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

2025-05-14

AI Summary

This study investigates public perception and response to public health risks during the 2025 Los Angeles wildfire, as manifested on Reddit. Method: Leveraging 385 posts and 114,000 comments, we develop a human-verified, LLM-augmented hierarchical topic modeling framework integrating optimized LDA, prompt-engineering–driven topic refinement, human-in-the-loop annotation, and joint temporal–semantic analysis. Contribution/Results: We release the first annotated 2025 LA Wildfire Reddit dataset. Our analysis identifies four salient thematic domains: environmental health, occupational health, “One Health,” and nighttime-emergent mental health risks—where grief signals constitute 60% of mental health content and risk peaks occur nocturnally. Topic evolution closely aligns with real-time fire progression; public health services (PHS), damage assessment, and emergency resource allocation exhibit the highest co-occurrence frequency. These findings advance precision public health response and risk communication during disasters.

Technology Category

• Application Domains: Misinformation & Fake News
• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval

Application Category

• Web Mining and Content Analysis: Topic discovery and tracking
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

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.

Problem

• Analyzing public health concerns during wildfires via social media
• Enhancing topic modeling with LLMs for crisis discourse analysis
• Identifying persistent health risks to improve disaster response strategies

Innovation

• LLM-enhanced topic modeling for crisis discourse
• Hierarchical framework categorizing Situational Awareness and Crisis Narratives
• Human-in-the-loop refinement for latent topic identification