Carolina Coimbra Vieira

Gathering information about where to migrate to is an important component of the migration process, particularly in the context of forced migration, when individuals must make rapid decisions under conditions of uncertainty. In this study, we investigate the relationship between forced migration and online information-seeking behavior on Wikipedia. Focusing on the 2022 Russian invasion of Ukraine, we analyze how the resulting refugee crisis, which led to over six million Ukrainians fleeing to countries across Europe, affected information-seeking behavior on Wikipedia, as measured by views of articles about European cities. We compared changes in the number of views of Ukrainian-language Wikipedia articles, used as a proxy for information-seeking by Ukrainians, with those in four other language editions of Wikipedia. Our results show that views of Ukrainian-language Wikipedia articles about European cities are more strongly correlated with the number of Ukrainian refugees who applied for temporary protection in European countries than views of articles in other languages. As Poland and Germany emerged as the main destinations of Ukrainian refugees, we investigated further and found that the number of applications for temporary protection in Polish and German cities is also more strongly correlated with views of their respective Wikipedia articles in Ukrainian. Finally, we analyzed the temporal relationship between refugee flows to Poland and information-seeking on Wikipedia. Our findings reveal that refugee border crossings preceded the increase in views of Ukrainian-language Wikipedia articles about Polish cities, suggesting that information-seeking surged after displacement. This highlights the reactive nature of information-seeking behavior during forced migration, in contrast to the pre-departure planning behavior that is characteristic of regular labor migration. However, while official applications for protection often lagged behind border crossings by at least a couple of weeks, Wikipedia activity increased almost immediately after border crossings. Thus, information-seeking on Wikipedia can offer a near real-time indicator of emerging migration patterns during crises.

Measuring the affinity to a particular culture has been an active area of research. Countries and their residents can be characterized by many cultural aspects, such as clothing, music, art and food. As one of the central aspects, the cuisine of a country can reflect one of the dominant aspects of its culture. As such, the number of people interested in a typical national dish can be used to estimate the prevalence of that culture inside the host region. In this study, we measure the global spread of Brazilian culture across countries by exploring Facebook user’s preferences for typical Brazilian dishes through the Facebook Advertising Platform. To decide which dish will be considered typical from Brazil, we made use of spatial analysis to understand the distribution of interests around the world and to quantify how typical the dish is in Brazil and among Brazilian immigrants. This methodology can be generalized to other countries to infer cultural elements that emigrants usually take to and preserve in the countries they migrate to. Also, the interest in Brazilian typical dishes can be used to characterize countries in terms of Brazilian cultural exposition. While evaluating the cultural distance between Brazil and the countries with more Brazilian immigrants, we explore several measures of distance to compare these in the context of affinity to Brazilian cuisine. Our results revealed that these cultural distance measures can complement other metrics of distance applied to gravity-type models, for example, in order to explain flows of people between countries.

Migration has been proposed as one of the factors that shape cultural similarities across countries. However, studying the relationship between culture and migration has been challenging, in part because culture is difficult to quantify. The traditionally used survey questionnaires have a number of drawbacks, including that they are costly and difficult to scale to a large number of countries. To complement survey data, we propose the use of passively-collected digital traces from social media. We focus on food and drink as markers of a country’s culture. We then measure similarities between countries in terms of food and drink interests using aggregated data from the Facebook Advertising Platform. Methodologically, we offer approaches to measure the similarity between countries with both symmetric and asymmetric indices. Substantively, we assess the association between migration cultural similarity between countries by comparing our measure of cultural similarity with international migration data. In most countries, larger immigrant populations are associated with more similar food and drink preferences between their country of origin and the country of destination. Our results suggest that immigrants contribute to bringing the culture of their home countries to new countries. Moreover, our study identifies considerable variability in this pattern across countries.

Abstract One of the strongest empirical regularities in spatial demography is that flows of migrants are positively associated with population stocks at origin and destination and are inversely related to distance. This pattern was formalized into what are known as gravity models of migration. Traditionally, distance is measured geographically, but other measures of distance, such as cultural distance, are also relevant in explaining migration flows. However, measures of cultural distance are not widely adopted in the literature on modeling migration flows, partially because of the difficulties associated with operationalizing and producing these measures across space and time. In this paper, we use a scalable approach to obtain proxies for measuring cultural similarity between countries by using Facebook data and illustrate the impact of incorporating these measures, based on food and drink interests, into gravity models for predicting migration. Our results show that, despite their limitations, the new measures of cultural similarity derived from Facebook data improve the prediction power of traditional gravity models and have a predictive capacity comparable to that of classic variables used in the literature, such as shared language and history. The results open up new opportunities for understanding the determinants of migration and for predicting migration when considering broader and complementary perspectives on the meaning and measurement of distance.