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

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%.Comment: 8 pages, Advances in Social Networks Analysis and Mining (ASONAM), 2017 IEEE/ACM International Conferenc

Identifying Depressive Disorder in the Twitter Population

Depression is a highly prevalent public health challenge and a major cause of disability across the globe. Annually 6.7% of Americans (that is, more than 16 million). Traditional approaches to curb depression involve survey·based methods via phone or online questionnaires. Large temporal gaps and cognitive bias. Social media provides a method for learning users\u27 feelings, emotions, behaviors, and decisions in real-time

Identifying Depressive Disorder in the Twitter Population

Depression is a highly prevalent public health challenge and a major cause of disability across the globe. Annually 6.7% of Americans (that is, more than 16 million). Traditional approaches to curb depression involve survey·based methods via phone or online questionnaires. Large temporal gaps and cognitive bias. Social media provides a method for learning users\u27 feelings, emotions, behaviors, and decisions in real-time