Online Seminar Talk
EU-15 Immigrants Language Acquisition on Twitter
Laboratory of Digital and Computational Demography, February 08, 2022
Sofia Gil-Clavel from the Laboratory of Digital and Computational Demography at MPIDR and the University of Groningen showed that countries with high requirements for language acquisition are the ones where immigrants have the slowest pace of language acquisition.
The increasingly complex and heterogeneous immigrant-communities settling in Europe have led European countries to adopt civic-integration measures. Among these measures, language is regarded as a critical factor inducing integration and cooperation between immigrants and natives. Simultaneously, the rapid expansion of the use of online social networks is believed to change the factors that affect immigrants’ language acquisition. This article uses data from Twitter, a novel longitudinal-data source, to: (1) analyze differences in immigrants’ language-acquisition pace between different civic-integration regimes; and (2) study how the relative size of migrant groups in the destination country and the linguistic and geographical distance between countries of origin and destination are associated with language acquisition within those regimes. We focus on immigrants whose destination countries were in the EU-15 between 2012 and 2016. We study the time until a user mostly tweets in the language of the destination country for one month as a proxy of language acquisition, and we consider destination-country civic-integration measures as the primary exposure for the risk of acquiring the language. Results show that, first, countries with high requirements for language acquisition are the ones where immigrants have the slowest pace of language acquisition. Second, despite the novelty of social network sites and their potential impact on the way people acquire languages, language acquisition is still associated with classic explanatory variables, such as immigrant group size in the destination country, linguistic-distance between origin- and destination-language, and geographical distance between origin- and destination-country.
Soﬁa Gil-Clavel is a PhD student at the Max Planck Institute for Demographic Research & University of Groningen. In her work, Soﬁa uses data from Facebook and Twitter to study older people’s usage of communication technologies and migrants’ cultural integration, respectively. She combines techniques from computer science (e.g. machine learning and metaheuristics) with statistical methods (generalized linear models, survival analysis, bootstrapping). Previously she studied actuarial science at the National Autonomous University of Mexico (UNAM) and has a MSc in Computer Science from the Mathematical Research Center of Mexico (CIMAT). After her MSc, she joined the Mexican National Population Council (CONAPO) as head of the Department of Demographic Studies.