Lecture: Monitoring Clinical Depressive Symptoms in Social Media by Amir Yazdavar on 7/10/18

Monitoring Clinical Depressive Symptoms in Social Media

Brown Bag Lecture by Amir Yazdavar | 7/10/2018 11:00AM – 12PM | 7th Floor Conference Room, Bldg 38A

Abstract:

With the rise of social media, millions of people express their moods, feelings and daily struggles with mental health issues routinely 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 of 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.

Bio:

Amir is a 3rd year Ph.D. researcher at Kno.e.sis Research Center, OH under the guidance of  Dr. Amit P. Sheth and Dr. Krishnaprasad Thirunarayan (Kno.e.sis), and Dr. Jyotishman Pathak (Chief of Health Informatics at Weill Cornell Medicine). He is broadly interested in machine learning (incl. deep learning) and semantic web (incl. creation and use of knowledge graphs) and their applications to NLP/NLU and social media analytics. He has a particular interest in the extraction of subjective information with applications to search, social and biomedical/health applications. At Kno.e.sis Center – He is working on several real world projects mainly focused on studying human behavior on the web via Natural Language Understanding, Social Media Analytics utilizing Machine learning (Deep learning) and Knowledge Graph techniques. In particular, his focus is to enhance statistical models via domain semantics and guidance from offline behavioral knowledge to understand user’s behavior from unstructured and large-scale social data.

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