Designing a predictive model of self-destructive behavior based on automated analysis of search queries

A. B. Uglova, B. A. Nizomutdinov

Abstract


The article considers the possibility of creating a predictive model of self-destructive behavior based on the results of automated analysis of users' search queries about destructive behaviors and the search for psychological help. An automated analysis of statistical information about user requests was carried out on the basis of an empirically identified implicit dictionary of markers about autodestructive behavior. The All-Russian and regional query statistics were processed and analyzed using the Yandex Wordstat tool. The study showed the annual variability of users' interest in self-destructive topics, as well as the regional specifics of requests. There was an increase in interest in the fourth quarter in the autumn-winter period and a decline in interest in the third quarter in the summer. The results obtained repeat the seasonal dynamics of clinical diseases identified in previous studies. To build a preventive model using regression analysis, predictors of an increase in requests for professional psychological assistance were identified. The regions with the greatest interest in self-destructive behavior (Barnaul, Belgorod, Irkutsk, Khabarovsk) were also identified, which can be useful for building a flexible monitoring system. The study showed that the analysis of information about self-destructive practices and the need for psychological help is one of the most objective analysis tools, since it allows you to evaluate large amounts of information without the influence of social desirability and socio-cultural stereotypes about psychological help.

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