Tales of the 2025 Los Angeles Fire: Hotwash for Public Health Concerns in Reddit via LLM-Enhanced Topic Modeling
- URL: http://arxiv.org/abs/2505.09665v2
- Date: Fri, 16 May 2025 03:41:06 GMT
- Title: Tales of the 2025 Los Angeles Fire: Hotwash for Public Health Concerns in Reddit via LLM-Enhanced Topic Modeling
- Authors: Sulong Zhou, Qunying Huang, Shaoheng Zhou, Yun Hang, Xinyue Ye, Aodong Mei, Kathryn Phung, Yuning Ye, Uma Govindswamy, Zehan Li,
- Abstract summary: This study analyzes Reddit discourse during the 2025 Los Angeles wildfires.<n>We collect 385 posts and 114,879 comments related to the Palisades and Eaton fires.<n>We categorize latent topics, consisting of two main categories, Situational Awareness (SA) and Crisis Narratives (CN)
- Score: 1.3833272340822185
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
Related papers
- SPEED++: A Multilingual Event Extraction Framework for Epidemic Prediction and Preparedness [73.73883111570458]
We introduce the first multilingual Event Extraction framework for extracting epidemic event information for a wide range of diseases and languages.
Annotating data in every language is infeasible; thus we develop zero-shot cross-lingual cross-disease models.
Our framework can provide epidemic warnings for COVID-19 in its earliest stages in Dec 2019 from Chinese Weibo posts without any training in Chinese.
arXiv Detail & Related papers (2024-10-24T03:03:54Z) - Public Health in Disaster: Emotional Health and Life Incidents Extraction during Hurricane Harvey [1.433758865948252]
We collected a dataset of approximately 400,000 public tweets related to the storm.
Using a BERT-based model, we predicted the emotions associated with each tweet.
We further refined our analysis by integrating Graph Neural Networks (GNN) and Large Language Models (LLM)
arXiv Detail & Related papers (2024-08-20T18:31:20Z) - CrisisSense-LLM: Instruction Fine-Tuned Large Language Model for Multi-label Social Media Text Classification in Disaster Informatics [49.2719253711215]
This study introduces a novel approach to disaster text classification by enhancing a pre-trained Large Language Model (LLM)<n>Our methodology involves creating a comprehensive instruction dataset from disaster-related tweets, which is then used to fine-tune an open-source LLM.<n>This fine-tuned model can classify multiple aspects of disaster-related information simultaneously, such as the type of event, informativeness, and involvement of human aid.
arXiv Detail & Related papers (2024-06-16T23:01:10Z) - Event Detection from Social Media for Epidemic Prediction [76.90779562626541]
We develop a framework to extract and analyze epidemic-related events from social media posts.
Experimentation reveals how ED models trained on COVID-based SPEED can effectively detect epidemic events for three unseen epidemics.
We show that reporting sharp increases in the extracted events by our framework can provide warnings 4-9 weeks earlier than the WHO epidemic declaration for Monkeypox.
arXiv Detail & Related papers (2024-04-02T06:31:17Z) - Community-based Behavioral Understanding of Crisis Activity Concerns
using Social Media Data: A Study on the 2023 Canadian Wildfires in New York
City [0.5793371273485736]
NYC topped the global chart for the worst air pollution in June 2023, owing to the wildfire smoke drifting in from Canada.
This study utilized large-scale social media data to study different crisis activity concerns.
arXiv Detail & Related papers (2024-01-22T06:57:45Z) - Investigating disaster response through social media data and the
Susceptible-Infected-Recovered (SIR) model: A case study of 2020 Western U.S.
wildfire season [0.8999666725996975]
Social media can reflect public concerns and demands during a disaster.
We used Bidirectional Representations from Transformers (BERT) topic modeling to cluster topics from Twitter data.
Our study details how the SIR model and topic modeling using social media data can provide decision-makers with a quantitative approach to measure disaster response.
arXiv Detail & Related papers (2023-08-10T01:51:33Z) - Know it to Defeat it: Exploring Health Rumor Characteristics and
Debunking Efforts on Chinese Social Media during COVID-19 Crisis [65.74516068984232]
We conduct a comprehensive analysis of four months of rumor-related online discussion during COVID-19 on Weibo, a Chinese microblogging site.
Results suggest that the dread (cause fear) type of health rumors provoked significantly more discussions and lasted longer than the wish (raise hope) type.
We show the efficacy of debunking in suppressing rumor discussions, which is time-sensitive and varies across rumor types and debunkers.
arXiv Detail & Related papers (2021-09-25T14:02:29Z) - When a crisis strikes: Emotion analysis and detection during COVID-19 [96.03869351276478]
We present CovidEmo, 1K tweets labeled with emotions.
We examine how well large pre-trained language models generalize across domains and crises.
arXiv Detail & Related papers (2021-07-23T04:07:14Z) - From Static to Dynamic Prediction: Wildfire Risk Assessment Based on
Multiple Environmental Factors [69.9674326582747]
Wildfire is one of the biggest disasters that frequently occurs on the west coast of the United States.
We propose static and dynamic prediction models to analyze and assess the areas with high wildfire risks in California.
arXiv Detail & Related papers (2021-03-14T17:56:17Z) - Tracking the evolution of crisis processes and mental health on social
media during the COVID-19 pandemic [0.90238471756546]
This study aims at examining the stages of crisis response and recovery as a sociological problem.
Based on a large collection of Twitter data spanning from March to August 2020 in Argentina, we present a thematic analysis on the differences in language used in social media posts.
arXiv Detail & Related papers (2020-11-22T14:30:09Z) - Leveraging Natural Language Processing to Mine Issues on Twitter During
the COVID-19 Pandemic [0.3674863913115431]
The recent global outbreak of the coronavirus disease (COVID-19) has spread to all corners of the globe.
To understand the public concerns and responses to the pandemic, a system that can leverage machine learning techniques to filter out irrelevant tweets is needed.
In this study, we constructed a system to identify the relevant tweets related to the COVID-19 pandemic throughout January 1st, 2020 to April 30th, 2020.
arXiv Detail & Related papers (2020-10-31T22:26:26Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.