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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    Abstrakt

    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.
    OriginalsprogEngelsk
    TitelProceedings. 2017 IEEE International Conference on Big Data : IEEE Big Data 2017
    RedaktørerJian-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
    Antal sider10
    UdgivelsesstedLos Alamitos, CA
    ForlagIEEE
    Publikationsdato2017
    Sider4343-4352
    ISBN (Trykt)9781538627167
    ISBN (Elektronisk)9781538627150, 9781538627143
    DOI
    StatusUdgivet - 2017
    Begivenhed2017 IEEE International Conference on Big Data - Boston, USA
    Varighed: 11 dec. 201714 dec. 2017
    Konferencens nummer: 5
    http://cci.drexel.edu/bigdata/bigdata2017/

    Konference

    Konference2017 IEEE International Conference on Big Data
    Nummer5
    LandUSA
    ByBoston
    Periode11/12/201714/12/2017
    Internetadresse

    Emneord

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

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