  When a damaging earthquake occurs, immediate information about casualties is
critical for time-sensitive decision-making by emergency response and aid
agencies in the first hours and days. Systems such as Prompt Assessment of
Global Earthquakes for Response (PAGER) by the U.S. Geological Survey (USGS)
were developed to provide a forecast within about 30 minutes of any significant
earthquake globally. Traditional systems for estimating human loss in disasters
often depend on manually collected early casualty reports from global media, a
process that's labor-intensive and slow with notable time delays. Recently,
some systems have employed keyword matching and topic modeling to extract
relevant information from social media. However, these methods struggle with
the complex semantics in multilingual texts and the challenge of interpreting
ever-changing, often conflicting reports of death and injury numbers from
various unverified sources on social media platforms. In this work, we
introduce an end-to-end framework to significantly improve the timeliness and
accuracy of global earthquake-induced human loss forecasting using
multi-lingual, crowdsourced social media. Our framework integrates (1) a
hierarchical casualty extraction model built upon large language models, prompt
design, and few-shot learning to retrieve quantitative human loss claims from
social media, (2) a physical constraint-aware, dynamic-truth discovery model
that discovers the truthful human loss from massive noisy and potentially
conflicting human loss claims, and (3) a Bayesian updating loss projection
model that dynamically updates the final loss estimation using discovered
truths. We test the framework in real-time on a series of global earthquake
events in 2021 and 2022 and show that our framework streamlines casualty data
retrieval, achieving speed and accuracy comparable to manual methods by USGS.
