Deep learning for COVID-19 topic modelling via Twitter: Alpha, Delta and Omicron

被引:3
|
作者
Lande, Janhavi [1 ]
Pillay, Arti [2 ]
Chandra, Rohitash [3 ]
机构
[1] Indian Inst Technol Guwahati, Dept Phys, Gauhati, Assam, India
[2] Fiji Natl Univ, Sch Sci, Suva, Fiji
[3] UNSW Sydney, Sch Math & Stat, Transit Artificial Intelligence Res Grp, Sydney, NSW, Australia
来源
PLOS ONE | 2023年 / 18卷 / 08期
关键词
2ND WAVE;
D O I
10.1371/journal.pone.0288681
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Topic modelling with innovative deep learning methods has gained interest for a wide range of applications that includes COVID-19. It can provide, psychological, social and cultural insights for understanding human behaviour in extreme events such as the COVID-19 pandemic. In this paper, we use prominent deep learning-based language models for COVID-19 topic modelling taking into account data from the emergence (Alpha) to the Omicron variant in India. Our results show that the topics extracted for the subsequent waves had certain overlapping themes such as governance, vaccination, and pandemic management while novel issues aroused in political, social and economic situations during the COVID-19 pandemic. We also find a strong correlation between the major topics with news media prevalent during the respective time period. Hence, our framework has the potential to capture major issues arising during different phases of the COVID-19 pandemic which can be extended to other countries and regions.
引用
收藏
页数:26
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