Identifying emergency stages in Facebook posts of police departments with convolutional and recurrent neural networks and support vector machines

Classification of social media posts in emergency response is an important practical problem: accurate classification can help automate processing of such messages and help other responders and the public react to emergencies in a timely fashion. This research focused on classifying Facebook message...

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Bibliographic Details
Published in:arXiv.org 2018-01
Main Authors: Pogrebnyakov, Nicolai, Maldonado, Edgar
Format: Article
Language:English
Subjects:
Classification
Classifiers
Digital media
Emergency management
Emergency preparedness
Emergency response
Messages
Neural networks
Police
Recurrent neural networks
Social networks
Support vector machines
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Summary:Classification of social media posts in emergency response is an important practical problem: accurate classification can help automate processing of such messages and help other responders and the public react to emergencies in a timely fashion. This research focused on classifying Facebook messages of US police departments. Randomly selected 5,000 messages were used to train classifiers that distinguished between four categories of messages: emergency preparedness, response and recovery, as well as general engagement messages. Features were represented with bag-of-words and word2vec, and models were constructed using support vector machines (SVMs) and convolutional (CNNs) and recurrent neural networks (RNNs). The best performing classifier was an RNN with a custom-trained word2vec model to represent features, which achieved the F1 measure of 0.839.
ISSN:2331-8422
DOI:10.48550/arxiv.1801.00801