International Journal of Disaster Risk Reduction, 2020
This is a PDF file of an article that has undergone enhancements after acceptance, such as the ad... more This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
International Journal of Disaster Risk Reduction, 2020
Social media is increasingly used by people during large-scale natural disasters to request emerg... more 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.
International Journal of Disaster Risk Reduction, 2020
This is a PDF file of an article that has undergone enhancements after acceptance, such as the ad... more This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
International Journal of Disaster Risk Reduction, 2020
Social media is increasingly used by people during large-scale natural disasters to request emerg... more 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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Papers by Aman Dontula