Methodology of monitoring negative psychological influences in online media

Tetiana Vakaliuk; Ihor  Pilkevych; Dmytro  Fedorchuk; Viacheslav Osadchyi; Andrii  Tokar; Olena  Naumchak

doi:10.55056/etq.1

Authors

Tetiana A. Vakaliuk Zhytomyr Polytechnic State University https://orcid.org/0000-0001-6825-4697
Ihor A. Pilkevych Korolov Zhytomyr Military Institute https://orcid.org/0000-0001-5064-3272
Dmytro L. Fedorchuk Korolov Zhytomyr Military Institute https://orcid.org/0000-0003-2896-3522
Viacheslav V. Osadchyi Bogdan Khmelnitsky Melitopol State Pedagogical University https://orcid.org/0000-0001-5659-4774
Andrii M. Tokar Korolov Zhytomyr Military Institute https://orcid.org/0000-0001-7534-2820
Olena M. Naumchak Korolov Zhytomyr Military Institute https://orcid.org/0000-0003-3336-1032

DOI:

https://doi.org/10.55056/etq.1

Keywords:

online media, negative psychological influence, regression model, methodology of monitoring

Abstract

The experience of the military aggression of the Russian Federation against Ukraine has shown the relevance and necessity of understanding the problems of moral and psychological support in the Armed Forces of Ukraine in modern conditions. The problem of constant misinformation of the population, the spread of propaganda and the implementation of destructive psychological influence in the interests of the enemy is very sensitive. The simplest tool for distribution misinformation is the Internet (its easy access and wide popularity). The goal of the article is to develop a methodology for monitoring negative psychological influences in online media. The basis of develop methodology is the build a mathematical model using a 4th order polynomial trendline. To determine the number of text messages that were simultaneously processed in statistical analysis, the Hurst exponent was applied. Indicators of negative psychological influences in text messages are selected. Statistical observation is carried out at the expense of a database with text messages from online media. The coefficients of the polynomial regression model are calculated using the least squares method for using a spreadsheet processor Microsoft Excel, or by solving a system of linear algebraic equations using Cramer's method. It has been proved that the developed mathematical model for monitoring negative psychological influences is adequate over the time interval under study. Due to the developed methodology for monitoring negative psychological influences in online media, it is possible to mathematically describe the process of the influence of text messages on a person. The mathematical model underlying the methodology can be used not only at the monitoring stage, but also at the stage of counteracting destructive psychological effects, as well as for the implementation of preventive measures to prevent the spread of such effects by taking into account the frequency and common ways of spreading negative psychological effects in text messages online media. It should be noted that the developed methodology can be used to automate online media monitoring in order to timely identify information threats to military command and control bodies and the personnel of the Armed Forces of Ukraine in the context of ensuring the information security of the state.

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References

Alguliyev, R.M. and Aghayeva, S.R., 2016. Online media monitoring: current state, problems and development prospects. Problems of information society, (1), pp.56–62. Available from: https://doi.org/10.25045/jpis.v07.i1.07. DOI: https://doi.org/10.25045/jpis.v07.i1.07

Gneiting, T. and Schlather, M., 2004. Stochastic models that separate fractal dimension and the Hurst effect. Siam review, 46(2), pp.269–282. Available from: https://doi.org/10.1137/s0036144501394387. DOI: https://doi.org/10.1137/S0036144501394387

Hilbert, M. and López, P., 2011. The world’s technological capacity to store, communicate, and compute information. Science, 332(6025), pp.60–65. Available from: https://doi.org/10.1126/science.1200970. DOI: https://doi.org/10.1126/science.1200970

Hrabar, I.H., Hryshchuk, R.V. and Molodetska, K.V., 2019. Bezpekova synerhetyka: kibernetychnyi ta informatsiinyi aspekty (Security synergetics: cybernetic and information aspects). Zhytomyr: Zhytomyr National Agroecological University.

Maevsky, O., Artemchuk, V., Brodsky, Y., Pilkevych, I. and Topolnitsky, P., 2020. Modeling of the process of optimization of decision-making at control of parameters of energy and technical systems on the example of remote earth’s sensing tools. In: V. Babak, V. Isaienko and A. Zaporozhets, eds. Systems, decision and control in energy i. Cham: Springer International Publishing, pp.111–122. Available from: https://doi.org/10.1007/978-3-030-48583-2_7. DOI: https://doi.org/10.1007/978-3-030-48583-2_7

Mazurenko, V.V. and Shtovba, S.D., 2015. Overview of models for social network analysis. Bulletin of vinnytsia polytechnic institute, 2, pp.62–74. Available from: http://shtovba.vk.vntu.edu.ua/file/0b9b46d5ddfdac57eb82795cd7d4a060.pdf.

Molodetska-Hrynchuk, K.V., 2017. The model of decision making support system for detection and assessment of the state information security threat of social networking services. Ukrainian scientific journal of information security, 23(2), pp.136–144. Available from: https://doi.org/10.18372/2225-5036.23.11803. DOI: https://doi.org/10.18372/2225-5036.23.11803

Pinelis, I., 2019. An asymptotically optimal transform of Pearson’s correlation statistic. Mathematical methods of statistics, 28(4), pp.307–318. Available from: https://doi.org/10.3103/S1066530719040057. DOI: https://doi.org/10.3103/S1066530719040057

Qin, H.Q., Li, Z.H. and Yang, J.J., 2020. The impact of online media big data on firm performance: Based on grey relation entropy method. Mathematical problems in engineering, 2020, p.1847194. Available from: https://doi.org/10.1155/2020/1847194. DOI: https://doi.org/10.1155/2020/1847194

Savchuk, V.S., 2018. Simulation model of distribution of products of psychological influence in social networks. Collection of scientific works of zhytomyr korolyov military institute, 15, pp.94–102. Available from: https://zvir.zt.ua/images/stories/ZbirnikNP/19_12_18/11.pdf.

Ulichev, O., 2018. Mathematical model of dissemination of informational and psychological influences in the social network segment. Machinery in agricultural production, industrial engineering, automation, 31, pp.165–174. Available from: https://doi.org/10.32515/2409-9392.2018.31.165-174. DOI: https://doi.org/10.32515/2409-9392.2018.31.165-174

Vasilieva, N.K., Mironenko, O.A., Samarets, N.M. and Chorna, N.O., 2017. Ekonometryka v elektronnykh tablytsiakh (Econometrics in spreadsheets). Dnipro: Bila K. O.