Unsupervised Detection of Sub-events in Large Scale Disasters
This work addresses the challenge for emergency responders of filtering actionable information from vast social media posts during disasters, though it appears incremental as it builds on existing unsupervised and ontology-based approaches.
The paper tackles the problem of automatically identifying important sub-events in large-scale disasters from social media data, presenting an unsupervised learning framework that extracts and ranks noun-verb pairs and phrases against a crisis-specific ontology, with quantitative experiments on Hurricane Harvey and the 2015 Nepal Earthquake datasets demonstrating effectiveness over state-of-the-art methods.
Social media plays a major role during and after major natural disasters (e.g., hurricanes, large-scale fires, etc.), as people ``on the ground'' post useful information on what is actually happening. Given the large amounts of posts, a major challenge is identifying the information that is useful and actionable. Emergency responders are largely interested in finding out what events are taking place so they can properly plan and deploy resources. In this paper we address the problem of automatically identifying important sub-events (within a large-scale emergency ``event'', such as a hurricane). In particular, we present a novel, unsupervised learning framework to detect sub-events in Tweets for retrospective crisis analysis. We first extract noun-verb pairs and phrases from raw tweets as sub-event candidates. Then, we learn a semantic embedding of extracted noun-verb pairs and phrases, and rank them against a crisis-specific ontology. We filter out noisy and irrelevant information then cluster the noun-verb pairs and phrases so that the top-ranked ones describe the most important sub-events. Through quantitative experiments on two large crisis data sets (Hurricane Harvey and the 2015 Nepal Earthquake), we demonstrate the effectiveness of our approach over the state-of-the-art. Our qualitative evaluation shows better performance compared to our baseline.