01861nas a2200145   4500008004100000245006100041210006100102260001300163520137000176100001401546700001701560700002101577700002301598856009401621       2020                        eng d00aDigital Footprints of International Migration on Twitter0 aDigital Footprints of International Migration on Twitter  bSpringer3 aStudying 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’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.1 aKim, Jisu1 aSirbu, Alina1 aGiannotti, Fosca1 aGabrielli, Lorenzo  uhttps://datasciencephd.it/publications/digital-footprints-international-migration-twitter