Abstract
Migration is hard to measure due to the complexity of the phenomenon and the limitations of traditional data sources. The Digital Revolution has brought opportunities in terms of new data and new methodologies for migration research. Social scientists have started to leverage data from multiple digital data sources, which have huge potential given their timeliness and wide geographic availability. Novel digital data might help in estimating migrant stocks and flows, infer intentions to migrate, and investigate the integration and cultural assimilation of migrants. Moreover, innovative methodologies can help make sense of new and diverse
streams of data. For example, Bayesian methods, natural language processing, high-intensity time series, and computational methods might be relevant to study different aspects of migration. Importantly, researchers should consider the ethical implications of using these data sources, as well as the repercussions of their results.
streams of data. For example, Bayesian methods, natural language processing, high-intensity time series, and computational methods might be relevant to study different aspects of migration. Importantly, researchers should consider the ethical implications of using these data sources, as well as the repercussions of their results.
| Original language | English |
|---|---|
| Title of host publication | Handbook of computational Social Science for policy |
| Editors | Eleonora Bertoni , Matteo Fontana , Lorenzo Gabrielli , Serena Signorelli , Michele Vespe |
| Publisher | Springer Paris |
| Chapter | 18 |
| Pages | 345-359 |
| Number of pages | 15 |
| ISBN (Electronic) | 9783031166242 |
| ISBN (Print) | 9783031166266 |
| Publication status | Published - 2023 |