@article {775, title = {Multimodal mental health analysis in social media}, journal = {PLoS ONE}, year = {2020}, abstract = {

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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.

}, keywords = {Explainable Machine Learning, Hypothesis Testing, National Language Processing, Prediction, Regression}, url = {https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0226248\&type=printable}, author = {Amir Hossein Yazdavar and Mohammad Saeid Mahdavinejad and Goonmeet Baja and William Romine and Amit Sheth and Amir Hassan Monadjemi and Krishnaprasad Thirunarayan and John M. Meddar and Annie Myers and Jyotishman Pathak and Pascal Hitzler} } @conference {777, title = {Mental Health Analysis Via Social Media Data, IEEE ICHI 2018}, booktitle = {IEEE, ICHI}, year = {2018}, author = {Amir Hossein Yazdavar and Mohammad Saied Mahdavinejad and Goonmeet Bajaj and Krishnaprasad Thirunarayan and Jyotishman Pathak and Amit Sheth} } @article {779, title = {Challenges of Sentiment Analysis for Dynamic Events}, year = {2017}, abstract = {

Efforts to assess people{\textquoteright}s sentiments on Twitter have suggested that Twitter could be a valuable resource for studying political sentiment and that it reflects the offline political landscape. Many opinion mining systems and tools provide users with people{\textquoteright}s attitudes toward products, people, or topics and their attributes/aspects. However, although it may appear simple, using sentiment analysis to predict election results is difficult, since it is empirically challenging to train a successful model to conduct sentiment analysis on tweet streams for a dynamic event such as an election. This article highlights some of the challenges related to sentiment analysis encountered during monitoring of the presidential election using Kno.e.sis{\textquoteright}s Twitris system.

}, author = {Monireh Ebrahimi and Amir Hossein Yazdavar and Amit Sheth} } @conference {778, title = {Relatedness-based Multi-Entity Summarization}, booktitle = {IJCAI}, year = {2017}, abstract = {

Representing world knowledge in a machine processable format is important as entities and their descriptions have fueled tremendous growth in knowledge-rich information processing platforms, services, and systems. Prominent applications of knowledge graphs include search engines (e.g., Google Search and Microsoft Bing), email clients (e.g., Gmail), and intelligent personal assistants (e.g., Google Now, Amazon Echo, and Apple{\textquoteright}s Siri). In this paper, we present an approach that can summarize facts about a collection of entities by analyzing their relatedness in preference to summarizing each entity in isolation. Specifically, we generate informative entity summaries by selecting: (i) inter-entity facts that are similar and (ii) intra-entity facts that are important and diverse. We employ a constrained knapsack problem solving approach to efficiently compute entity summaries. We perform both qualitative and quantitative experiments and demonstrate that our approach yields promising results compared to two other stand-alone state-ofthe-art entity summarization approaches.

}, author = {Kalpa Gunaratna and Amir Hossein Yazdavar and Krishnaprasad Thirunarayan and Amit Sheth and Gong Cheng} } @conference {776, title = {Semi-Supervised Approach to Monitoring Clinical Depressive Symptoms in Social Media}, booktitle = {ASONAM}, year = {2017}, abstract = {

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\%.

}, url = {https://dl.acm.org/doi/abs/10.1145/3110025.3123028}, author = {Amir Hossein Yazdavar}, editor = {Hussein S. Al-Olimat and Monireh Ebrahimi and Goonmeet Bajaj and Tanvi Banerjee and Krishnaprasad Thirunarayan and Jyotishman Pathak and Amit Sheth} }