![]() Given this work and excitement about the potential of this data it is surprising, that there seems little in terms of research that directly evaluates whether or not these methods work in the sense that they actually find migrants. Righi provides a comprehensive summary of work to date, referencing some 50 works in the area. ![]() There is now a substantial body of work on this topic and a number methods have been proposed for detecting migrants and migration flows from digital traces. Beyond studying population movements, some of these data might also provide new ways to study post-migration processes like assimilation and cultural change or even track online hate speech against immigrants. Given these challenges, 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. In the global south, where in fact a large majority of the migration occurs, the situation in terms of data availability, accuracy, and access is worse still. Although 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. Similarly foreign workers, often with precarious legal status, make up a much larger share of the population in some of the Gulf states where in some cases more than three-quarters of the population is foreign born.īeing by definition a mobile population, and, in most countries, constituting a relatively small share of the population, migrants are a hard demographic to study. Footnote 1 In most of these countries immigration will be an important aspect of population development for the foreseeable future. At the same time, in many developed countries of the global north a non-trivial share of the population is foreign-born ranging from about 10% in Europe, 15% in the US to 20% in Canada and almost 30% in Australia. We suspect that increasing the correct classification rate substantially will not be easy and may introduce other biases.įor all the attention it attracts, international migration is a minority phenomenon 97% of the world’s population lives in their country of birth. For most research trying to use these data to study migrant populations, the data will be of limited utility. 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. 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”. Upon close inspection very few of the accounts that are classified as migrants appear to be migrants in any conventional sense or international students. In a second step we hand-code the entire tweet histories of a subset of the accounts identified as migrants by these methods. ![]() ![]() 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. We assess strategies used in previous work to identify migrants based on their geolocation histories. In this paper we assess the reliability of one such data source that is heavily used within the research community: geolocated tweets. 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. ![]()
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