期刊
INTERNATIONAL JOURNAL OF DISASTER RISK REDUCTION
卷 51, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.ijdrr.2020.101757
关键词
Text classification; Deep learning; Social media analysis; Disaster response; 2010 MSC; 00-01; 99-00
资金
- U.S. National Science Foundation [1760453, 1902460]
- Direct For Computer & Info Scie & Enginr
- Division Of Computer and Network Systems [1760453] Funding Source: National Science Foundation
- Div Of Civil, Mechanical, & Manufact Inn
- Directorate For Engineering [1902460] Funding Source: National Science Foundation
Social media is increasingly used by people during large-scale natural disasters to request emergency help. Previous work has had success in applying machine-learning classifiers to detect tweets in coarse-grained categories, such as disaster type and relevance. However, there is a dearth of work that focuses on detecting tweets containing requests for help that are actionable by first responders. Using over 5 million tweets posted during 2017's Hurricane Harvey in Houston, U.S., we show that though such requests are uncommon, their often life-or death nature justifies the development of tweet classifiers to detect them. We find that the best-performing classifiers are a convolutional neural network (CNN) trained on word embeddings, support vector machine (SVM) trained on average word embeddings, and multilayer perceptron (MLP) trained on a combination of unigrams and part-of-speech (POS) tags. These models achieve F1 scores of over 0.86, confirming their efficacy in detecting urgent tweets. We highlight the utility of average word embeddings for training non-neural models, and that such features produce results competitive with more traditional n-gram and POS features.
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