Unsupervised Detection of Sub-Events in Large Scale Disasters
| Autor: | Manas Gaur, Chidubem Arachie, Alejandro Jaimes, William Groves, Sam Anzaroot, Ke Zhang |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Social and Information Networks (cs.SI)
FOS: Computer and information sciences Computer Science - Machine Learning Information retrieval Computer science Event (computing) InformationSystems_INFORMATIONSTORAGEANDRETRIEVAL Rank (computer programming) Machine Learning (stat.ML) Computer Science - Social and Information Networks 020206 networking & telecommunications 02 engineering and technology General Medicine Ontology (information science) Machine Learning (cs.LG) Statistics - Machine Learning 0202 electrical engineering electronic engineering information engineering Unsupervised learning Social media Natural disaster Scale (map) Baseline (configuration management) |
| Zdroj: | AAAI |
| ISSN: | 2374-3468 2159-5399 |
| Popis: | 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. AAAI-20 Social Impact Track |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|