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Social Media for Mental Health: Data, Methods, and Findings

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Open Source Intelligence and Cyber Crime

Part of the book series: Lecture Notes in Social Networks ((LNSN))

Abstract

There is an increasing number of virtual communities and forums available on the web. With social media, people can freely communicate and share their thoughts, ask personal questions, and seek peer-support, especially those with conditions that are highly stigmatized, without revealing personal identity. We study the state-of-the-art research methodologies and findings on mental health challenges like depression, anxiety, suicidal thoughts, from the pervasive use of social media data. We also discuss how these novel thinking and approaches can help to raise awareness of mental health issues in an unprecedented way. Specifically, this chapter describes linguistic, visual, and emotional indicators expressed in user disclosures. The main goal of this chapter is to show how this new source of data can be tapped to improve medical practice, provide timely support, and influence government or policymakers. In the context of social media for mental health issues, this chapter categorizes social media data used, introduces different deployed machine learning, feature engineering, natural language processing, and surveys methods and outlines directions for future research.

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Notes

  1. 1.

    http://www.reddit.com/dev/api.

  2. 2.

    https://praw.readthedocs.org/en/latest/index.html.

  3. 3.

    https://au.reachout.com/.

  4. 4.

    https://www.instagram.com/developer/.

  5. 5.

    http://scikit-learn.org/stable/.

  6. 6.

    http://www.liwc.net/.

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Acknowledgements

We would like to thank all members of Data Mining Machine Learning Research Lab (DMML) at Arizona State University (ASU) for their constant support and feedback for this work. Special thanks to our lab members, Jundong Li, Matthew Davis, and Alex Nou for their detailed feedback on the earlier versions of this chapter. This work, in part, is supported by the Ministry of Higher Education Malaysia and University Malaysia Pahang (UMP).

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Authors and Affiliations

  1. School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA

    Nur Shazwani Kamarudin, Ghazaleh Beigi, Lydia Manikonda & Huan Liu

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  1. Nur Shazwani Kamarudin
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  2. Ghazaleh Beigi
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  3. Lydia Manikonda
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  4. Huan Liu
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Correspondence to Nur Shazwani Kamarudin .

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Editors and Affiliations

  1. School of Computing Science, Simon Fraser University, Burnaby, BC, Canada

    Mohammad A. Tayebi

  2. School of Computing Science, Simon Fraser University, Burnaby, BC, Canada

    Uwe Glässer

  3. School of Computing, Queen’s University, Kingston, ON, Canada

    David B. Skillicorn

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Kamarudin, N.S., Beigi, G., Manikonda, L., Liu, H. (2020). Social Media for Mental Health: Data, Methods, and Findings. In: Tayebi, M.A., Glässer, U., Skillicorn, D.B. (eds) Open Source Intelligence and Cyber Crime. Lecture Notes in Social Networks. Springer, Cham. https://doi.org/10.1007/978-3-030-41251-7_8

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