%0 Journal Article %J PLoS ONE %D 2020 %T Multimodal mental health analysis in social media %A Amir Hossein Yazdavar %A Mohammad Saeid Mahdavinejad %A Goonmeet Baja %A William Romine %A Amit Sheth %A Amir Hassan Monadjemi %A Krishnaprasad Thirunarayan %A John M. Meddar %A Annie Myers %A Jyotishman Pathak %A Pascal Hitzler %K Explainable Machine Learning %K Hypothesis Testing %K National Language Processing %K Prediction %K Regression %X

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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.

%B PLoS ONE %G eng %U https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0226248&type=printable %0 Conference Paper %B Scientific and Statistical Database Management, 22nd International Conference, SSDBM 2010 %D 2010 %T Provenance Context Entity (PaCE): Scalable Provenance Tracking for Scientific RDF Data %A Satya S. Sahoo %A Olivier Bodenreider %A Pascal Hitzler %A Amit Sheth %A Krishnaprasad Thirunarayan %E Michael Gertz %E Bertram Ludäscher %K Biomedical knowledge repository %K Context theory %K Provenance context entity %K Provenance Management Framework. %K Provenir ontology %K RDF reification %X

The Semantic Web Resource Description Framework (RDF) format is being used by a large number of scientific applications to store and disseminate their datasets. The provenance information, describing the source or lineage of the datasets, is playing an increasingly significant role in ensuring data quality, computing trust value of the datasets, and ranking query results. Current Semantic Web provenance tracking approaches using the RDF reification vocabulary suffer from a number of known issues, including lack of formal semantics, use of blank nodes, and application-dependent interpretation of reified RDF triples that hinders data sharing. In this paper, we introduce a new approach called Provenance Context Entity (PaCE) that uses the notion of provenance context to create provenance-aware RDF triples without the use of RDF reification or blank nodes. We also define the formal semantics of PaCE through a simple extension of the existing RDF(S) semantics that ensures compatibility of PaCE with existing Semantic Web tools and implementations. We have implemented the PaCE approach in the Biomedical Knowledge Repository (BKR) project at the US National Library of Medicine to support provenance tracking on RDF data extracted from multiple sources, including biomedical literature and the UMLS Metathesaurus. The evaluations demonstrate a minimum of 49% reduction in total number of provenancespecific RDF triples generated using the PaCE approach as compared to RDF reification. In addition, using the PACE approach improves the performance of complex provenance queries by three orders of magnitude and remains comparable to the RDF reification approach for simpler provenance queries. 

%B Scientific and Statistical Database Management, 22nd International Conference, SSDBM 2010 %I Springer %C Heidelberg, Germany %V 6187 %P 461–470 %G eng %U http://dx.doi.org/10.1007/978-3-642-13818-8_32 %R 10.1007/978-3-642-13818-8_32