Emergencies and critical incidents often unfold rapidly, necessitating a
swift and effective response. In this research, we introduce a novel approach
to identify and classify emergency situations from social media posts and
direct emergency messages using an open source Large Language Model, LLAMA2.
The goal is to harness the power of natural language processing and machine
learning to assist public safety telecommunicators and huge crowds during
countrywide emergencies. Our research focuses on developing a language model
that can understand users describe their situation in the 911 call, enabling
LLAMA2 to analyze the content and offer relevant instructions to the
telecommunicator, while also creating workflows to notify government agencies
with the caller's information when necessary. Another benefit this language
model provides is its ability to assist people during a significant emergency
incident when the 911 system is overwhelmed, by assisting the users with simple
instructions and informing authorities with their location and emergency
information.

通过使用开放源代码的大型语言模型 LLAMA2，我们介绍了一种新的方法来从社交媒体帖子和直接应急信息中识别和分类紧急情况，旨在利用自然语言处理和机器学习的力量，为公共安全通信员和大规模人群在全国范围内的紧急情况提供帮助，并开发了一种语言模型，使其能够理解用户在 911 呼叫中描述的情况，使 LLAMA2 能够分析内容并向通信员提供相关指示，并在必要时创建工作流程以通知政府机构与呼叫者信息。这种语言模型的另一个好处是在 911 系统不堪重负时能够帮助用户提供简单的指导并向相关部门提供其位置和紧急信息。

LLM 辅助危机管理：创建先进的 LLM 平台以实现有效的应急响应和公共协作

LLM-Assisted Crisis Management: Building Advanced LLM Platforms for  Effective Emergency Response and Public Collaboration

Social media have the potential to provide timely information about emergency
situations and sudden events. However, finding relevant information among
millions of posts being posted every day can be difficult, and developing a
data analysis project usually requires time and technical skills. This study
presents an approach that provides flexible support for analyzing social media,
particularly during emergencies. Different use cases in which social media
analysis can be adopted are introduced, and the challenges of retrieving
information from large sets of posts are discussed.
The focus is on analyzing images and text contained in social media posts and
a set of automatic data processing tools for filtering, classification, and
geolocation of content with a human-in-the-loop approach to support the data
analyst. Such support includes both feedback and suggestions to configure
automated tools, and crowdsourcing to gather inputs from citizens. The results
are validated by discussing three case studies developed within the Crowd4SDG
H2020 European project.

本研究提出了一种方法，对社交媒体进行灵活的支持，特别是在紧急情况下的社交媒体分析，基于自动化数据处理的工具可用于筛选、分类和地理位置标记内容，同时杂交方法支持人工数据分析师的反馈和建议，从民众中收集输入。通过 Euroopean 项目中的三个案例研究进行结果验证。

在 Crowd4SDG 中使用众包分析社交媒体

Analyzing social media with crowdsourcing in Crowd4SDG

We present SAVITR, a system that leverages the information posted on the
Twitter microblogging site to monitor and analyse emergency situations. Given
that only a very small percentage of microblogs are geo-tagged, it is essential
for such a system to extract locations from the text of the microblogs. We
employ natural language processing techniques to infer the locations mentioned
in the microblog text, in an unsupervised fashion and display it on a map-based
interface. The system is designed for efficient performance, achieving an
F-score of 0.79, and is approximately two orders of magnitude faster than other
available tools for location extraction.

SAVITR 是一个利用 Twitter 信息来监控和分析紧急情况的系统，通过自然语言处理技术，对微博文本中提到的位置进行无监督式推断，并将其显示在基于地图的界面上。该系统的性能高效，F-score 为 0.79，速度约为其他可用位置提取工具的两个数量级。