Abstract
With the growing importance of the digital world, it becomes more and more important to ensure people’s, especially young people’s, security in the digital world as a whole and in the social networks, particularly. In this paper the authors introduce for the first time the developed full-cycle methodology for detection and monitoring of the presence of destructive impacts via their manifestation in young people profiles in the social network. The research uses information technology methods together with psychological methods. The paper describes the proposed methodology and the techniques included in it as well as the results of the experiments. The methodology should help to determine the features of destructive impacts for further development of recommendations for young people on how to identify and resist them.
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The reported study was funded by RFBR, project number 18-29-22034 mk.
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Doynikova, E., Branitskiy, A., Kotenko, I. (2021). Detection and Monitoring of the Destructive Impacts in the Social Networks Using Machine Learning Methods. In: Pissaloux, E., Papadopoulos, G.A., Achilleos, A., Velázquez, R. (eds) ICT for Health, Accessibility and Wellbeing. IHAW 2021. Communications in Computer and Information Science, vol 1538. Springer, Cham. https://doi.org/10.1007/978-3-030-94209-0_6
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