Introduction: In social networks, the users can remotely communicate, express themselves, and search for people with similarinterests. At the same time, social networks as a source of information can have a negative impact on the behavior and thinking oftheir users. Purpose: Developing a technique of forecasting the exposure of social network users to destructive influences, based onthe use of artificial neural networks. Results: A technique has been developed and experimentally evaluated for forecasting Ammon’stest results by a social network user’s profile using artificial neural networks. The technique is based on the results of Ammon’s testfor medical students. For training the neural network, a set of features was generated based on the information provided by socialnetwork users. The results of the experiments have confirmed the dependence between the data provided by social network users andtheir psychological characteristics. A mechanism has been developed aimed at prompt detection of destructive impacts or social networkusers’ profiles indicating the susceptibility to such impacts, in order to facilitate the work of psychologists. The experiments haveshown that out of the four investigated types of neural networks, the highest accuracy is provided by a multilayer neural network. Inthe future, it is planned to expand the set of features in order to achieve a better accuracy. Practical relevance: The obtained results canbe used to develop systems for monitoring the Internet environment, detecting the impacts potentially dangerous for mental health ofthe young generation and the nation as a whole.
Authorship Authentication of Short Messages from Social Networks Using Recurrent Artificial Neural Networks
