IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v17y2020i13p4752-d379223.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/17/13/4752/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/17/13/4752/
    Download Restriction: no
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Rafael Salas-Zárate & Giner Alor-Hernández & Mario Andrés Paredes-Valverde & María del Pilar Salas-Zárate & Maritza Bustos-López & José Luis Sánchez-Cervantes, 2024. "Mental-Health: An NLP-Based System for Detecting Depression Levels through User Comments on Twitter (X)," Mathematics, MDPI, vol. 12(13), pages 1-30, June.
    2. 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.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:17:y:2020:i:13:p:4752-:d:379223. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.