利用大型语言模型从社交媒体获取多方利益相关者的灾害洞察
Multi-Stakeholder Disaster Insights from Social Media Using Large Language Models
AI总结：
针对社交媒体灾害数据难以为多方利益相关者提供定制化可操作洞察的问题，提出结合LLMs分类（如BERT多维度分类）与生成（如ChatGPT定制报告）的方法，性能优于标准方法，能为灾害响应提供精准支持。
摘要：
近年来，社交媒体已成为用户在灾害和紧急情况期间及时分享反馈和问题的主要渠道，在危机管理中发挥着关键作用。尽管在社交媒体内容的收集和分析方面取得了显著进展，但仍迫切需要增强这些数据的自动化、聚合和定制能力，以提供针对新闻界、警方、急救医疗服务（EMS）和消防员等不同利益相关者的可操作洞察。这一工作对于改善救援工作、资源分配和媒体沟通等活动的协调至关重要。本文提出了一种利用大型语言模型（LLMs）能力来增强灾害响应和管理的方法。我们的方法将分类技术与生成式人工智能相结合，以弥合原始用户反馈与利益相关者特定报告之间的差距。分析灾难性事件期间分享的社交媒体帖子，重点关注用户报告的问题、服务中断和遇到的挑战。我们使用全谱LLMs，例如利用BERT等分析模型对内容类型、情感、情绪、地理位置和主题进行精确的多维度分类。然后，使用ChatGPT等生成模型生成面向不同受众的可读、信息丰富的报告，综合从详细分类中得出的洞察。我们将直接使用ChatGPT提示分析帖子的标准方法与我们结合了多维度分类、子事件选择和定制报告生成的先进方法进行了比较。我们的方法在文本连贯性得分和潜在表示等定量指标以及自动化工具和实地专家的定性评估中均表现出优越性能，为不同的灾害响应利益相关者提供精确洞察。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
