Immigrant community integration in world cities

Abstract

As a consequence of the accelerated globalization process, today major cities all over the world are characterized by an increasing multiculturalism. The integration of immigrant communities may be affected by social polarization and spatial segregation. How are these dynamics evolving over time? To what extent the different policies launched to tackle these problems are working? These are critical questions traditionally addressed by studies based on surveys and census data. Such sources are safe to avoid spurious biases, but the data collection becomes an intensive and rather expensive work. Here, we conduct a comprehensive study on immigrant integration in 53 world cities by introducing an innovative approach: an analysis of the spatio-temporal communication patterns of immigrant and local communities based on language detection in Twitter and on novel metrics of spatial integration. We quantify the Power of Integration of cities –their capacity to spatially integrate diverse cultures– and characterize the relations between different cultures when acting as hosts or immigrants.

Demographic Inference and Representative Population Estimates from Multilingual Social Media Data

“…The selection options regarding gender also included a category for profiles belonging to multiple persons, and to a non-person (e.g., organization/bot). Regarding age, annotators were given seven categories to choose from: <= 18, (18,30), [30,40), [40,50), [50,60), [60,70), >= 69. They could select "don't know" for all questions, if they felt not enough information was available.…”

“…Social media data has been proposed as a real-time and inexpensive way to measure social phenomenathis area of research is known as "social sensing." Social media data has been used, with various levels of success, in areas such as infectious disease [30,31,51], migration and tourism [7,50,81], and box office takings for films [21,55].…”

Twitter trolls: a linguistic profile of anti-democratic discourse

“…The baseline dataset NTS-HGT contains the English-language tweets sent during 2017-2018 from within the Nordic region, a total of 4,360,261 messages. With regard to whether the data represent English use in the region and not messages from tourists and visitors, previous studies that use Twitter data have suggested that a great majority of messages in one location such as a single country are in fact from residents of that location (Lamanna et al 2018). We therefore assume that our baseline dataset is reliable, given the general limitations of Twitter data in general, while certain demographic groups, such as younger generations (male in particular) and public figures, may well be over-represented.…”

Fact-checking strategies to limit urban legends spreading in a segregated society

“…However, many attributes or factors that lead to the formation of segregated communities are somehow 'mutable': for example, nodes that join or leave the network can contribute to create new shortest paths to otherwise distant communities, interests change during time and, as a consequence, attention to given topics. On the other hand, segregation has been largely studied (Oka and Wong 2015;Massey and Denton 1993;1987) and observed (Bajardi et al 2015;Herdagdelen et al 2016;Lamanna et al 2018) in urban environments, involving features of human life as language, religion, ethnicity, education, employment and so on. Many of these attributes are 'immutable' , and the topology of the network can be shaped accordingly.…”