Towards Automated Situational Awareness Reporting for Disaster Management—A Case Study

Abstract

Disasters do not follow a predictable timetable. Rapid situational awareness is essential for disaster management. People witnessing a disaster in the same area and beyond often use social media to report, inform, summarize, update, or warn each other. These warnings and recommendations are faster than traditional news and mainstream media. However, due to the massive amount of raw and unfiltered information, the data cannot be managed by humans in time. Automated situational awareness reporting could significantly and sustainably improve disaster management and save lives by quickly filtering, detecting, and summarizing important information. In this work, we aim to provide a novel approach towards automated situational awareness reporting using microblogging data through event detection and summarization. Therefore, we combine an event detection algorithm with different summarization libraries. We test the proposed approach against data from the Russo-Ukrainian war to evaluate its real-time capabilities and determine how many of the events that occurred could be highlighted. The results reveal that the proposed approach can outline significant events. Further research can be carried out to improve short-text summarization and filtering.