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

Author

Listed:
  • Rodrigo Martínez-Castaño

    (Centro Singular de Investigación en Tecnoloxías Intelixentes (CiTIUS), Universidade de Santiago de Compostela, 15705 Santiago de Compostela, Spain)

  • Juan C. Pichel

    (Centro Singular de Investigación en Tecnoloxías Intelixentes (CiTIUS), Universidade de Santiago de Compostela, 15705 Santiago de Compostela, Spain)

  • David E. Losada

    (Centro Singular de Investigación en Tecnoloxías Intelixentes (CiTIUS), Universidade de Santiago de Compostela, 15705 Santiago de Compostela, Spain)

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.

Suggested Citation

  • Rodrigo Martínez-Castaño & Juan C. Pichel & David E. Losada, 2020. "A Big Data Platform for Real Time Analysis of Signs of Depression in Social Media," IJERPH, MDPI, vol. 17(13), pages 1-23, July.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:13:p:4752-:d:379223
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    Cited by:

    1. Jiexiong Duan & Weixin Zhai & Chengqi Cheng, 2020. "Crowd Detection in Mass Gatherings Based on Social Media Data: A Case Study of the 2014 Shanghai New Year’s Eve Stampede," IJERPH, MDPI, vol. 17(22), pages 1-14, November.

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