01762nas a2200181   4500008004100000245008800041210006900129520113900198100002801337700002701365700002201392700002001414700002001434700003201454700002301486700001601509856005501525       2017                        eng d00aSemi-Supervised Approach to Monitoring Clinical Depressive Symptoms in Social Media0 aSemiSupervised Approach to Monitoring Clinical Depressive Sympto3 a<p><span style="font-family: Merriweather, serif; font-size: 17px; background-color: rgb(250, 250, 250);">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%.</span></p>
1 aYazdavar, Amir, Hossein1 aAl-Olimat, Hussein, S.1 aEbrahimi, Monireh1 aBajaj, Goonmeet1 aBanerjee, Tanvi1 aThirunarayan, Krishnaprasad1 aPathak, Jyotishman1 aSheth, Amit  uhttps://dl.acm.org/doi/abs/10.1145/3110025.3123028