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        <title>Quantifying Societal Stress: Forecasting Historical London Mortality using Hardship Sentiment and Crime Data with Natural Language Processing and Time-Series</title>
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            <namePart type="given">Isuri</namePart>
            <namePart type="given">Nanomi</namePart>
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    <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.</abstract>
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%0 Conference Proceedings
%T Quantifying Societal Stress: Forecasting Historical London Mortality using Hardship Sentiment and Crime Data with Natural Language Processing and Time-Series
%A Olsen, Sebastian
%A Bloem, Jelke
%Y Arachchige, Isuri Nanomi
%Y Frontini, Francesca
%Y Mitkov, Ruslan
%Y Rayson, Paul
%S Proceedings of the First on Natural Language Processing and Language Models for Digital Humanities
%D 2025
%8 September
%I INCOMA Ltd., Shoumen, Bulgaria
%C Varna, Bulgaria
%F olsen-bloem-2025-quantifying
%X 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.
%U https://aclanthology.org/2025.lm4dh-1.10/
%P 112-119

Markdown (Informal)

[Quantifying Societal Stress: Forecasting Historical London Mortality using Hardship Sentiment and Crime Data with Natural Language Processing and Time-Series](https://aclanthology.org/2025.lm4dh-1.10/) (Olsen & Bloem, LM4DH 2025)

• Quantifying Societal Stress: Forecasting Historical London Mortality using Hardship Sentiment and Crime Data with Natural Language Processing and Time-Series (Olsen & Bloem, LM4DH 2025)

ACL

• Sebastian Olsen and Jelke Bloem. 2025. Quantifying Societal Stress: Forecasting Historical London Mortality using Hardship Sentiment and Crime Data with Natural Language Processing and Time-Series. In Proceedings of the First on Natural Language Processing and Language Models for Digital Humanities, pages 112–119, Varna, Bulgaria. INCOMA Ltd., Shoumen, Bulgaria.