  By training deep neural networks on massive archives of digitized text, large
language models (LLMs) learn the complex linguistic patterns that constitute
historic and contemporary discourses. We argue that LLMs can serve as a
valuable tool for sociological inquiry by enabling accurate simulation of
respondents from specific social and cultural contexts. Applying LLMs in this
capacity, we reconstruct the public opinion landscape of 2019 to examine the
extent to which the future polarization over COVID-19 was prefigured in
existing political discourse. Using an LLM trained on texts published through
2019, we simulate the responses of American liberals and conservatives to a
battery of pandemic-related questions. We find that the simulated respondents
reproduce observed partisan differences in COVID-19 attitudes in 84% of cases,
significantly greater than chance. Prompting the simulated respondents to
justify their responses, we find that much of the observed partisan gap
corresponds to differing appeals to freedom, safety, and institutional trust.
Our findings suggest that the politicization of COVID-19 was largely consistent
with the prior ideological landscape, and this unprecedented event served to
advance history along its track rather than change the rails.
