This paper aims to illustrate how attitudes towards immigration can be measured using Twitter data and natural processing language.
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.