A Big Data Platform for Real Time Analysis of Signs of Depression in Social Media

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URI: http://hdl.handle.net/10347/23770
E-ISSN: 1660-4601
DOI: 10.3390/ijerph17134752

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2020

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Abstract

In this paper we propose a scalable platform for real-time processing of Social Media data. The platform ingests huge amounts of contents, such as Social Media posts or comments, and can support Public Health surveillance tasks. The processing and analytical needs of multiple screening tasks can easily be handled by incorporating user-defined execution graphs. The design is modular and supports different processing elements, such as crawlers to extract relevant contents or classifiers to categorise Social Media. We describe here an implementation of a use case built on the platform that monitors Social Media users and detects early signs of depression

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Social Media| Text mining| Depression| Public health surveillance| Stream processing| Real-time processing

Bibliographic citation

Martínez-Castaño, R.; Pichel, J.C.; Losada , D.E. A Big Data Platform for Real Time Analysis of Signs of Depression in Social Media. Int. J. Environ. Res. Public Health 2020, 17, 4752

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This work was funded by FEDER/Ministerio de Ciencia, Innovación y Universidades—Agencia Estatal de Investigación/ Project (RTI2018-093336-B-C21). Our research also receives financial support from the Consellería de Educación, Universidade e Formación Profesional (accreditation 2019–2022 ED431G-2019/04, ED431C 2018/29, ED431C 2018/19) and the European Regional Development Fund (ERDF), which acknowledges the CiTIUS-Research Center in Intelligent Technologies of the University of Santiago de Compostela as a Research Center of the Galician University System

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© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/)
Atribución 4.0 Internacional

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Electrónica e Computación
Centro de Investigación en Tecnoloxías Intelixentes da USC (CiTIUS)