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 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. 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 finish by offering several directions for research for those who can harness these new
capabilities.