Tracing origins: Comparing a heuristic, machine learning, and a large language model for migrant identification

Migration has become a defining feature of contemporary societies and digital economies. Migration significantly reshapes the global digital economy, yet the lack of explicit country-of-origin data in digital records hinders large-scale quantitative research. This study addresses this methodological gap by systematically evaluating and comparing three computational approaches for inferring migrant origins from social networking data: a rule-based heuristic, supervised machine learning, and a lar