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

TL;DR

This study employs deep learning-based language models to analyze Twitter data from India, revealing evolving public concerns and themes across COVID-19 waves from Alpha to Omicron, with implications for understanding societal responses.

Contribution

It introduces a deep learning framework for COVID-19 topic modeling on social media data across different pandemic waves, capturing both common and emerging themes.

Findings

Overlapping themes in governance, vaccination, and pandemic management across waves

Novel issues related to political, social, and economic aspects emerged during the pandemic

Major topics correlated with contemporaneous news media coverage

Abstract

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.