@conference {1323, title = {Digital Footprints of International Migration on Twitter}, booktitle = {International Symposium on Intelligent Data Analysis}, year = {2020}, publisher = {Springer}, organization = {Springer}, abstract = {Studying migration using traditional data has some limitations. To date, there have been several studies proposing innovative methodologies to measure migration stocks and flows from social big data. Nevertheless, a uniform definition of a migrant is difficult to find as it varies from one work to another depending on the purpose of the study and nature of the dataset used. In this work, a generic methodology is developed to identify migrants within the Twitter population. This describes a migrant as a person who has the current residence different from the nationality. The residence is defined as the location where a user spends most of his/her time in a certain year. The nationality is inferred from linguistic and social connections to a migrant{\textquoteright}s country of origin. This methodology is validated first with an internal gold standard dataset and second with two official statistics, and shows strong performance scores and correlation coefficients. Our method has the advantage that it can identify both immigrants and emigrants, regardless of the origin/destination countries. The new methodology can be used to study various aspects of migration, including opinions, integration, attachment, stocks and flows, motivations for migration, etc. Here, we exemplify how trending topics across and throughout different migrant communities can be observed.}, doi = {https://doi.org/10.1007/978-3-030-44584-3_22}, url = {https://link.springer.com/chapter/10.1007/978-3-030-44584-3_22}, author = {Jisu Kim and Alina Sirbu and Fosca Giannotti and Lorenzo Gabrielli} } @article {1404, title = {Human migration: the big data perspective}, journal = {International Journal of Data Science and Analytics}, year = {2020}, month = {2020/03/23}, pages = {1{\textendash}20}, abstract = {How can big data help to understand the migration phenomenon? In this paper, we try to answer this question through an analysis of various phases of migration, comparing traditional and novel data sources and models at each phase. We concentrate on three phases of migration, at each phase describing the state of the art and recent developments and ideas. The first phase includes the journey, and we study migration flows and stocks, providing examples where big data can have an impact. The second phase discusses the stay, i.e. migrant integration in the destination country. We explore various data sets and models that can be used to quantify and understand migrant integration, with the final aim of providing the basis for the construction of a novel multi-level integration index. The last phase is related to the effects of migration on the source countries and the return of migrants.}, isbn = {2364-4168}, doi = {https://doi.org/10.1007/s41060-020-00213-5}, url = {https://link.springer.com/article/10.1007\%2Fs41060-020-00213-5}, author = {Alina Sirbu and Andrienko, Gennady and Andrienko, Natalia and Boldrini, Chiara and Conti, Marco and Fosca Giannotti and Riccardo Guidotti and Bertoli, Simone and Jisu Kim and Muntean, Cristina Ioana and Luca Pappalardo and Passarella, Andrea and Dino Pedreschi and Pollacci, Laura and Francesca Pratesi and Sharma, Rajesh} }