Abstract Summary
Information retrieval systems play a central role in how people access and understand information about complex social issues, including immigration. Yet little is known about how the datasets that underpin these systems represent migrants or structure public narratives about migration. In this paper, we investigate how immigration is framed within a widely used IR benchmark and how ranking models shape the visibility of those frames. Using MS MARCO as our data source, we curate immigration-related queries and annotate retrieved passages using a migration-specific framing taxonomy grounded in social-science research. Our goal is to identify which narratives dominate and to measure how different retrieval models influence their exposure. We find that legality and security frames are far more common than humanitarian or inclusive ones, and that neural reranking amplifies exclusionary portrayals compared to sparse retrieval.
