This paper aims to illustrate how attitudes towards immigration can be measured using Twitter data and natural processing language.
Back in February, the European Commission Joint Research Centre published a report on data innovation applications in demography, migration and human mobility. This report is a very welcome contribution summarising all the relevant case applications in the field of migration and human mobility.
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
This report introduces a novel framework for using Twitter data to measure and monitor shifts in public sentiment towards migrants, complementing traditional data sources.
We use a Twitter sample composed of 36 K users and 160 K tweets discussing the topic in 2017, when the immigrant population in the country recorded an increase by a factor of four from 2010.
We utilise a novel dataset of all COVID-19-related social media posts on Twitter from the UK 48 hours before and 48 hours after the announcement (n=2,531,888).