Twitter, language and politics: What language do people use to make arguments about immigrant children?

Public debates about immigration increasingly unfold on social media platforms, where language plays a central role in shaping perceptions of national belonging and social membership. This work examines how individuals construct and justify positions on immigration through discourse surrounding the Deferred Action for Childhood Arrivals (DACA) policy on Twitter. Using computational text analysis methods, I analyze these large-scale social media data to identify recurring argumentative patterns and discursive frameworks deployed in public discussions. I develop a taxonomy of cognitive and rhetorical schemas through which users interpret citizenship, legitimacy, and inclusion within the nation-state. The findings highlight that people rely on distinct but overlapping schemas—including legalistic, racialized nationalist, and advocacy-oriented frames—to interpret and justify positions on citizenship and belonging. By linking computational analysis with theories of social cognition and discourse, this research contributes methodological approaches for studying political sense-making in online environments and provides insights into how sociotechnical systems shape public reasoning about contested social issues.