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VLDB Workshop on Data Management and Analytics for Medicine and Healthcare

DMAH 2017: Data Management and Analytics for Medicine and Healthcare pp 55–70Cite as

Social Media Mining to Understand Public Mental Health

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10494))

Abstract

In this paper, we apply text mining and topic modelling to understand public mental health. We focus on identifying common mental health topics across two anonymous social media platforms: Reddit and a mobile journalling/mood-tracking app. Furthermore, we analyze journals from the app to uncover relationships between topics, journal visibility (private vs. visible to other users of the app), and user-labelled sentiment. Our main findings are that (1) anxiety and depression are shared on both platforms; (2) users of the journalling app keep routine topics such as eating private, and these topics rarely appear on Reddit; and (3) sleep was a critical theme on the journalling app and had an unexpectedly negative sentiment.

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Notes

  1. 1.

    We manually inspected a sample of short public journals and found that those under 20 characters long typically re-stated the mood of the user and did not refer to any specific topic.

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Author information

Authors and Affiliations

  1. University of Waterloo, Waterloo, ON, N2L 3G1, Canada

    Andrew Toulis & Lukasz Golab

Authors
  1. Andrew Toulis
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  2. Lukasz Golab
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Corresponding author

Correspondence to Lukasz Golab .

Editor information

Editors and Affiliations

  1. Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA

    Edmon Begoli

  2. Stony Brook University, Stony Brook, New York, USA

    Fusheng Wang

  3. University of Washington, Seattle, Washington, USA

    Gang Luo

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© 2017 Springer International Publishing AG

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Toulis, A., Golab, L. (2017). Social Media Mining to Understand Public Mental Health. In: Begoli, E., Wang, F., Luo, G. (eds) Data Management and Analytics for Medicine and Healthcare. DMAH 2017. Lecture Notes in Computer Science(), vol 10494. Springer, Cham. https://doi.org/10.1007/978-3-319-67186-4_6

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  • DOI: https://doi.org/10.1007/978-3-319-67186-4_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-67185-7

  • Online ISBN: 978-3-319-67186-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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