Arbeitsbereich

Migration und Mobilität

Auf einen Blick Projekte Publikationen Team

Projekt

Evaluating Immigrants’ Cultural Assimilation by Using Digital Trace Data

Jisu Kim, André Grow-Böser, Asli Ebru Sanlitürk, Emilio Zagheni; in Zusammenarbeit mit René Flores D. (University of Chicago, Vereinigte Staaten), Kiran Garimella (Rutgers, The State University of New Jersey, New Brunswick, Vereinigte Staaten), Ingmar Weber (Saarland University, Saarbrücken, Deutschland), Ilana Ventura (University of Chicago, Vereinigte Staaten)

Ausführliche Beschreibung

Immigrants are expected to assimilate gradually into the host society over generations, and various measures have been developed to assess their level of assimilation, such as indicators of economic success, education, language proficiency, and political engagement. However, measuring cultural assimilation has been challenging due to the lack of suitable data and indicators.

Are immigrants assimilating to the cultural tastes of the native population? And how do different subpopulations of immigrants assimilate? According to the "straight line" assimilation hypothesis, the more time immigrants spend in the host society, the more culturally similar to natives they become. European immigrants to the United States historically assimilated and became culturally indistinguishable from Anglo-American natives by the third generation. However, the assimilation patterns of Latin American and Asian immigrants' who entered the US after 1965 appear to be "segmented" rather than "linear." While advantaged immigrants are still assimilating "linearly," disadvantaged groups tend to adopt the cultural norms and styles of native minorities, such as African-Americans, due to their concentration in poor neighborhoods.

In this project, we aim to leverage digital trace data in order to provide insight into immigrants' interests and preferences and to complement existing sources. To study cultural assimilation, we focus on users present in various digital spaces such as Facebook, Twitter (or currently X), and YouTube. We examine their interests as revealed online, distinguishing between different demographic groups, including between different countries of previous residence.

As a first step toward assessing cultural assimilation with digital trace data, we proposed a new method to evaluate assimilation processes using Facebook data (Dubois et al. 2018). Applying the proposed metric, we found that Turkish immigrants in Germany show relatively high levels of assimilation, with some subgroups assimilating more than others (e.g., men more than women). In Stewart et al. (2019), we further expanded on this work, using the same metric to evaluate the musical taste of Mexico-US immigrants. We found that Mexican immigrants tend to assimilate more toward African Americans than toward non-Hispanic Whites in the US.

In a related study, we proposed a new approach to measuring two aspects of cultural integration using Twitter data (Kim et al. 2022). These aspects are the origin attachment index, which represents the preservation of links to the origin country and culture, and the destination attachment index, which indicates the creation of new links and the adoption of cultural traits of the new country of residence. Using these indexes, we analyzed how immigrants from different countries integrate into host countries, and found that having a common language and having low proficiency in the host country’s language promote attachment to the country of origin.

The next phase of the project extends our approaches by systematically testing observed patterns of cultural assimilation across multiple dimensions. Specifically, we employ a range of proxy measures for culture – such as media consumption, language use, and online engagement – to assess how assimilation manifests in different domains, and how consistently these patterns hold across platforms and populations.

Bewertung der kulturellen Integration anhand von Daten aus sozialen Medien

Verhältnis zwischen Herkunfts- und Zielanbindungsindizes für alle Migrant*innen in den Daten, hochgerechnet auf vier Integrationstypen. Hier sind verschiedene Muster für einzelne Nutzer*innen zu beobachten, die zu unterschiedlichen Assimilationstypen führen. Die roten Linien geben einen ungefähren Hinweis auf den Assimilationstyp der Nutzer*innen. Der Winkel jedes Individuums zur x/y-Achse gibt uns zusammen mit der Entfernung vom Ursprung einen Hinweis auf den Assimilationstyp. Ein*e Migrant*in nahe der x-Achse durchläuft höchstwahrscheinlich einen Assimilationsprozess, ein*e Migrant*in nahe der y-Achse durchläuft einen Separationsprozess, während diejenigen, die dazwischen liegen, eine Integration oder Globalisierung durchlaufen. © Kim Jisu, et al. "Origin and destination attachment: study of cultural integration on Twitter." EPJ Data Science 11.1 (2022): 55.

Publikationen

Gil-Clavel, B. S.; Grow, A.; Bijlsma, M. J.:

Analyzing EU-15 immigrants’ language acquisition using Twitter data

MPIDR Working Paper WP-2022-012. (2022)    

Kim, J.; Sîrbu, A.; Giannotti, F.; Rossetti, G.; Rapoport, H.:

Origin and destination attachment: study of cultural integration on Twitter

EPJ Data Science 11:55, 1–20. (2022)    

Alburez-Gutierrez, D.; Aref, S.; Gil-Clavel, B. S.; Grow, A.; Negraia, D. V.; Zagheni, E.:

Demography in the digital era: new data sources for population research

In: Smart statistics for smart applications : book of short papers SIS2019, 23–30. Pearson. (2019)    

Stewart, I. B.; Flores, R. D.; Riffe, T.; Weber, I. G.; Zagheni, E.:

Rock, rap, or reggaeton?: assessing Mexican immigrants' cultural assimilation using Facebook data

In: Proceedings of the World Wide Web Conference WWW 2019: May 13-17, 2019, San Francisco, CA, USA, 3258–3264. New York: ACM. (2019)