Tracking Policy-relevant Narratives of Democratic Resilience at Scale: from experts and machines, to AI & the transformer revolution

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. In this study, we consider a variety of tools from the field of computational linguistics, which either automate the standard approach to textual annotation, or introduce entirely new ways of conceptualising `text as data', opening up new horizons for the tracking of public narratives of democratic resilience. In particular, we assess the regime-shift occurring in natural language processing and artificial intelligence brought about by the advent of the transformer architecture. By undertaking two distinct empirical analysis tasks with recent transformer methods we provide practical demonstrations that these new tools offer, perhaps for the first time, the `holy grail' of the quantitative social scientist: the ability to identify, accurately, and efficiently, nuanced narratives in text at scale. We conclude by contributing data and research recommendations for public stakeholders who wish to see these opportunities realised. (updated May 2025)