发表机构
J.E. Cairnes School of Business & Economics, University of Galway（杰伊·E·凯恩斯商学院与经济学院，高威大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出基于爱尔兰公共话语的洪水福祉评估框架，利用Wellbeing-Former模型分析22.4万条帖子，从痛苦、功能中断和制度疏离三维度量化影响，补充调查方法，支持政策与适应规划。
AI 中文摘要
背景。爱尔兰位于北大西洋东部，受潮湿西风带和频繁低压系统影响，降水丰富，面临显著的暴雨和河流洪水风险。气候变化正加剧强降雨和复合洪水风险，其后果不仅限于物理损害和传统经济指标。方法。我们提出一个框架，通过随时间推移的非侵入性公共话语评估洪水相关福祉。该框架开发了一个评估工具，包含三个互补构念：痛苦、功能中断和制度疏离，分别捕捉洪水对情感评价、日常功能以及社会和制度连接的影响。为支持人群层面的复杂认知和心理反应分析，我们开发了一个专门的洪水相关福祉推理模型（Wellbeing-Former），该模型读取洪水相关福祉证据，赋予基于证据的评分，并生成透明的推理依据。数据、结果与意义。利用研究平台MCL（元内容库），我们通过查询爱尔兰包含“洪水”、“雨”和“风暴”术语的帖子，构建了一个洪水相关话语数据集。所得数据集包含约224,000条帖子，时间跨度为2012年至2016年。我们将所开发的Wellbeing-Former应用于该数据集，进行人群层面分析。该方法提供了对经历和预期洪水影响的时间敏感证据，补充了基于调查的评估，并支持在气候相关灾害下对福祉的多维理解，以服务于政策制定、适应规划、公共沟通和决策。
英文摘要
\ 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.
CommentsExtended Abstract for CERIS Workshop 2026
