@inproceedings{olsen-bloem-2025-quantifying,
    title = "Quantifying Societal Stress: Forecasting Historical {L}ondon Mortality using Hardship Sentiment and Crime Data with Natural Language Processing and Time-Series",
    author = "Olsen, Sebastian  and
      Bloem, Jelke",
    editor = "Arachchige, Isuri Nanomi  and
      Frontini, Francesca  and
      Mitkov, Ruslan  and
      Rayson, Paul",
    booktitle = "Proceedings of the First on Natural Language Processing and Language Models for Digital Humanities",
    month = sep,
    year = "2025",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd., Shoumen, Bulgaria",
    url = "https://preview.aclanthology.org/master-new-author-system-docs/2025.lm4dh-1.10/",
    pages = "112--119",
    abstract = "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."
}