Abstract Democratic resilience is as much about the narratives of our nation we affirm, as the institutions that enshrine our values and laws, a fact re-affirmed by scholarship across many branches of social science in recent decades. For policymakers and quantitative social scientists, analysing or tracking public discourse through the lens of narrative and framing has historically involved the annotation of texts by hand, placing severe limitations on the scale and modality of discourse under inquiry. Yet, a revolution is at hand—a transformer revolution: first arising in computer science, and now enabling a new kind of automated narrative analysis at scale, transformers are opening up new horizons for the tracking of public narratives of democratic resilience. Here, we: formulate a conceptual framework linking computational language methods to democratic resilience analysis; introduce transformer-based artificial intelligence (AI) methods as a third wave in natural language processing technology; and demonstrate two practical applications of transformer methods to democratic narrative analysis. Finally, we conclude by contributing data and research recommendations which flow naturally from the opportunities unlocked by transformer methods for public stakeholders who wish to see these opportunities realised. Together, we suggest that, perhaps for the first time, the “holy grail” of the quantitative social scientist is near: the ability to identify, accurately, and efficiently, nuanced narratives in text, at scale.
Simon D. Angus (Thu,) studied this question.