  Social media aids disaster response but suffers from noise, hindering
accurate impact assessment and decision making for resilient cities, which few
studies considered. To address the problem, this study proposes the first
domain-specific LLM model and an integrated method for rapid earthquake impact
assessment. First, a few categories are introduced to classify and filter
microblogs considering their relationship to the physical and social impacts of
earthquakes, and a dataset comprising 7282 earthquake-related microblogs from
twenty earthquakes in different locations is developed as well. Then, with a
systematic analysis of various influential factors, QuakeBERT, a
domain-specific large language model (LLM), is developed and fine-tuned for
accurate classification and filtering of microblogs. Meanwhile, an integrated
method integrating public opinion trend analysis, sentiment analysis, and
keyword-based physical impact quantification is introduced to assess both the
physical and social impacts of earthquakes based on social media texts.
Experiments show that data diversity and data volume dominate the performance
of QuakeBERT and increase the macro average F1 score by 27%, while the best
classification model QuakeBERT outperforms the CNN- or RNN-based models by
improving the macro average F1 score from 60.87% to 84.33%. Finally, the
proposed approach is applied to assess two earthquakes with the same magnitude
and focal depth. Results show that the proposed approach can effectively
enhance the impact assessment process by accurate detection of noisy
microblogs, which enables effective post-disaster emergency responses to create
more resilient cities.
