Wildfires have become increasingly frequent, irregular, and severe in recent years. Understanding how affected populations perceive and respond during wildfire crises is critical for timely and empathetic disaster response. Social media platforms offer a crowd-sourced channel to capture evolving public discourse, providing hyperlocal information and insight into public sentiment. This study analyzes Reddit discourse during the 2025 Los Angeles wildfires, spanning from the onset of the disaster to full containment. We collect 385 posts and 114,879 comments related to the Palisades and Eaton fires. We adopt topic modeling methods to identify the latent topics, enhanced by large language models (LLMs) and human-in-the-loop (HITL) refinement. Furthermore, we develop a hierarchical framework to categorize latent topics, consisting of two main categories, Situational Awareness (SA) and Crisis Narratives (CN). The volume of SA category closely aligns with real-world fire progressions, peaking within the first 2-5 days as the fires reach the maximum extent. The most frequent co-occurring category set of public health and safety, loss and damage, and emergency resources expands on a wide range of health-related latent topics, including environmental health, occupational health, and one health. Grief signals and mental health risks consistently accounted for 60 percentage and 40 percentage of CN instances, respectively, with the highest total volume occurring at night. This study contributes the first annotated social media dataset on the 2025 LA fires, and introduces a scalable multi-layer framework that leverages topic modeling for crisis discourse analysis. By identifying persistent public health concerns, our results can inform more empathetic and adaptive strategies for disaster response, public health communication, and future research in comparable climate-related disaster events.

翻译：近年来，野火的发生日益频繁、不规则且严重。了解受灾人群在野火危机中的认知与应对方式，对于及时且富有同理心的灾害响应至关重要。社交媒体平台提供了一个众包渠道，能够捕捉不断演变的公共讨论，提供超本地化信息并洞察公众情绪。本研究分析了2025年洛杉矶野火期间Reddit上的讨论，时间跨度从灾害爆发到完全受控。我们收集了385篇帖子和114,879条与帕利塞兹和伊顿火灾相关的评论。我们采用主题建模方法来识别潜在主题，并通过大型语言模型（LLMs）和人机协同（HITL）优化进行增强。此外，我们开发了一个分层框架来对潜在主题进行分类，该框架包含两个主要类别：态势感知（SA）和危机叙事（CN）。SA类别的讨论量与现实世界的火灾进展紧密相关，在火灾达到最大范围的前2-5天内达到峰值。公共卫生与安全、损失与损害以及应急资源这三个最常共现的类别集合，扩展了广泛的健康相关潜在主题，包括环境健康、职业健康以及"一体化健康"。悲伤信号和心理健康风险分别持续占CN实例的60%和40%，且总讨论量在夜间达到最高。本研究贡献了首个关于2025年洛杉矶火灾的标注社交媒体数据集，并引入了一个可扩展的多层框架，该框架利用主题建模进行危机话语分析。通过识别持续的公共卫生关切，我们的研究结果可为灾害响应、公共卫生沟通以及未来类似气候相关灾害事件的研究，提供更具同理心和适应性的策略参考。