Unsupervised Detection of Sub-events in Large Scale Disasters

TL;DR

This paper introduces an unsupervised framework for detecting and ranking sub-events in social media posts during large-scale disasters, aiding emergency response efforts by identifying critical incident details.

Contribution

It proposes a novel unsupervised method that extracts, embeds, filters, and clusters sub-event candidates from tweets, improving over existing approaches for crisis analysis.

Findings

Effective detection of sub-events in crisis data sets

Outperforms state-of-the-art methods

Validated on Hurricane Harvey and Nepal Earthquake data

Abstract

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.