用多模态大模型分析社交媒体，实现地震灾情快速精准评估
A Multimodal, Multilingual, and Multidimensional Pipeline for Fine-grained Crowdsourcing Earthquake Damage Evaluation
- 构建跨模态、跨语言、多维度的灾情评估流水线
- 模型在两次大地震中与真实地震数据高度相关（相关系数>0.8）
- 适合应急响应、灾害监测和多语言社会媒体分析研究者
快速、精细的灾情评估对有效应急响应至关重要，但受限于地面传感器不足和官方报告延迟。社交媒体提供了丰富且实时的人类观测信息，但其多模态、非结构化特性给传统分析方法带来挑战。本文提出一种结构化的多模态、多语言、多维度（3M）评估流水线，利用多模态大语言模型（MLLMs）进行灾情分析。我们在两次重大地震事件中评估了三种基础模型，采用宏观与微观双重分析方法。结果表明，MLLMs能有效融合图像-文本信号，与真实地震数据呈现强相关性（相关系数>0.8）。然而，模型性能受语言、震中距离和输入模态影响。本研究展示了MLLMs在灾情评估中的潜力，并为未来在实时危机场景中应用提供基础。代码与数据已公开：https://github.com/missa7481/EMNLP25_earthquake
原文摘要 · Abstract (English)
Rapid, fine-grained disaster damage assessment is essential for effective emergency response, yet remains challenging due to limited ground sensors and delays in official reporting. Social media provides a rich, real-time source of human-centric observations, but its multimodal and unstructured nature presents challenges for traditional analytical methods. In this study, we propose a structured Multimodal, Multilingual, and Multidimensional (3M) pipeline that leverages multimodal large language models (MLLMs) to assess disaster impacts. We evaluate three foundation models across two major earthquake events using both macro- and micro-level analyses. Results show that MLLMs effectively integrate image-text signals and demonstrate a strong correlation with ground-truth seismic data. However, performance varies with language, epicentral distance, and input modality. This work highlights the potential of MLLMs for disaster assessment and provides a foundation for future research in applying MLLMs to real-time crisis contexts. The code and data are released at: https://github.com/missa7481/EMNLP25_earthquake
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