ISMAEL, Mieles Toloza,; JESUS, Delgado Meza,    JOHANA, Acevedo-Suárez,. Natural Language Analysis for the Identification of Mental Disturbances in Social Networks: A Systematic Review of Studies. []. , 53, 1, pp.57-72. ISSN 2477-8990.  https://doi.org/10.33333/rp.vol53n1.06.

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.

: social networks; mental health; natural language processing; neural networks; machine learning.

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