Problem
Research questions and friction points this paper is trying to address.
flood-related wellbeing
population-level assessment
public discourse
climate change
Ireland
A population-level assessment framework for flood-related wellbeing from public discourse in Ireland
🤖 AI Summary
This study addresses the challenge of quantifying the impacts of climate change-induced flooding on residents' physical and mental well-being by transcending the limitations of conventional economic indicators. We propose a public discourse-based well-being assessment framework that integrates natural language processing with large language model reasoning. Specifically, we introduce Wellbeing-Former, a novel model designed to accurately extract multidimensional psychological indicators—including distress, functional disruption, and institutional alienation—from non-invasive social media data. By analyzing 224,000 posts, this project achieves large-scale dynamic monitoring of collective well-being. Ultimately, this work provides both empirical and anticipatory evidence to inform climate adaptation planning and policy formulation.
📝 Abstract
\ textbf{Background.} Ireland's position on the eastern North Atlantic exposes it to moisture-laden westerlies and frequent low-pressure systems, generating abundant precipitation and substantial pluvial and fluvial flood risk. Climate change is intensifying heavy rainfall and compound flood risk, with consequences extending beyond physical damage and conventional economic indicators.
\ textbf{Method.} We present a framework for assessing flood-related wellbeing from unobtrusive public discourse over time. The framework develops an assessment instrument comprising three complementary constructs: \emph{distress}, \emph{functional disruption}, and \emph{institutional alienation}, capturing flood-related impacts on affective appraisal, daily functioning, and social and institutional connectedness. To enable population-scale analysis of complex cognitive and psychological responses, we develop a dedicated flood-related wellbeing reasoning model (\textsc{Wellbeing-Former}) that reads evidence of flood-related wellbeing, assigns evidence-grounded scores, and produces transparent rationales.
\textbf{Data, Results \& Implications.} Using the research platform \textsc{MCL} (Meta Content Library), we construct a flood-related discourse dataset by querying posts from Ireland containing the terms \emph{flood}, \emph{rain}, and \emph{storm}. The resulting dataset comprises approximately \textbf{$224{,}000$} posts produced between 2012 and 2016. We apply the resulting \textsc{Wellbeing-Former} to this dataset to conduct a population-level analysis. This approach provides temporally sensitive evidence of experienced and anticipated flood impacts, complements survey-based assessment, and supports a multidimensional understanding of wellbeing under climate-related hazards for policy development, adaptation planning, public communication, and decision-making.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Wellbeing-Former
Population-level assessment framework
Public discourse analysis
Flood-related wellbeing
Reasoning model
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