TY - CONF T1 - Digital Footprints of International Migration on Twitter T2 - International Symposium on Intelligent Data Analysis Y1 - 2020 A1 - Jisu Kim A1 - Alina Sirbu A1 - Fosca Giannotti A1 - Lorenzo Gabrielli AB - 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’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. JF - International Symposium on Intelligent Data Analysis PB - Springer UR - https://link.springer.com/chapter/10.1007/978-3-030-44584-3_22 ER -