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.

翻译：有效的灾害响应对于受灾社区至关重要。响应人员和决策者需要可靠、及时的指标来了解灾害期间影响社区的问题，而社交媒体提供了潜在丰富的数据源。社交媒体能够反映灾害期间的公众关切与需求，为决策者理解事态演变、优化资源配置提供宝贵见解。我们采用双向编码器表示（BERT）主题建模方法，对推特数据进行主题聚类。随后，通过时空分析考察2020年美国西部野火季期间这些主题在不同区域的分布情况。结果表明，推特用户主要关注三大主题："健康影响"、"损害"和"疏散"。我们运用易感-感染-康复（SIR）理论，探究推特上主题扩散的规模与速度。结果揭示了主题趋势与野火蔓延模式之间的明确关联。从选定城市SIR模型估计的参数显示，居民在火灾期间对多项议题表现出高度关切。本研究详细阐述了如何利用社交媒体数据的SIR模型与主题建模，为决策者提供量化测量灾害响应的方法，并支持其决策过程。