Detecting Perceived Emotions in Hurricane Disasters

TweetDIS: A Large Twitter Dataset for Natural Disasters Built using Weak Supervision

Social media is often utilized as a lifeline for communication during natural disasters. Traditionally, natural disaster tweets are filtered from the Twitter stream using the name of the natural disaster and the filtered tweets are sent for human annotation. The process of human annotation to create labeled sets for machine learning models is laborious, time consuming, at times inaccurate, and more importantly not scalable in terms of size and real-time use. In this work, we curate a silver standard dataset using weak supervision. In order to validate its utility, we train machine learning mod…

灾害中的公共卫生：哈维飓风期间的情绪健康与生活事件提取

Public Health in Disaster: Emotional Health and Life Incidents Extraction during Hurricane Harvey

本研究利用约40万条哈维飓风相关推文，结合BERT情绪预测、LDA主题建模、GNN聚类优化及LLM自动命名，提取灾害前后民众情绪与生活事件，为灾害应对提供关键洞察。

Forecasting People's Needs in Hurricane Events from Social Network

Social networks can serve as a valuable communication channel for calls for help, offering assistance, and coordinating rescue activities in disaster. Social networks such as Twitter allow users to continuously update relevant information, which is especially useful during a crisis, where the rapidly changing conditions make it crucial to be able to access accurate information promptly. Social media helps those directly affected to inform others of conditions on the ground in real time and thus enables rescue workers to coordinate their efforts more effectively, better meeting the survivors' n…

为多族裔社区灾害防备沟通定制生成式AI聊天机器人：扩展CASA范式

Tailoring Generative AI Chatbots for Multiethnic Communities in Disaster Preparedness Communication: Extending the CASA Paradigm

本研究开发了基于GPT-4的GenAI聊天机器人原型，通过441名多族裔居民的被试间实验，证实语气正式性与文化定制能显著影响其友好度与可信度感知，进而提升飓风防备效果。

实时多源飓风疏散通知的信息检索与分类

Information Retrieval and Classification of Real-Time Multi-Source Hurricane Evacuation Notices

本文提出了一种结合空间定向网络爬取、深度学习和自然语言处理的方法，用于实时检索和分类多源飓风疏散通知，并以飓风伊恩为例验证了其在态势感知和灾害响应研究中的有效性。

Weakly-supervised Fine-grained Event Recognition on Social Media Texts for Disaster Management

People increasingly use social media to report emergencies, seek help or share information during disasters, which makes social networks an important tool for disaster management. To meet these time-critical needs, we present a weakly supervised approach for rapidly building high-quality classifiers that label each individual Twitter message with fine-grained event categories. Most importantly, we propose a novel method to create high-quality labeled data in a timely manner that automatically clusters tweets containing an event keyword and asks a domain expert to disambiguate event word senses…