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Manas Gaur

Researcher at University of South Carolina

Publications -  79
Citations -  952

Manas Gaur is an academic researcher from University of South Carolina. The author has contributed to research in topics: Computer science & Social media. The author has an hindex of 14, co-authored 57 publications receiving 509 citations. Previous affiliations of Manas Gaur include Delhi Technological University & Wright State University.

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Book ChapterDOI

Predictive Analysis on Twitter: Techniques and Applications

TL;DR: This chapter presents fine-grained analysis involving aspects such as sentiment, emotion, and the use of domain knowledge in the coarse- grained analysis of Twitter data for making decisions and taking actions, and relates a few success stories.
Proceedings ArticleDOI

Knowledge-aware Assessment of Severity of Suicide Risk for Early Intervention

TL;DR: In this paper, a 5-label classification scheme was proposed for Reddit users with suicidal ideation, behavior, or attempt. But, the proposed scheme was based on the Columbia Suicide Severity Rating Scale (C-SSRS).
Proceedings ArticleDOI

"Let Me Tell You About Your Mental Health!": Contextualized Classification of Reddit Posts to DSM-5 for Web-based Intervention

TL;DR: A novel approach to map each subreddit to the best matching DSM-5 (Diagnostic and Statistical Manual of Mental Disorders - 5th Edition) category using multi-class classifier and a detailed analysis of the nature of subreddit content from domain expert's perspective is provided.

Knowledge-aware Assessment of Severity of Suicide Risk for Early Intervention

TL;DR: This interdisciplinary study concerns the use of Reddit as an unobtrusive data source for gleaning information about suicidal tendencies and other related mental health conditions afflicting depressed users and develops a suicide risk severity lexicon using medical knowledge bases and suicide ontology to detect cues relevant to suicidal thoughts and actions.
Journal ArticleDOI

Semantics of the Black-Box: Can Knowledge Graphs Help Make Deep Learning Systems More Interpretable and Explainable?

TL;DR: This article demonstrates how knowledge, provided as a knowledge graph, is incorporated into DL using K- iL, and discusses the utility of K-iL towards interpretability and explainability.