text: Migrants' assimilation is a major challenge for European societies, in part because of the sudden surge of refugees in recent years and in part because of long-term demographic trends. In this paper, we use Facebook's data for advertisers to study the levels of assimilation of Arabic-speaking migrants in Germany, as seen through the interests they express online. Our results indicate a gradient of assimilation along demographic lines, language spoken and country of origin. Given the difficulty to collect timely migration data, in particular for traits related to cultural assimilation, the methods that we develop and the results that we provide open new lines of research that computational social scientists are well-positioned to address.
  @cite_4: Capturing human movement patterns across political borders is difficult and this difficulty highlights the need to investigate alternative data streams. With the advent of smart phones and the ability to attach accurate coordinates to Twitter messages, users leave a geographic digital footprint of their movement when posting tweets. In this study we analyzed 10 months of geo-located tweets for Kenya and were able to capture movement of people at different temporal (daily to periodic) and spatial (local, national to international) scales. We were also able to capture both long and short distances travelled, highlighting regional connections and cross-border movement between Kenya and the surrounding countries. The findings from this study has broad implications for studying movement patterns and mapping inter intra-region movement dynamics.
  @cite_9: Migration estimates are sensitive to definitions of time interval and duration. For example, when does a tourist become a migrant? As a result, harmonizing across different kinds of estimates or data sources can be difficult. Moreover in countries like the United States, that do not have a national registry system, estimates of internal migration typically rely on survey data that can require over a year from data collection to publication. In addition, each survey can ask only a limited set questions about migration (e.g., where did you live a year ago? where did you live five years ago?). We leverage a sample of geo-referenced Twitter tweets for about 62,000 users, spanning the period between 2010 and 2016, to estimate a series of US internal migration flows under varying time intervals and durations. Our findings, expressed in terms of 'migration curves', document, for the first time, the relationships between short-term mobility and long-term migration. The results open new avenues for demographic research. More specifically, future directions include the use of migration curves to produce probabilistic estimates of long-term migration from short-term (and vice versa) and to nowcast mobility rates at different levels of spatial and temporal granularity using a combination of previously published American Community Survey data and up-to-date data from a panel of Twitter users.
  @cite_1: Recently, there have been considerable efforts to use online data to investigate international migration. These efforts show that Web data are valuable for estimating migration rates and are relatively easy to obtain. However, existing studies have only investigated flows of people along migration corridors, i.e. between pairs of countries. In our work, we use data about "places lived" from millions of Google+ users in order to study migration "clusters", i.e. groups of countries in which individuals have lived. For the first time, we consider information about more than two countries people have lived in. We argue that these data are very valuable because this type of information is not available in traditional demographic sources which record country-to-country migration flows independent of each other. We show that migration clusters of country triads cannot be identified using information about bilateral flows alone. To demonstrate the additional insights that can be gained by using data about migration clusters, we first develop a model that tries to predict the prevalence of a given triad using only data about its constituent pairs. We then inspect the groups of three countries which are more or less prominent, compared to what we would expect based on bilateral flows alone. Next, we identify a set of features such as a shared language or colonial ties that explain which triple of country pairs are more or less likely to be clustered when looking at country triples. Then we select and contrast a few cases of clusters that provide some qualitative information about what our data set shows. The type of data that we use is potentially available for a number of social media services. We hope that this first study about migration clusters will stimulate the use of Web data for the development of new theories of international migration that could not be tested appropriately before.
  @cite_3: Moving to a new country can be difficult, but relationships made there can ease the integration into the new environment. The social ties can be formed with different groups: compatriots from their home country, people originally from their new country (locals), and also immigrants from other countries. Yet very little research on immigration has addressed this important aspect, primarily because large-scale studies of social networks are impractical using traditional methods such as surveys. In this study we provide the first comprehensive view into the composition of immigrants' social networks in the United States using data from the social networking site Facebook. We measure the integration of immigrant populations through the structure of friendship ties, and contrast it with the spatial density of immigrant communities. Beyond friendships with compatriots and locals, we look at friendships between immigrant groups, deriving a map of cultural friendship affinities.
  @cite_2: Data about migration flows are largely inconsistent across countries, typically outdated, and often inexistent. Despite the importance of migration as a driver of demographic change, there is limited availability of migration statistics. Generally, researchers rely on census data to indirectly estimate flows. However, little can be inferred for specific years between censuses and for recent trends. The increasing availability of geolocated data from online sources has opened up new opportunities to track recent trends in migration patterns and to improve our understanding of the relationships between internal and international migration. In this paper, we use geolocated data for about 500,000 users of the social network website "Twitter". The data are for users in OECD countries during the period May 2011- April 2013. We evaluated, for the subsample of users who have posted geolocated tweets regularly, the geographic movements within and between countries for independent periods of four months, respectively. Since Twitter users are not representative of the OECD population, we cannot infer migration rates at a single point in time. However, we proposed a difference-in-differences approach to reduce selection bias when we infer trends in out-migration rates for single countries. Our results indicate that our approach is relevant to address two longstanding questions in the migration literature. First, our methods can be used to predict turning points in migration trends, which are particularly relevant for migration forecasting. Second, geolocated Twitter data can substantially improve our understanding of the relationships between internal and international migration. Our analysis relies uniquely on publicly available data that could be potentially available in real time and that could be used to monitor migration trends. The Web Science community is well-positioned to address, in future work, a number of methodological and substantive questions that we discuss in this article.
  @cite_10: We investigate trends in the international migration of professional workers by analyzing a dataset of millions of geolocated career histories provided by LinkedIn, the largest online platform for professionals. The new dataset confirms that the United States is, in absolute terms, the top destination for international migrants. However, we observe a decrease, from 2000 to 2012, in the percentage of professional migrants, worldwide, who have the United States as their country of destination. The pattern holds for persons with Bachelor’s, Master’s, and PhD degrees alike, and for individuals with degrees from highly-ranked worldwide universities. Our analysis also reveals the growth of Asia as a major professional migration destination during the past twelve years. Although we see a decline in the share of employment-based migrants going to the United States, our results show a recent rebound in the percentage of international students who choose the United States as their destination.
  @cite_12: International migration is one of the major determinants of demographic change. Although efforts to produce comparable statistics are underway, estimates of demographic flows are inexistent, outdated, or largely inconsistent, for most countries. We estimate age and gender-specific migration rates using data extracted from a large sample of Yahoo! e-mail messages. Self-reported age and gender of anonymized e-mail users were linked to the geographic locations (mapped from IP addresses) from where users sent e-mail messages over time (2009-2011). The users' country of residence over time was inferred as the one from where most e-mail messages were sent. Our estimates of age profiles of migration are qualitatively consistent with existing administrative data sources. Selection bias generates uncertainty for estimates at one point in time, especially for developing countries. However, our approach allows us to compare in a reliable way migration trends of females and males. We document the recent increase in human mobility and we observe that female mobility has been increasing at a faster pace. Our findings suggest that e-mail data may complement existing migration data, resolve inconsistencies arising from different definitions of migration, and provide new and rich information on mobility patterns and social networks of migrants. The use of digital records for demographic research has the potential to become particularly important for developing countries, where the diffusion of Internet will be faster than the development of mature demographic registration systems.
summary: New information, like Web and social media data, are a main source of innovation in the context of migration studies. Research in this area has focused on using online data to improve estimates of migration flows and stocks. After Zagheni and Weber used geo-located Yahoo! e-mail data to estimate international migration flows @cite_12 , several platforms have been used to understand the network structure of migration, including Facebook @cite_3 and Google+ @cite_1 . Geo-located Twitter data has proved useful for studying the relationship between internal and international migration @cite_2 , as well as short-term mobility versus long-term migration @cite_9 @cite_4 . LinkedIn data has provided insights into global patterns of migration for professionals @cite_10 .