Large-scale coordinated efforts have been dedicated to understanding the global health and economic implications of the COVID-19 pandemic. Yet, the rapid spread of discrimination and xenophobia against specific populations has largely been neglected. Understanding public attitudes toward migration is essential to counter discrimination against immigrants and promote social cohesion. Traditional data sources to monitor public opinion are often limited, notably due to slow collection and release activities. New forms of data, particularly from social media, can help overcome these limitations. While some bias exists, social media data are produced at an unprecedented temporal frequency, geographical granularity, are collected globally and accessible in real-time. Drawing on a data set of 30.39 million tweets and natural language processing, this article aims to measure shifts in public sentiment opinion about migration during early stages of the COVID-19 pandemic in Germany, Italy, Spain, the United Kingdom, and the United States. Results show an increase of migration-related Tweets along with COVID-19 cases during national lockdowns in all five countries. Yet, we found no evidence of a significant increase in anti-immigration sentiment, as rises in the volume of negative messages are offset by comparable increases in positive messages. Additionally, we presented evidence of growing social polarization concerning migration, showing high concentrations of strongly positive and strongly negative sentiments.

In 2020, the world faced an unprecedented challenge to tackle and understand the spread and impacts of COVID-19. Large-scale coordinated efforts have been dedicated to understand the global health and economic implicationsof the pandemic. Yet, the rapid spread of discrimination and xenophobia against specific populations, particularlymigrants and individuals of Asian descent, has largely been neglected. Understanding public attitudes towardsmigration is essential to counter discrimination against immigrants and promote social cohesion. Traditional datasources to monitor public opinion – ethnographies, interviews, and surveys – are often limited due to smallsamples, high cost, low temporal frequency, slow collection, release and coarse spatial resolution. New forms ofdata, particularly from social media, can help overcome these limitations. While some bias exists, social mediadata are produced at an unprecedented temporal frequency, geographical granularity, are collected globally andaccessible in real-time. Drawing on a data set of 30.39 million tweets and natural language processing, this paperaims to measure shifts in public sentiment opinion about migration during early stages of the COVID-19 pandemicin Germany, Italy, Spain, the United Kingdom and the United States. Results show an increase of migration-relatedTweets along with COVID-19 cases during national lockdowns in all five countries. Yet, we found no evidence ofa significant increase in anti-immigration sentiment, as rises in the volume of negative messages are offset bycomparable increases in positive messages. Additionally, we presented evidence of growing social polarisationconcerning migration, showing high concentrations of strongly positive and strongly negative sentiments.

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