# Measuring Shifts in Attitudes Towards COVID-19 Measures in Belgium Using Multilingual BERT

arXiv: https://arxiv.org/abs/2104.09947 · HTML: https://mlsanity.com/paper/2104.09947/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 15 references that are in the map.
- First posted on arXiv 20 Apr 2021.

## Builds on (most cited first, 2 of 2)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Show Your Work: Improved Reporting of Experimental Results](https://mlsanity.com/paper/1909.03004/) (2019)

## Built on by (most cited first, 2 of 2)

- [RobBERT-2022: Updating a Dutch Language Model to Account for Evolving Language Use](https://mlsanity.com/paper/2211.08192/) (2022)
- [OneLove beyond the field -- A few-shot pipeline for topic and sentiment analysis during the FIFA World Cup in Qatar](https://mlsanity.com/paper/2408.02520/) (2024)

## Abstract (arXiv)

We classify seven months' worth of Belgian COVID-related Tweets using multilingual BERT and relate them to their governments' COVID measures. We classify Tweets by their stated opinion on Belgian government curfew measures (too strict, ok, too loose). We examine the change in topics discussed and views expressed over time and in reference to dates of related events such as implementation of new measures or COVID-19 related announcements in the media.