Murthy Dhiraj, Kurz Sophia Elisavet, Anand Tanvi, Hornick Sonali, Lakuduva Nandhini, Sun Jerry
Moody College of Communication, Department of Sociology, and School of Information, University of Texas at Austin, Austin, Texas, United States of America.
Computational Media Lab, University of Texas at Austin, Austin, Texas, United States of America.
PLoS One. 2025 Jan 24;20(1):e0316852. doi: 10.1371/journal.pone.0316852. eCollection 2025.
Instead of turning to emergency phone systems, social media platforms, such as Twitter, have emerged as alternative and sometimes preferred venues for members of the public in the US to communicate during hurricanes and other natural disasters. However, relevant posts are likely to be missed by responders given the volume of content on platforms. Previous work successfully identified relevant posts through machine-learned methods, but depended on human annotators. Our study indicates that a GPU-accelerated version of BERTopic, a transformer-based topic model, can be used without human training to successfully discern topics during multiple hurricanes. We use 1.7 million tweets from four US hurricanes over seven years and categorize identified topics as temporal constructs. Some of the more prominent topics related to disaster relief, user concerns, and weather conditions. Disaster managers can use our model, data, and constructs to be aware of the types of themes social media users are producing and consuming during hurricanes.
在美国，社交媒体平台（如推特）已成为公众在飓风和其他自然灾害期间进行交流的替代场所，有时甚至是首选场所，而不是求助于应急电话系统。然而，由于平台上的内容量巨大，响应者很可能会错过相关帖子。先前的工作通过机器学习方法成功识别了相关帖子，但依赖于人工标注。我们的研究表明，基于Transformer的主题模型BERTopic的GPU加速版本无需人工训练，就能在多次飓风期间成功辨别主题。我们使用了七年来来自美国四次飓风的170万条推文，并将识别出的主题归类为时间结构。一些较为突出的主题与救灾、用户关注和天气状况有关。灾害管理人员可以使用我们的模型、数据和结构，了解社交媒体用户在飓风期间产生和关注的主题类型。
