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Tales of the 2025 Los Angeles Fire: Hotwash for Public Health Concerns in Reddit via LLM-Enhanced Topic Modeling

Sulong Zhou, Qunying Huang, Shaoheng Zhou, Yun Hang, Xinyue Ye, Aodong Mei, Kathryn Phung, Yuning Ye, Uma Govindswamy, Zehan Li

TL;DR

This study targets real-time public health insights during the 2025 Los Angeles wildfires by applying an LLM-enhanced, hierarchical topic modeling framework that combines post-level LDA with comment-level BERTopic, guided by human-in-the-loop validation. It jointly analyzes Situational Awareness and Crisis Narratives to map public health concerns (air and water quality, occupational health, one health) and grief/mental health signals over time and across two major fires, Palisades and Eaton. The authors provide a publicly released, annotated Reddit dataset and demonstrate how SA and CN structures, together with grief and mental health indicators, reveal persistent health risks and inform empathetic disaster response and health communication. The approach yields a scalable, adaptable framework applicable to other climate-related disasters and social-media data sources, supporting faster, more nuanced crisis analysis than traditional methods.

Abstract

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.

Tales of the 2025 Los Angeles Fire: Hotwash for Public Health Concerns in Reddit via LLM-Enhanced Topic Modeling

TL;DR

This study targets real-time public health insights during the 2025 Los Angeles wildfires by applying an LLM-enhanced, hierarchical topic modeling framework that combines post-level LDA with comment-level BERTopic, guided by human-in-the-loop validation. It jointly analyzes Situational Awareness and Crisis Narratives to map public health concerns (air and water quality, occupational health, one health) and grief/mental health signals over time and across two major fires, Palisades and Eaton. The authors provide a publicly released, annotated Reddit dataset and demonstrate how SA and CN structures, together with grief and mental health indicators, reveal persistent health risks and inform empathetic disaster response and health communication. The approach yields a scalable, adaptable framework applicable to other climate-related disasters and social-media data sources, supporting faster, more nuanced crisis analysis than traditional methods.

Abstract

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.
Paper Structure (24 sections, 11 figures, 6 tables)

This paper contains 24 sections, 11 figures, 6 tables.

Table of Contents

  1. Introduction
  2. Literature Review
  3. Topic Modeling Algorithms
  4. Topic Modeling Applications in Wildfires
  5. Dataset Sources
  6. Topic Classification Schema
  7. Methods
  8. Data Collection
  9. Data Preprocessing
  10. Topic Modeling
  11. Post-level Modeling
  12. Comment-level Modeling
  13. Topic Analysis
  14. Results
  15. Hierarchy Topic Modeling Analysis
  16. ...and 9 more sections

Figures (11)

  • Figure 1: A Topic Modeling framework enhanced by Large Language Models and Human-in-the-Loop annotation
  • Figure 2: Situational awareness (SA) intersection patterns revealed by UpSet plot. The UpSet plot visualizes topic co-occurrences across three components: the left horizontal bar shows the size of each individual set (topic category), the bottom matrix indicates which sets are involved in each intersection, and the top histogram displays the size of these intersections. For example, the set size of Public Health and Safety is 24,832 instances. The combination of Public Health and Safety and Fire Operations (indicated by connected dots in the matrix) accounts for 1,219 instances.
  • Figure 3: Crisis narrative (CN) intersection patterns revealed by UpSet plot. The UpSet plot visualizes topic co-occurrences across three components: the left horizontal bar shows the size of each individual set (topic category), the bottom matrix indicates which sets are involved in each intersection, and the top histogram displays the size of these intersections. For example, the set size of Victim is 35,369 instances. The combination of Victim and Blame (indicated by connected dots in the matrix) accounts for 11,489 instances.
  • Figure 4: Spatial pattern: a. Geographic extent and burned area comparison for the Palisades and Eaton Fires, mapped as of January 31; b. Comparison of public health realted topic distributions between the Palisades and Eaton Fires
  • Figure 5: Temporal pattern: a. Summary of progression and b. Suppression details for the Palisades and Eaton Fires, as inferred from Reddit posts c. Temporal trends and domain keywords of selected public health-related topics
  • ...and 6 more figures