Selecting classification features for detection of mass emergencies on social media

Viktor Pekar, Jane Binner, Hossein Najafi, Christopher Hale

    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 Security and Management
    Subtitle of host publicationSAM'16
    Place of PublicationLas Vegas, NV
    PublisherCSREA Press
    Number of pages7
    Publication statusAccepted/In press - 10 May 2016
    EventThe 2016 International Conference on Security and Management : (SAM'16) - Nevada, Las Vegas, United States
    Duration: 25 Jul 201628 Jul 2016

    Conference

    ConferenceThe 2016 International Conference on Security and Management
    Country/TerritoryUnited States
    CityLas Vegas
    Period25/07/1628/07/16

    Keywords

    • text classification
    • machine learning
    • feature selection
    • social media
    • social media analysis
    • disaster management

    ASJC Scopus subject areas

    • Artificial Intelligence
    • Safety Research

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