Use of neural networks for forecasting of the exposure of social network users to destructive impacts
Keywords:
neural network, Ammon’s test, user profile, social network, psychological scales, destructive impact, features.Abstract
Introduction: In social networks, the users can remotely communicate, express themselves, and search for people with similar
interests. At the same time, social networks as a source of information can have a negative impact on the behavior and thinking of
their users. Purpose: Developing a technique of forecasting the exposure of social network users to destructive influences, based on
the use of artificial neural networks. Results: A technique has been developed and experimentally evaluated for forecasting Ammon’s
test results by a social network user’s profile using artificial neural networks. The technique is based on the results of Ammon’s test
for medical students. For training the neural network, a set of features was generated based on the information provided by social
network users. The results of the experiments have confirmed the dependence between the data provided by social network users and
their psychological characteristics. A mechanism has been developed aimed at prompt detection of destructive impacts or social network
users’ profiles indicating the susceptibility to such impacts, in order to facilitate the work of psychologists. The experiments have
shown that out of the four investigated types of neural networks, the highest accuracy is provided by a multilayer neural network. In
the future, it is planned to expand the set of features in order to achieve a better accuracy. Practical relevance: The obtained results can
be used to develop systems for monitoring the Internet environment, detecting the impacts potentially dangerous for mental health of
the young generation and the nation as a whole.