# An Exploratory Study of COVID-19 Information on Twitter in the Greater Region

arXiv: https://arxiv.org/abs/2008.05900 · HTML: https://mlsanity.com/paper/2008.05900/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Aug 2020.

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

- [Knowledge-augmented Graph Machine Learning for Drug Discovery: A Survey](https://mlsanity.com/paper/2302.08261/) (2023)
- [BERT-Deep CNN: State-of-the-Art for Sentiment Analysis of COVID-19 Tweets](https://mlsanity.com/paper/2211.09733/) (2022)
- [A hybrid transformer and attention based recurrent neural network for robust and interpretable sentiment analysis of tweets](https://mlsanity.com/paper/2404.00297/) (2024)

## Abstract (arXiv)

The outbreak of the COVID-19 leads to a burst of information in major online social networks (OSNs). Facing this constantly changing situation, OSNs have become an essential platform for people expressing opinions and seeking up-to-the-minute information. Thus, discussions on OSNs may become a reflection of reality. This paper aims to figure out the distinctive characteristics of the Greater Region (GR) through conducting a data-driven exploratory study of Twitter COVID-19 information in the GR and related countries using machine learning and representation learning methods. We find that tweets volume and COVID-19 cases in GR and related countries are correlated, but this correlation only exists in a particular period of the pandemic. Moreover, we plot the changing of topics in each country and region from 2020-01-22 to 2020-06-05, figuring out the main differences between GR and related countries.