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

Centro Singular de Investigación en Tecnoloxías Intelixentes (CiTIUS), Universidade de Santiago de Compostela, 15705 Santiago de Compostela, Spain
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Int. J. Environ. Res. Public Health 2020, 17(13), 4752; https://doi.org/10.3390/ijerph17134752
Received: 28 May 2020 / Revised: 10 June 2020 / Accepted: 25 June 2020 / Published: 1 July 2020
(This article belongs to the Special Issue Social Media Intelligence for Public Health Surveillance)
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. View Full-Text
Keywords: Social Media; text mining; depression; public health surveillance; stream processing; real-time processing Social Media; text mining; depression; public health surveillance; stream processing; real-time processing
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MDPI and ACS Style

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. https://doi.org/10.3390/ijerph17134752

AMA Style

Martínez-Castaño R, Pichel JC, Losada  DE. A Big Data Platform for Real Time Analysis of Signs of Depression in Social Media. International Journal of Environmental Research and Public Health. 2020; 17(13):4752. https://doi.org/10.3390/ijerph17134752

Chicago/Turabian Style

Martínez-Castaño, Rodrigo; Pichel, Juan C.; Losada , David E. 2020. "A Big Data Platform for Real Time Analysis of Signs of Depression in Social Media" Int. J. Environ. Res. Public Health 17, no. 13: 4752. https://doi.org/10.3390/ijerph17134752

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