# Large-scale, Language-agnostic Discourse Classification of Tweets During COVID-19

arXiv: https://arxiv.org/abs/2008.00461 · HTML: https://mlsanity.com/paper/2008.00461/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Aug 2020.

## Built on by (most cited first, 3 of 3)

- [Language Scaling for Universal Suggested Replies Model](https://mlsanity.com/paper/2106.02232/) (2021)
- [BERT-Deep CNN: State-of-the-Art for Sentiment Analysis of COVID-19 Tweets](https://mlsanity.com/paper/2211.09733/) (2022)
- [A Comparative Study on Transfer Learning and Distance Metrics in Semantic Clustering over the COVID-19 Tweets](https://mlsanity.com/paper/2111.08658/) (2021)

## Abstract (arXiv)

Quantifying the characteristics of public attention is an essential prerequisite for appropriate crisis management during severe events such as pandemics. For this purpose, we propose language-agnostic tweet representations to perform large-scale Twitter discourse classification with machine learning. Our analysis on more than 26 million COVID-19 tweets shows that large-scale surveillance of public discourse is feasible with computationally lightweight classifiers by out-of-the-box utilization of these representations.