Topic modelling with innovative deep learning methods has gained interest for a wide range of applications that includes COVID-19. Topic modelling 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 emergence (Alpha) to the Omicron variant. We apply topic modeling to review the public behaviour across the first, second and third waves based on Twitter dataset from India. Our results show that the topics extracted for the subsequent waves had certain overlapping themes such as covers governance, vaccination, and pandemic management while novel issues aroused in political, social and economic situation during COVID-19 pandemic. We also found a strong correlation of the major topics qualitatively to news media prevalent at 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.

翻译：利用创新深度学习方法进行主题建模，已在包括COVID-19在内的广泛应用中引起关注。主题建模能够为理解人类在COVID-19大流行等极端事件中的行为提供心理学、社会学和文化层面的洞察。本文采用基于深度学习的先进语言模型，针对从疫情暴发（阿尔法变体）到奥密克戎变体期间的数据进行COVID-19主题建模。我们基于印度推特数据集，运用主题建模方法回顾了第一、二、三波疫情中的公众行为。研究结果表明，后续疫情波次提取的主题存在若干重叠议题，如治理、疫苗接种和疫情管理，而COVID-19大流行期间的政治、社会和经济形势也催生了新兴议题。此外，我们定量发现主要主题与相应时期的主流新闻媒体存在显著相关性。因此，本框架具备识别COVID-19大流行不同阶段重大议题的潜力，并可推广至其他国家和地区。