ArticleThe Psychiatric quarterly2018
Online Communication about Depression and Anxiety among Twitter Users with Schizophrenia: Preliminary Findings to Inform a Digital Phenotype Using Social Media.
Article in The Psychiatric quarterly, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- A study on the coupling mechanism between the urban environment and depression perception based on deep learning and street view image.Scientific reports · 2026Article
- Are Treatment Services Ready for the Use of Big Data Analytics and AI in Managing Opioid Use Disorder?Journal of medical Internet research · 2025Article
- Leveraging social media data to study disease and treatment characteristics of Hodgkin's lymphoma Using Natural Language Processing methods.PLOS digital health · 2025Article
- Detecting and tracking depression through temporal topic modeling of tweets: insights from a 180-day study.Npj mental health research · 2024Article
- Natural Language Processing for Depression Prediction on Sina Weibo: Method Study and Analysis.JMIR mental health · 2024Article
- Using #ActuallyAutistic on Twitter for Precision Diagnosis of Autism Spectrum Disorder: Machine Learning Study.JMIR formative research · 2024Article
- Investigating COVID-19's Impact on Mental Health: Trend and Thematic Analysis of Reddit Users' Discourse.Journal of medical Internet research · 2023Article
- Sampling Twitter users for social science research: evidence from a systematic review of the literature.Quality & quantity · 2023Article
- Linking coastal environmental and health observations for human wellbeing.Frontiers in public health · 2023Article
- Social media and schizophrenia: An update on clinical applications.World journal of psychiatry · 2022Review
- Risks and Opportunities to Ensure Equity in the Application of Big Data Research in Public Health.Annual review of public health · 2022Review
- Assessing vulnerability to psychological distress during the COVID-19 pandemic through the analysis of microblogging content.Future generations computer systems : FGCS · 2021Article
- Exploring discussions of health and risk and public sentiment in Massachusetts during COVID-19 pandemic mandate implementation: A Twitter analysis.SSM - population health · 2021Article
- Association of "#covid19" Versus "#chinesevirus" With Anti-Asian Sentiments on Twitter: March 9-23, 2020.American journal of public health · 2021Article
- Utilizing Instagram Data to Identify Usage Patterns Associated With Schizophrenia Spectrum Disorders.Frontiers in psychiatry · 2021Article
- Article
- Social Media and Mental Health: Benefits, Risks, and Opportunities for Research and Practice.Journal of technology in behavioral science · 2020Article
- Racial and Ethnic Disparities in Patient Experiences in the United States: 4-Year Content Analysis of Twitter.Journal of medical Internet research · 2020Article
- The Digital Phenotyping Project: A Psychoanalytical and Network Theory Perspective.Frontiers in psychology · 2020Article
- A computational study of mental health awareness campaigns on social media.Translational behavioral medicine · 2019Article
Corrections and comments
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Authors and funding
4 authors.
Funding
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.
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