Identifying Emergency Stages in Facebook Posts of Police Departments with Convolutional and Recurrent Neural Networks and Support Vector Machines

Nicolai Pogrebnyakov, Edgar Maldonado

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    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.
    Original languageEnglish
    Title of host publicationProceedings. 2017 IEEE International Conference on Big Data : IEEE Big Data 2017
    EditorsJian-Yun Nie, Zoran Obradovic, Toyotaro Suzumura, Rumi Ghosh, Raghunath Nambiar, Chonggang Wang, Hui Zang, Ricardo Baeza-Yates, Xiaohua Hu, Jeremy Kepner, Alfredo Cuzzocrea, Jian Tang, Masashi Toyoda
    Number of pages10
    Place of PublicationLos Alamitos, CA
    Publication date2017
    ISBN (Print)9781538627167
    ISBN (Electronic)9781538627150, 9781538627143
    Publication statusPublished - 2017
    EventFifth IEEE International Conference on Big Data. IEEE BigData 2017 - Boston, United States
    Duration: 11 Dec 201714 Dec 2017
    Conference number: 5


    ConferenceFifth IEEE International Conference on Big Data. IEEE BigData 2017
    CountryUnited States
    Internet address


    • Social media
    • Classification
    • Police
    • Support vector machines
    • Neural networks

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