Quantifying Societal Stress: Forecasting Historical London Mortality using Hardship Sentiment and Crime Data with Natural Language Processing and Time-Series

We study links between societal stress - quantified from 18th–19th century Old Bailey trial records - and weekly mortality in historical London. Using MacBERTh-based hardship sentiment and time-series analyses (CCF, VAR/IRF, and a Temporal Fusion Transformer, TFT), we find robust lead–lag associations. Hardship sentiment shows its strongest predictive contribution at a 5–6 week lead for mortality in the TFT, while mortality increases precede higher conviction rates in the courts. Results align with Epidemic Psychology and suggest that text-derived stress markers can improve forecasting of public-health relevant mortality fluctuations.