mlsanityData to 7 Oct 2026

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

arXiv 2104.09947 ↗First posted 20 Apr 2021cs.CL cs.SIcited by 2

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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.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 2 of its 15 references that are in the map. First posted on arXiv 20 Apr 2021.

Builds on

2 of 15 references.

References · 2

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2019Show Your Work: Improved Reporting of Experimental Resultscited by 164

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Citing papers per year: 2022: 1; 2023: 0; 2024: 1.

2 papers.

  1. 2022RobBERT-2022: Updating a Dutch Language Model to Account for Evolving Language Usecited by 2
  2. 2024OneLove beyond the field -- A few-shot pipeline for topic and sentiment analysis during the FIFA World Cup in Qatarcited by 1