mlsanityData to 7 Oct 2026

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

arXiv 2303.00135 ↗First posted 28 Feb 2023cs.LG cs.CL cs.IRcited by 4

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Abstract (arXiv)

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.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 21 of its 125 references that are in the map. First posted on arXiv 28 Feb 2023.

Builds on

21 of 125 references.

References · 21

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9 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 1; 2025: 1; 2026: 1.

4 papers.

  1. 2023An analysis of vaccine-related sentiments from development to deployment of COVID-19 vaccinescited by 5
  2. 2024A longitudinal sentiment analysis of Sinophobia during COVID-19 using large language modelscited by 1
  3. 2025HP-BERT: A framework for longitudinal study of Hinduphobia on social media via language modelscited by 1
  4. 2026An evaluation of LLMs for political bias in Western media: Israel-Hamas and Ukraine-Russia warscited by 1