<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Amir Hossein Yazdavar</style></author><author><style face="normal" font="default" size="100%">Mohammad Saeid Mahdavinejad</style></author><author><style face="normal" font="default" size="100%">Goonmeet Baja</style></author><author><style face="normal" font="default" size="100%">William Romine</style></author><author><style face="normal" font="default" size="100%">Amit Sheth</style></author><author><style face="normal" font="default" size="100%">Amir Hassan Monadjemi</style></author><author><style face="normal" font="default" size="100%">Krishnaprasad Thirunarayan</style></author><author><style face="normal" font="default" size="100%">John M. Meddar</style></author><author><style face="normal" font="default" size="100%">Annie Myers</style></author><author><style face="normal" font="default" size="100%">Jyotishman Pathak</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Multimodal mental health analysis in social media</style></title><secondary-title><style face="normal" font="default" size="100%">PLoS ONE</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Explainable Machine Learning</style></keyword><keyword><style  face="normal" font="default" size="100%">Hypothesis Testing</style></keyword><keyword><style  face="normal" font="default" size="100%">National Language Processing</style></keyword><keyword><style  face="normal" font="default" size="100%">Prediction</style></keyword><keyword><style  face="normal" font="default" size="100%">Regression</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2020</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0226248&amp;type=printable</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;p.p1 {margin: 0.0px 0.0px 0.0px 0.0px; font: 9.5px Helvetica}&lt;/p&gt;

&lt;p class=&quot;p1&quot;&gt;Depression is a major public health concern in the U.S. and globally. While successful early&lt;/p&gt;

&lt;p class=&quot;p1&quot;&gt;identification and treatment can lead to many positive health and behavioral outcomes,&lt;/p&gt;

&lt;p class=&quot;p1&quot;&gt;depression, remains undiagnosed, untreated or undertreated due to several reasons,&lt;/p&gt;

&lt;p class=&quot;p1&quot;&gt;including denial of the illness as well as cultural and social stigma. With the ubiquity of social&lt;/p&gt;

&lt;p class=&quot;p1&quot;&gt;media platforms, millions of people are now sharing their online persona by expressing their&lt;/p&gt;

&lt;p class=&quot;p1&quot;&gt;thoughts, moods, emotions, and even their daily struggles with mental health on social&lt;/p&gt;

&lt;p class=&quot;p1&quot;&gt;media. Unlike traditional observational cohort studies conducted through questionnaires&lt;/p&gt;

&lt;p class=&quot;p1&quot;&gt;and self-reported surveys, we explore the reliable detection of depressive symptoms from&lt;/p&gt;

&lt;p class=&quot;p1&quot;&gt;tweets obtained, unobtrusively. Particularly, we examine and exploit multimodal big (social)&lt;/p&gt;

&lt;p class=&quot;p1&quot;&gt;data to discern depressive behaviors using a wide variety of features including individuallevel&lt;/p&gt;

&lt;p class=&quot;p1&quot;&gt;demographics. By developing a multimodal framework and employing statistical techniques&lt;/p&gt;

&lt;p class=&quot;p1&quot;&gt;to fuse heterogeneous sets of features obtained through the processing of visual,&lt;/p&gt;

&lt;p class=&quot;p1&quot;&gt;textual, and user interaction data, we significantly enhance the current state-of-the-art&lt;/p&gt;

&lt;p class=&quot;p1&quot;&gt;approaches for identifying depressed individuals on Twitter (improving the average F1-&lt;/p&gt;

&lt;p class=&quot;p1&quot;&gt;Score by 5 percent) as well as facilitate demographic inferences from social media. Besides&lt;/p&gt;

&lt;p class=&quot;p1&quot;&gt;providing insights into the relationship between demographics and mental health, our&lt;/p&gt;

&lt;p class=&quot;p1&quot;&gt;research assists in the design of a new breed of demographic-aware health interventions.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Amir Hossein Yazdavar</style></author><author><style face="normal" font="default" size="100%">Mohammad Saied Mahdavinejad</style></author><author><style face="normal" font="default" size="100%">Goonmeet Bajaj</style></author><author><style face="normal" font="default" size="100%">Krishnaprasad Thirunarayan</style></author><author><style face="normal" font="default" size="100%">Jyotishman Pathak</style></author><author><style face="normal" font="default" size="100%">Amit Sheth</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Mental Health Analysis Via Social Media Data, IEEE ICHI 2018</style></title><secondary-title><style face="normal" font="default" size="100%">IEEE, ICHI</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2018</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Amir Hossein Yazdavar</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Hussein S. Al-Olimat</style></author><author><style face="normal" font="default" size="100%">Monireh Ebrahimi</style></author><author><style face="normal" font="default" size="100%">Goonmeet Bajaj</style></author><author><style face="normal" font="default" size="100%">Tanvi Banerjee</style></author><author><style face="normal" font="default" size="100%">Krishnaprasad Thirunarayan</style></author><author><style face="normal" font="default" size="100%">Jyotishman Pathak</style></author><author><style face="normal" font="default" size="100%">Amit Sheth</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Semi-Supervised Approach to Monitoring Clinical Depressive Symptoms in Social Media</style></title><secondary-title><style face="normal" font="default" size="100%">ASONAM</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2017</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://dl.acm.org/doi/abs/10.1145/3110025.3123028</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;&lt;span style=&quot;font-family: Merriweather, serif; font-size: 17px; background-color: rgb(250, 250, 250);&quot;&gt;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%.&lt;/span&gt;&lt;/p&gt;
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