用AI融合社交媒体与官方报告，提升洪水灾害响应效率
Signals from the Floods: AI-Driven Disaster Analysis through Multi-Source Data Fusion
- 结合LDA主题模型与大语言模型，从海量碎片信息中提炼关键内容
- 分析5.5万条推文与1450份申诉文档，发现公众行为的地理分布与观点差异
- 提出相关性指数机制，降低噪声干扰，适合应急管理和公共政策研究
大规模且多源的网络数据在政府灾害响应中日益重要，以2022年澳大利亚新南威尔士州（NSW）洪灾为例。本研究分析了超过55,000条与洪水相关的X（原推特）推文及1,450份公众申诉材料，揭示危机期间公众行为模式。社交媒体内容短小零散，而申诉材料则为详细、多页的结构化文本。研究方法融合隐含狄利克雷分配（LDA）进行主题建模，并利用大语言模型（LLMs）增强语义理解。LDA识别出不同观点与地理分布特征，而LLMs通过以申诉材料为参考，实现对洪水相关推文的精准筛选。提出的相关性指数（Relevance Index）有效降低信息噪声，突出可操作内容，提升应急人员的情境感知能力。通过整合两种互补数据流，本方法构建了一种新型人工智能驱动的灾情信息处理范式，有助于优化实时响应并支持长期韧性规划。
原文摘要 · Abstract (English)
Massive and diverse web data are increasingly vital for government disaster response, as demonstrated by the 2022 floods in New South Wales (NSW), Australia. This study examines how X (formerly Twitter) and public inquiry submissions provide insights into public behaviour during crises. We analyse more than 55,000 flood-related tweets and 1,450 submissions to identify behavioural patterns during extreme weather events. While social media posts are short and fragmented, inquiry submissions are detailed, multi-page documents offering structured insights. Our methodology integrates Latent Dirichlet Allocation (LDA) for topic modelling with Large Language Models (LLMs) to enhance semantic understanding. LDA reveals distinct opinions and geographic patterns, while LLMs improve filtering by identifying flood-relevant tweets using public submissions as a reference. This Relevance Index method reduces noise and prioritizes actionable content, improving situational awareness for emergency responders. By combining these complementary data streams, our approach introduces a novel AI-driven method to refine crisis-related social media content, improve real-time disaster response, and inform long-term resilience planning.
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