Semi-Supervised Approach to Monitoring Clinical Depressive Symptoms in Social Media.

Journal: Proceedings of the ... IEEE/ACM International Conference on Advances in Social Network Analysis and Mining. International Conference on Advances in Social Network Analysis and Mining
Published Date: Jul 31, 2017

Abstract

With the rise of social media, millions of people are routinely expressing their moods, feelings, and daily struggles with mental health issues on social media platforms like Twitter. Unlike traditional observational cohort studies conducted through questionnaires and self-reported surveys, we explore the reliable detection of clinical depression from tweets obtained unobtrusively. Based on the analysis of tweets crawled from users with self-reported depressive symptoms in their Twitter profiles, we demonstrate the potential for detecting clinical depression symptoms which emulate the PHQ-9 questionnaire clinicians use today. Our study uses a semi-supervised statistical model to evaluate how the duration of these symptoms and their expression on Twitter (in terms of word usage patterns and topical preferences) align with the medical findings reported via the PHQ-9. Our proactive and automatic screening tool is able to identify clinical depressive symptoms with an accuracy of 68% and precision of 72%.

Authors

  • Amir Hossein Yazdavar
    Kno.e.sis Center, Wright State University, Dayton, OH, USA.
  • Hussein S Al-Olimat
    Kno.e.sis Center, Wright State University, Dayton, OH, USA.
  • Monireh Ebrahimi
    Kno.e.sis Center, Wright State University, Dayton, OH, USA.
  • Goonmeet Bajaj
    Kno.e.sis Center, Wright State University, Dayton, OH, USA.
  • Tanvi Banerjee
    Department of Computer Science and Engineering, Wright State University, Dayton, OH, USA.
  • Krishnaprasad Thirunarayan
    Kno.e.sis Center, Wright State University, Dayton, Ohio, USA.
  • Jyotishman Pathak
    Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA.
  • Amit Sheth
    AI Institute, University of South Carolina, Columbia, SC, United States.

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