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

    Fri, Jul 07, 2017 @ 03:00 PM - 04:00 PM

    Information Sciences Institute

    Conferences, Lectures, & Seminars

    Speaker: Amir Hossein Yazdavar, Wright State University

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

    Series: Natural Language Seminar

    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 PHQ9 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%.

    Biography: Amir is a 2nd year PhD Researcher at Knoesis Center Wright State University, OH under the guidance of Prof. Amit P. Sheth, the founder and executive director of Knoesis 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 Knoesis 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 users behavior from unstructured and large scale Social data.

    Host: Marjan Ghazvininejad and Kevin Knight

    More Info: http://nlg.isi.edu/nl-seminar/

    Location: Information Science Institute (ISI) - 6th Flr Conf Rm -# 689

    Audiences: Everyone Is Invited

    Contact: Peter Zamar

    Event Link: http://nlg.isi.edu/nl-seminar/

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