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Análisis del Lenguaje Natural para la Identificación de Alteraciones Mentales en Redes Sociales: Una Revisión Sistemática de Estudios

  • Mieles, Ismael [1] ; Delgado Meza, Jesus Armando [2] ; Acevedo, Johana [1]
    1. [1] Universidad de Investigación y Desarrollo -UDI, Psicología virtual, Bucaramanga, Colombia
    2. [2] Universidad de Investigación y Desarrollo -UDI, Psicología, Bucaramanga, Colombia
  • Localización: Revista Politécnica, ISSN-e 2477-8990, Vol. 53, Nº. 1, 2024 (Ejemplar dedicado a: Revista Politécnica), págs. 57-72
  • Idioma: español
  • DOI: 10.33333/rp.vol53n1.06
  • Títulos paralelos:
    • Natural Language Analysis for the Identification of Mental Disturbances in Social Networks: A Systematic Review of Studies
  • Enlaces
  • Resumen
    • español

      Las enfermedades mentales constituyen una de las principales causas de angustia en la vida de las personas a nivel individual, y repercuten en la salud y el bienestar de la sociedad. Para captar estas complejas asociaciones, las ciencias computacionales y la comunicación, a través del uso de métodos de procesamiento del lenguaje natural (NLP) en datos recolectados en redes sociales, han aportado prometedores avances para potenciar la atención sanitaria mental proactiva y ayudar al diagnóstico precoz. Por ello, se realizó una revisión sistemática de la literatura acerca de la detección de alteraciones mentales a través de redes sociales, mediante el uso de NLP en los últimos 5 años, que permitió identificar métodos, tendencias y orientaciones futuras, a través del análisis de 73 estudios, de 509 que arrojó la revisión de documentos extraídos de bases de datos científicas. El estudio reveló que, los fenómenos más comúnmente estudiados, correspondieron a Depresión e Ideación suicida, identificados a través del uso de algoritmos como el LIWC, CNN, LSTM, RF y SVM, en datos extraídos principalmente de Reddit y Twitter. Este estudio, finalmente proporciona algunas recomendaciones sobre las metodologías de NLP para la detección de enfermedades mentales, que pueden ser adoptadas en el ejercicio de profesionales interesados en la salud mental, y algunas reflexiones sobre el uso de estas tecnologías.

    • English

      Mental illness is a major cause of distress in people's lives at the individual level and impacts the health and well-being of society. To capture these complex associations, computational science and communication, through the use of natural language processing (NLP) methods on data collected in social networks, have provided promising advances to enhance proactive mental health care and aid in early diagnosis. Therefore, a systematic review of the literature on the detection of mental disorders through social networks, using NLP in the last 5 years, was carried out, which allowed identifying methods, trends and future directions, through the analysis of 73 studies, out of 509 that resulted from the review of documents extracted from scientific databases. The study revealed that the most commonly studied phenomena corresponded to Depression and Suicidal Ideation, identified through the use of algorithms such as LIWC, CNN, LSTM, RF and SVM on data extracted mainly from Reddit and Twitter. This study finally provides some recommendations on NLP methodologies for mental illness detection that can be adopted in the practice of professionals interested in mental health and some reflections on the use of these technologies.

       

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