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    Automatically Infer Human Traits and Behavior from Social Media Data

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    article (246.5Kb)
    Links to Files
    https://arxiv.org/abs/1804.04191
    Permanent Link
    http://hdl.handle.net/11603/10728
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    • UMBC Faculty Collection
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    Author/Creator
    Pan, Shimei
    Ding, Tao
    Date
    2018
    Type of Work
    7 pages
    Text
    journal articles preprints
    Rights
    This item may be protected under Title 17 of the U.S. Copyright Law. It is made available by UMBC for non-commercial research and education. For permission to publish or reproduce, please contact the author.
    Subjects
    social media
    human behavior
    machine learning
    Abstract
    Given the complexity of human minds and their behavioral flexibility, it requires sophisticated data analysis to sift through a large amount of human behavioral evidence to model human minds and to predict human behavior. People currently spend a significant amount of time on social media such as Twitter and Facebook. Thus many aspects of their lives and behaviors have been digitally captured and continuously archived on these platforms. This makes social media a great source of large, rich and diverse human behavioral evidence. In this paper, we survey the recent work on applying machine learning to infer human traits and behavior from social media data. We will also point out several future research directions.


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    Albin O. Kuhn Library & Gallery
    University of Maryland, Baltimore County
    1000 Hilltop Circle
    Baltimore, MD 21250
    www.umbc.edu/scholarworks

    Contact information:
    Email: scholarworks-group@umbc.edu
    Phone: 410-455-3544


    If you wish to submit a copyright complaint or withdrawal request, please email mdsoar-help@umd.edu.