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=printable02649nas a2200277 4500008004100000245009100041210006900132260003400201300001400235490000900249520174100258653003601999653001902035653003002054653003702084653002202121653002002143100002102163700002502184700002002209700001602229700003202245700001902277700002402296856005102320 2010 eng d00aProvenance Context Entity (PaCE): Scalable Provenance Tracking for Scientific RDF Data0 aProvenance Context Entity PaCE Scalable Provenance Tracking for  aHeidelberg, GermanybSpringer a461–4700 v61873 a

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. 

10aBiomedical knowledge repository10aContext theory10aProvenance context entity10aProvenance Management Framework.10aProvenir ontology10aRDF reification1 aSahoo, Satya, S.1 aBodenreider, Olivier1 aHitzler, Pascal1 aSheth, Amit1 aThirunarayan, Krishnaprasad1 aGertz, Michael1 aLudäscher, Bertram uhttp://dx.doi.org/10.1007/978-3-642-13818-8_32