Abstract
Given the challenges in collecting up-to-date, comparable data on migrant populations the potential of digital trace data to study migration and migrants has sparked considerable interest among researchers and policy makers. In this paper we assess the reliability of one such data source that is heavily used within the research community: geolocated tweets. We assess strategies used in previous work to identify migrants based on their geolocation histories. We apply these approaches to infer the travel history of a set of Twitter users who regularly posted geolocated tweets between July 2012 and June 2015. In a second step we hand-code the entire tweet histories of a subset of the accounts identified as migrants by these methods. Upon close inspection very few of the accounts that are classified as migrants appear to be migrants in any conventional sense or international students. Rather we find these approaches identify other highly mobile populations such as frequent business or leisure travellers, or people who might best be described as “transnationals”. For demographic research that draws on this kind of data to generate estimates of migration flows this high mis-classification rate implies that findings are likely sensitive to the adjustment model used. For most research trying to use these data to study migrant populations, the data will be of limited utility. We suspect that increasing the correct classification rate substantially will not be easy and may introduce other biases.
Highlights
Given the challenges in collecting up-to-date, comparable data on migrant populations the potential of digital trace data to study migration and migrants has sparked considerable interest among researchers and policy makers
Developed states generally have the capacity to collect statistics on their migrant populations, the definition of who is a migrant vary across countries making comparisons difficult, and even in the best of cases there is a substantial lag between migration flows and the availability of migration statistics which makes it especially hard to study and track the sudden population movements that occur in response to political crises
Approaches that draw on digital trace data have sparked the interest of social scientists, governments, and international organizations alike promising to deliver timely estimates of migration flows measured in a consistent way across countries [2, 3]
Summary
Given the challenges in collecting up-to-date, comparable data on migrant populations the potential of digital trace data to study migration and migrants has sparked considerable interest among researchers and policy makers. If these data are to form the base for research on migrants or postmigration processes these approaches have liabilities as they include features that are likely to change with the migration and integration process to identify the study population.
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