Public Health in Disaster: Emotional Health and Life Incidents Extraction during Hurricane Harvey
This work addresses mental health and disaster preparedness for affected populations by providing automated insights from social media data, though it is incremental as it builds on existing methods like BERT and LDA with new integrations.
The study tackled the problem of understanding public emotions and life incidents during Hurricane Harvey by analyzing about 400,000 tweets, using BERT for emotion prediction and integrating GNN and LLM to refine clustering and generate descriptive event names, achieving automated extraction of meaningful patterns for disaster response.
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.