Using Twitter to Track Immigration Sentiment During Early Stages of the COVID-19 Pandemic

Francisco Rowe, Michael Mahony, Eduardo Graells-Garrido, Marzia Rango and Niklas Sievers
Additional contact information
Francisco Rowe: University of Liverpool

No pc3za, SocArXiv from Center for Open Science

Abstract: 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 implications of the pandemic. Yet, the rapid spread of discrimination and xenophobia against specific populations, particularly migrants and individuals of Asian descent, has largely been neglected. Understanding public attitudes towards migration is essential to counter discrimination against immigrants and promote social cohesion. Traditional data sources to monitor public opinion – ethnographies, interviews, and surveys – are often limited due to small samples, high cost, low temporal frequency, slow collection, release and coarse spatial resolution. 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 paper 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 polarisation concerning migration, showing high concentrations of strongly positive and strongly negative sentiments.

Date: 2021-07-25
New Economics Papers: this item is included in nep-big, nep-hea and nep-mig
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

Downloads: (external link)
https://osf.io/download/60fb3b410c4cba0029bc94ba/

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:osf:socarx:pc3za

DOI: 10.31219/osf.io/pc3za

Access Statistics for this paper

More papers in SocArXiv from Center for Open Science
Bibliographic data for series maintained by OSF ().