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Revealing Mental Disorders Through Stylometric Features in Write-Ups

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Mobile and Ubiquitous Systems: Computing, Networking and Services (MobiQuitous 2022)

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Abstract

Mental disorders present one of the leading causes of worldwide disability and have become a major social concern, as the symptoms behind mental disorders are almost hidden. Most of the conventional approaches used for diagnosing and identifying mental disorders rely on oral conversations (through interviews) having a limited focus on write-ups. Therefore, in this study, we attempt to explore identifying different types of mental disorders among people through their write-ups. To do so, we collect a total of 6893 posts and discussions that appeared in different problem-specific Internet forums and utilize them to identify different types of mental disorders. Leveraging appropriate machine learning algorithms over the collected write-ups, our study can categorize Depression, Schizophrenia, Suicidal Intention, Anxiety, Post Traumatic Stress Disorder (PTSD), Borderline Personality Disorder (BPD), and Eating Disorder (ED). To achieve a balanced dataset in the process of our study, we apply a combined sampling approach and achieve up to 89% accuracy in the identification task. We perform varied exploration tasks in our study covering 5-fold cross-validation, 5-times repetition on the used dataset, etc. We explain our findings in terms of precision, recall, specificity, and Matthews correlation coefficient to demonstrate the capability of our proposed approach in identifying mental disorders based on write-ups.

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Acknowledgement

The work has been conducted at and supported by the Bangladesh University of Engineering and Technology (BUET), Dhaka, Bangladesh.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Bangladesh University of Engineering and Technology, Dhaka, 1000, Bangladesh

    Tamanna Haque Nipa & A. B. M. Alim Al Islam

Authors
  1. Tamanna Haque Nipa
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  2. A. B. M. Alim Al Islam
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Correspondence to Tamanna Haque Nipa .

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Editors and Affiliations

  1. University of Pittsburgh, Pittsburgh, PA, USA

    Shangguan Longfei

  2. Microsoft Research, Redmond, WA, USA

    Priyantha Bodhi

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Nipa, T.H., Al Islam, A.B.M.A. (2023). Revealing Mental Disorders Through Stylometric Features in Write-Ups. In: Longfei, S., Bodhi, P. (eds) Mobile and Ubiquitous Systems: Computing, Networking and Services. MobiQuitous 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 492. Springer, Cham. https://doi.org/10.1007/978-3-031-34776-4_14

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  • DOI: https://doi.org/10.1007/978-3-031-34776-4_14

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  • Online ISBN: 978-3-031-34776-4

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