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        <title>Improving Classification of Twitter Behavior During Hurricane Events</title>
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        <namePart type="given">Kevin</namePart>
        <namePart type="family">Stowe</namePart>
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    <name type="personal">
        <namePart type="given">Jennings</namePart>
        <namePart type="family">Anderson</namePart>
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    <name type="personal">
        <namePart type="given">Martha</namePart>
        <namePart type="family">Palmer</namePart>
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    <name type="personal">
        <namePart type="given">Leysia</namePart>
        <namePart type="family">Palen</namePart>
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    <name type="personal">
        <namePart type="given">Ken</namePart>
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        <dateIssued>2018-07</dateIssued>
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            <title>Proceedings of the Sixth International Workshop on Natural Language Processing for Social Media</title>
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        <name type="personal">
            <namePart type="given">Lun-Wei</namePart>
            <namePart type="family">Ku</namePart>
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        <name type="personal">
            <namePart type="given">Cheng-Te</namePart>
            <namePart type="family">Li</namePart>
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            <publisher>Association for Computational Linguistics</publisher>
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    <abstract>A large amount of social media data is generated during natural disasters, and identifying the relevant portions of this data is critical for researchers attempting to understand human behavior, the effects of information sources, and preparatory actions undertaken during these events. In order to classify human behavior during hazard events, we employ machine learning for two tasks: identifying hurricane related tweets and classifying user evacuation behavior during hurricanes. We show that feature-based and deep learning methods provide different benefits for tweet classification, and ensemble-based methods using linguistic, temporal, and geospatial features can effectively classify user behavior.</abstract>
    <identifier type="citekey">stowe-etal-2018-improving</identifier>
    <identifier type="doi">10.18653/v1/W18-3512</identifier>
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        <date>2018-07</date>
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            <start>67</start>
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%0 Conference Proceedings
%T Improving Classification of Twitter Behavior During Hurricane Events
%A Stowe, Kevin
%A Anderson, Jennings
%A Palmer, Martha
%A Palen, Leysia
%A Anderson, Ken
%Y Ku, Lun-Wei
%Y Li, Cheng-Te
%S Proceedings of the Sixth International Workshop on Natural Language Processing for Social Media
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F stowe-etal-2018-improving
%X A large amount of social media data is generated during natural disasters, and identifying the relevant portions of this data is critical for researchers attempting to understand human behavior, the effects of information sources, and preparatory actions undertaken during these events. In order to classify human behavior during hazard events, we employ machine learning for two tasks: identifying hurricane related tweets and classifying user evacuation behavior during hurricanes. We show that feature-based and deep learning methods provide different benefits for tweet classification, and ensemble-based methods using linguistic, temporal, and geospatial features can effectively classify user behavior.
%R 10.18653/v1/W18-3512
%U https://aclanthology.org/W18-3512
%U https://doi.org/10.18653/v1/W18-3512
%P 67-75

Markdown (Informal)

[Improving Classification of Twitter Behavior During Hurricane Events](https://aclanthology.org/W18-3512) (Stowe et al., SocialNLP 2018)

• Improving Classification of Twitter Behavior During Hurricane Events (Stowe et al., SocialNLP 2018)

ACL

• Kevin Stowe, Jennings Anderson, Martha Palmer, Leysia Palen, and Ken Anderson. 2018. Improving Classification of Twitter Behavior During Hurricane Events. In Proceedings of the Sixth International Workshop on Natural Language Processing for Social Media, pages 67–75, Melbourne, Australia. Association for Computational Linguistics.