Identifying Hurricane Evacuation Intent on Twitter
Keywords:Measuring predictability of real world phenomena based on social media, e.g., spanning politics, finance, and health, Social media usage on mobile devices; location, human mobility, and behavior
AbstractEvacuations have a significant impact on saving human lives during hurricanes. However, as a complex dynamic process, it is typically difficult to know individual evacuation decisions in real-time. Since a large amount of information is continuously posted through social media platforms, we can use them to understand individual evacuation behavior. In this paper, we collect tweets during Hurricane Irma in 2017 and train a text classifier in an active learning way to distinguish tweets expressing positive evacuation decisions from both negative and irrelevant ones. Additionally, we perform a demographic analysis and content clustering to investigate the potential causes and correlates of evacuation decisions. The results can be used to help inform planning strategies of emergency response agencies.
How to Cite
Li, X., Hasan, S., & Culotta, A. (2022). Identifying Hurricane Evacuation Intent on Twitter. Proceedings of the International AAAI Conference on Web and Social Media, 16(1), 618-627. Retrieved from https://ojs.aaai.org/index.php/ICWSM/article/view/19320