用大模型从社交媒体提取灾情信息，生成针对不同救援人员的定制报告。
Multi-Stakeholder Disaster Insights from Social Media Using Large Language Models
- 先分类后生成：用BERT多维分析内容，再用ChatGPT生成定制报告。
- 相比直接提问，报告更准确，文本连贯性提升23%以上。
- 适合应急指挥、媒体发布和一线救援人员快速获取关键信息。
近年来，社交媒体已成为灾时用户快速反馈问题的重要渠道，在危机管理中发挥关键作用。尽管已有大量研究聚焦于社交媒体内容的收集与分析，但如何实现数据的自动化、聚合化与定制化，以满足媒体、警察、急救、消防等多元利益相关方的差异化需求，仍是亟待解决的问题。本文提出一种基于大语言模型的灾情信息处理方法，结合分类与生成技术，将原始用户反馈转化为面向特定受众的可操作洞察。通过BERT等分析模型对灾时社交帖子进行内容类型、情感、情绪、地理位置和主题的多维度分类，再利用ChatGPT等生成模型输出符合受众需求的人类可读报告。实验表明，相较于直接使用提示词输入ChatGPT的标准方法，本方法在文本连贯性得分与潜在表示质量上均有显著提升，并获得自动化评估工具与领域专家的一致认可，有效支持了救援协调、资源分配与媒体沟通等关键任务。
原文摘要 · Abstract (English)
In recent years, social media has emerged as a primary channel for users to promptly share feedback and issues during disasters and emergencies, playing a key role in crisis management. While significant progress has been made in collecting and analyzing social media content, there remains a pressing need to enhance the automation, aggregation, and customization of this data to deliver actionable insights tailored to diverse stakeholders, including the press, police, EMS, and firefighters. This effort is essential for improving the coordination of activities such as relief efforts, resource distribution, and media communication. This paper presents a methodology that leverages the capabilities of LLMs to enhance disaster response and management. Our approach combines classification techniques with generative AI to bridge the gap between raw user feedback and stakeholder-specific reports. Social media posts shared during catastrophic events are analyzed with a focus on user-reported issues, service interruptions, and encountered challenges. We employ full-spectrum LLMs, using analytical models like BERT for precise, multi-dimensional classification of content type, sentiment, emotion, geolocation, and topic. Generative models such as ChatGPT are then used to produce human-readable, informative reports tailored to distinct audiences, synthesizing insights derived from detailed classifications. We compare standard approaches, which analyze posts directly using prompts in ChatGPT, to our advanced method, which incorporates multi-dimensional classification, sub-event selection, and tailored report generation. Our methodology demonstrates superior performance in both quantitative metrics, such as text coherence scores and latent representations, and qualitative assessments by automated tools and field experts, delivering precise insights for diverse disaster response stakeholders.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。