# A Twitter narrative of the COVID-19 pandemic in Australia

arXiv: https://arxiv.org/abs/2302.11136 · HTML: https://mlsanity.com/paper/2302.11136/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Feb 2023.

## Built on by (most cited first, 5 of 5)

- [Sentiment Analysis and Text Analysis of the Public Discourse on Twitter about COVID-19 and MPox](https://mlsanity.com/paper/2312.10580/) (2023)
- [CrisisTransformers: Pre-trained language models and sentence encoders for crisis-related social media texts](https://mlsanity.com/paper/2309.05494/) (2023)
- [CReMa: Crisis Response through Computational Identification and Matching of Cross-Lingual Requests and Offers Shared on Social Media](https://mlsanity.com/paper/2405.11897/) (2024)
- [Semantically Enriched Cross-Lingual Sentence Embeddings for Crisis-related Social Media Texts](https://mlsanity.com/paper/2403.16614/) (2024)
- ["Actionable Help" in Crises: A Novel Dataset and Resource-Efficient Models for Identifying Request and Offer Social Media Posts](https://mlsanity.com/paper/2502.16839/) (2025)

## Abstract (arXiv)

Social media platforms contain abundant data that can provide comprehensive knowledge of historical and real-time events. During crisis events, the use of social media peaks, as people discuss what they have seen, heard, or felt. Previous studies confirm the usefulness of such socially generated discussions for the public, first responders, and decision-makers to gain a better understanding of events as they unfold at the ground level. This study performs an extensive analysis of COVID-19-related Twitter discussions generated in Australia between January 2020, and October 2022. We explore the Australian Twitterverse by employing state-of-the-art approaches from both supervised and unsupervised domains to perform network analysis, topic modeling, sentiment analysis, and causality analysis. As the presented results provide a comprehensive understanding of the Australian Twitterverse during the COVID-19 pandemic, this study aims to explore the discussion dynamics to aid the development of future automated information systems for epidemic/pandemic management.