RT Journal Article
T1 A Big Data Platform for Real Time Analysis of Signs of Depression in Social Media
A1 Martínez Castaño, Rodrigo
A1 Pichel Campos, Juan Carlos
A1 Losada Carril, David Enrique
K1 Social Media
K1 Text mining
K1 Depression
K1 Public health surveillance
K1 Stream processing
K1 Real-time processing
AB 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
PB MDPI
YR 2020
FD 2020
LK http://hdl.handle.net/10347/23770
UL http://hdl.handle.net/10347/23770
LA eng
NO 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
NO 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
DS Minerva
RD 25 abr 2026