Detecting reports of mass emergency events on twitter

V. Pekar, J. Binner, H. Najafi, C. Hale, Vincent A. Schmidt

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    The paper addresses the problem of detecting eyewitness reports of mass emergencies on Twitter. This is the first work to conduct a large-scale comparative evaluation of classification features extracted from Twitter posts, using different learning algorithms and datasets representing a broad range of mass emergencies including both natural and technological disasters. We investigate the relative importance of different feature types as well as on the effect of several feature selection methods applied to this problem. Because the task of detecting mass emergencies is characterized by high heterogeneity of the data, our primary focus is on identifying those features that are capable of separating mass emergency reports from other messages, irrespective of the type of the disaster.

    Original languageEnglish
    Title of host publicationProceedings of the 2016 International Conference on Artificial Intelligence, ICAI 2016 - WORLDCOMP 2016
    EditorsHamid R. Arabnia, David de la Fuente, Roger Dziegiel, Elena B. Kozerenko, Peter M. LaMonica, Raymond A. Liuzzi, Jose A. Olivas, Todd Waskiewicz, George Jandieri, Ashu M.G. Solo, Fernando G. Tinetti
    PublisherCSREA Press
    Pages137-143
    Number of pages7
    ISBN (Electronic)1601324383, 9781601324382
    Publication statusPublished - 2016
    Event2016 International Conference on Artificial Intelligence, ICAI 2016 - WORLDCOMP 2016 - Las Vegas, United States
    Duration: 25 Jul 201628 Jul 2016

    Publication series

    NameProceedings of the 2016 International Conference on Artificial Intelligence, ICAI 2016 - WORLDCOMP 2016

    Conference

    Conference2016 International Conference on Artificial Intelligence, ICAI 2016 - WORLDCOMP 2016
    Country/TerritoryUnited States
    CityLas Vegas
    Period25/07/1628/07/16

    Keywords

    • Disaster management
    • Machine learning
    • Risk assessment
    • Social media analysis
    • Text classification

    ASJC Scopus subject areas

    • Software
    • Artificial Intelligence

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