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    Feeling anxious? Perceiving anxiety in tweets using machine learning


    Gruda, Jon and Hasan, Souleiman (2019) Feeling anxious? Perceiving anxiety in tweets using machine learning. Computers in Human Behavior, 98. pp. 245-255. ISSN 0747-5632

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    Abstract

    This study provides a predictive measurement tool to examine perceived anxiety from a longitudinal perspective, using a non-intrusive machine learning approach to scale human rating of anxiety in microblogs. Results suggest that our chosen machine learning approach depicts perceived user state-anxiety fluctuations over time, as well as mean trait anxiety. We further find a reverse relationship between perceived anxiety and outcomes such as social engagement and popularity. Implications on the individual, organizational, and societal levels are discussed.

    Item Type: Article
    Keywords: Anxiety; Machine learning; Twitter; Micro-blog; Health;
    Academic Unit: Faculty of Social Sciences > School of Business
    Item ID: 11261
    Identification Number: https://doi.org/10.1016/j.chb.2019.04.020
    Depositing User: Jon Gruda
    Date Deposited: 14 Oct 2019 16:11
    Journal or Publication Title: Computers in Human Behavior
    Publisher: Elsevier
    Refereed: Yes
    URI:

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