02855nas a2200277 4500008004100000245005400041210005400095520195300149653003302102653002302135653003302158653001502191653001502206100002802221700003402249700001902283700002002302700001602322700002802338700003202366700002102398700001702419700002302436700002002459856009802479 2020 eng d00aMultimodal mental health analysis in social media0 aMultimodal mental health analysis in social media3 a

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Depression is a major public health concern in the U.S. and globally. While successful early

identification and treatment can lead to many positive health and behavioral outcomes,

depression, remains undiagnosed, untreated or undertreated due to several reasons,

including denial of the illness as well as cultural and social stigma. With the ubiquity of social

media platforms, millions of people are now sharing their online persona by expressing their

thoughts, moods, emotions, and even their daily struggles with mental health on social

media. Unlike traditional observational cohort studies conducted through questionnaires

and self-reported surveys, we explore the reliable detection of depressive symptoms from

tweets obtained, unobtrusively. Particularly, we examine and exploit multimodal big (social)

data to discern depressive behaviors using a wide variety of features including individuallevel

demographics. By developing a multimodal framework and employing statistical techniques

to fuse heterogeneous sets of features obtained through the processing of visual,

textual, and user interaction data, we significantly enhance the current state-of-the-art

approaches for identifying depressed individuals on Twitter (improving the average F1-

Score by 5 percent) as well as facilitate demographic inferences from social media. Besides

providing insights into the relationship between demographics and mental health, our

research assists in the design of a new breed of demographic-aware health interventions.

10aExplainable Machine Learning10aHypothesis Testing10aNational Language Processing10aPrediction10aRegression1 aYazdavar, Amir, Hossein1 aMahdavinejad, Mohammad, Saeid1 aBaja, Goonmeet1 aRomine, William1 aSheth, Amit1 aMonadjemi, Amir, Hassan1 aThirunarayan, Krishnaprasad1 aMeddar, John, M.1 aMyers, Annie1 aPathak, Jyotishman1 aHitzler, Pascal uhttps://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0226248&type=printable00569nas a2200145 4500008004100000245006500041210006400106100002800170700003400198700002000232700003200252700002300284700001600307856010000323 2018 eng d00aMental Health Analysis Via Social Media Data, IEEE ICHI 20180 aMental Health Analysis Via Social Media Data IEEE ICHI 20181 aYazdavar, Amir, Hossein1 aMahdavinejad, Mohammad, Saied1 aBajaj, Goonmeet1 aThirunarayan, Krishnaprasad1 aPathak, Jyotishman1 aSheth, Amit uhttps://daselab.cs.ksu.edu/publications/mental-health-analysis-social-media-data-ieee-ichi-201801762nas a2200181 4500008004100000245008800041210006900129520113900198100002801337700002701365700002201392700002001414700002001434700003201454700002301486700001601509856005501525 2017 eng d00aSemi-Supervised Approach to Monitoring Clinical Depressive Symptoms in Social Media0 aSemiSupervised Approach to Monitoring Clinical Depressive Sympto3 a

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%.

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