性.
结论:
- 深度学习框架有效地捕捉了COVID-19流行病不同阶段的重要问题.
- 这种方法有可能扩展到分析其他国家和地区的公共话语.
- 主题建模为全球卫生危机期间的社会反应和挑战提供了宝贵的见解.
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