Public Health in Disaster: Emotional Health and Life Incidents Extraction during Hurricane Harvey

TL;DR

This study analyzes social media data during Hurricane Harvey to understand emotional responses and life incidents, employing advanced NLP techniques like BERT, GNN, and LLM for comprehensive disaster-related insights.

Contribution

It introduces an integrated approach combining BERT, GNN, and LLM to analyze large-scale social media data for disaster response insights, surpassing traditional manual methods.

Findings

Identified key emotional patterns related to the disaster

Generated meaningful event clusters with descriptive labels

Enhanced understanding of public sentiment and incidents during Hurricane Harvey

Abstract

Countless disasters have resulted from climate change, causing severe damage to infrastructure and the economy. These disasters have significant societal impacts, necessitating mental health services for the millions affected. To prepare for and respond effectively to such events, it is important to understand people's emotions and the life incidents they experience before and after a disaster strikes. In this case study, 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. To efficiently identify these topics, we utilized the Latent Dirichlet Allocation (LDA) technique for topic modeling, which allowed us to bypass manual content analysis and extract meaningful patterns from the data. However, rather than stopping at topic identification like previous methods \cite{math11244910}, we further refined our analysis by integrating Graph Neural Networks (GNN) and Large Language Models (LLM). The GNN was employed to generate embeddings and construct a similarity graph of the tweets, which was then used to optimize clustering. Subsequently, we used an LLM to automatically generate descriptive names for each event cluster, offering critical insights for disaster preparedness and response strategies.