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

When:
Friday, July 7, 2017, 3:00 pm - 4:00 pm PDTiCal
Where:
6th Flr Conf Room-CR #689
This event is open to the public.
Type:
NL Seminar
Speaker:
Amir Hossein Yazdavar (Wright State University)
Description:

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. Un- like 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. Our proactive and automatic screening tool is able to identify clinical depressive symptoms with an accuracy of 68% and precision of 72%.

Bio: Amir is a 2nd year Ph.D. Researcher at Kno.e.sis Center Wright State University, OH under the guidance of Prof. Amit P. Sheth, the founder and executive director of Kno.e.sis Center. 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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