  Effective disaster response is critical for affected communities. Responders
and decision-makers would benefit from reliable, timely measures of the issues
impacting their communities during a disaster, and social media offers a
potentially rich data source. Social media can reflect public concerns and
demands during a disaster, offering valuable insights for decision-makers to
understand evolving situations and optimize resource allocation. We used
Bidirectional Encoder Representations from Transformers (BERT) topic modeling
to cluster topics from Twitter data. Then, we conducted a temporal-spatial
analysis to examine the distribution of these topics across different regions
during the 2020 western U.S. wildfire season. Our results show that Twitter
users mainly focused on three topics:"health impact," "damage," and
"evacuation." We used the Susceptible-Infected-Recovered (SIR) theory to
explore the magnitude and velocity of topic diffusion on Twitter. The results
displayed a clear relationship between topic trends and wildfire propagation
patterns. The estimated parameters obtained from the SIR model in selected
cities revealed that residents exhibited a high level of several concerns
during the wildfire. 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 and support their decision-making
processes.

Social and Information Networks;Computation and Language;Information Retrieval;Machine Learning