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Psychiatric Quarterly

, Volume 89, Issue 3, pp 569–580 | Cite as

Online Communication about Depression and Anxiety among Twitter Users with Schizophrenia: Preliminary Findings to Inform a Digital Phenotype Using Social Media

  • Yulin HswenEmail author
  • John A. Naslund
  • John S. Brownstein
  • Jared B. Hawkins
Original Paper

Abstract

Digital technologies hold promise for supporting the detection and management of schizophrenia. This exploratory study aimed to generate an initial understanding of whether patterns of communication about depression and anxiety on popular social media among individuals with schizophrenia are consistent with offline representations of the illness. From January to July 2016, posts on Twitter were collected from a sample of Twitter users who self-identify as having a schizophrenia spectrum disorder (n = 203) and a randomly selected sample of control users (n = 173). Frequency and timing of communication about depression and anxiety were compared between groups. In total, the groups posted n = 1,544,122 tweets and users had similar characteristics. Twitter users with schizophrenia showed significantly greater odds of tweeting about depression compared with control users (OR = 2.69; 95% CI 1.76–4.10), and significantly greater odds of tweeting about anxiety compared with control users (OR = 1.81; 95% CI 1.20–2.73). This study offers preliminary insights that Twitter users with schizophrenia may express elevated symptoms of depression and anxiety in their online posts, which is consistent with clinical characteristics of schizophrenia observed in offline settings. Social media platforms could further our understanding of schizophrenia by informing a digital phenotype and may afford new opportunities to support early illness detection.

Keywords

Schizophrenia Depression Anxiety Mental health Technology Social media Digital phenotype 

Notes

Funding

This study was supported by the Computational Epidemiology Group at Boston Children’s Hospital. YH reports receiving funding from the Canadian Institutes of Health Research and the Robert Wood Johnson Foundation (Grant 73495). JSB reports receiving funding from the National Institutes of Health, National Library of Medicine (R01LM010812) and from the Bill & Melinda Gates Foundation (OPP1093011). JBH reports receiving funding from the National Library of Medicine (T15LM007092) and the Robert Wood Johnson Foundation (Grant 73495). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The authors report no competing interests.

Compliance with Ethical Standards

Conflict of Interest

No financial disclosures were reported by any of the authors of this manuscript. The authors report no conflicts of interest.

Ethical Approval

This study was considered exempt from ethical review because only publicly available online data collected from the Twitter platform was analyzed in this study.

Informed Consent

No human subjects were recruited in this study; therefore, informed consent was not necessary.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Yulin Hswen
    • 1
    • 2
    Email author
  • John A. Naslund
    • 3
  • John S. Brownstein
    • 2
    • 4
    • 5
  • Jared B. Hawkins
    • 2
    • 4
  1. 1.Department of Social and Behavioral SciencesHarvard T.H. Chan School of Public HealthBostonUSA
  2. 2.Computational Epidemiology GroupBoston Children’s HospitalBostonUSA
  3. 3.Department of Global Health and Social MedicineHarvard Medical SchoolBostonUSA
  4. 4.Department of PediatricsHarvard Medical SchoolBostonUSA
  5. 5.Department of Biomedical InformaticsHarvard Medical SchoolBostonUSA

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