Measuring Shifts in Attitudes Towards COVID-19 Measures in Belgium Using Multilingual BERT
This work addresses public opinion monitoring during the pandemic for policymakers and researchers, but it is incremental as it applies an existing method to new data.
The researchers analyzed Belgian COVID-19-related Tweets over seven months using multilingual BERT to classify opinions on government curfew measures and track changes in topics and views in relation to events like new measure implementations, finding shifts in public attitudes.
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.