Applying artificial intelligence to detect depressive disorders in adolescents via social network generated contents

Bolganay Kaldarova; Abay Tursynbayev; Gulnar Zhakypbekova; Gulbakhram Beissenova; Lyazzat Zhaidakbayeva; Sapargali Aldeshov

doi:10.53730/ijhs.v6nS8.12287

Authors

Bolganay Kaldarova South Kazakhstan State Pedagogical University, Shymkent, Kazakhstan
Abay Tursynbayev National Academy of Education named after I. Altynsarin, Nur-Sultan, Kazakhstan
Gulnar Zhakypbekova M. Auezov South Kazakhstan university, Shymkent, Kazakhstan
Gulbakhram Beissenova M. Auezov South Kazakhstan university, Shymkent, Kazakhstan
Lyazzat Zhaidakbayeva M. Auezov South Kazakhstan university, Shymkent, Kazakhstan
Sapargali Aldeshov South Kazakhstan State Pedagogical University, Shymkent, Kazakhstan

Keywords:

depression, healthcare, mental health, social networks, artificial intelligence

Abstract

Depressive disorders and suicide are one of the leading causes of death in most countries around the world; it is one of the three most common causes of death in a group of young people, but so far, no methods have been developed for diagnosing suicidal tendencies. In this connection, the problem of developing methods for identifying people prone to suicidal behavior is becoming especially topical. One of the directions of such research is the search for typological features of the speech related to depression using the methods of mathematical linguistics, automatic text processing and machine learning. In foreign science, the texts of people that were motivated by depression are studied using methods of automatic text processing, machine learning methods, and models that are constructed to allow to classify whether the text is related to depression or not. It seems obvious that in order to develop methods for identifying people who are prone to depression and suicide, it is necessary to analyze not only suicide notes, but also other texts created by people who have committed suicide.

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