The Perceptions of Migration During the Pandemic: What Twitter Data Tell Us?

Abstract

This paper aims to analyse sentiments and emotions about migration in Italy using Twitter, by comparing the period of COVID-19 pandemic with the previous year. We take Italy as a case study because it has been severely affected by the COVID-19, it is one of the largest recipients of immigrants in Europe and, is among the few countries that implemented an amnesty for irregular migrant workers during the pandemic. We apply a text mining and sentiment analysis to the tweets with hashtags and keywords related to the migration and to the COVID-19 pandemic. Results show that tweets related to migration express a sense of emergency and also invasion. No major changes occurred in the period of the pandemic in comparison with the previous period. Indeed, both negative and positive sentiments are present in the tweets in both periods, confirming a certain polarization in the public discourse about migration.