Метод комп’ютерного прогнозування поведінки пропагандиста при зворотному психологічному впливі

МЕТОД КОМП’ЮТЕРНОГО ПРОГНОЗУВАННЯ ПОВЕДІНКИ ПРОПАГАНДИСТА ПРИ ЗВОРОТНОМУ ПСИХОЛОГІЧНОМУ ВПЛИВІ

METHOD FOR COMPUTER PREDICTION OF THE PROPAGANDIST’S BEHAVIOR UNDER THE REVERSE PSYCHOLOGICAL INFLUENCE

Сторінки: 51-57. Номер: №3, 2021 (297)
Автори:
Я. В. ТАРАСЕНКО
Черкаський державний технологічний університет
Yaroslav V. TARASENKO
Cherkasy State Technological University
DOI: https://www.doi.org/10.31891/2307-5732-2021-297-3-51-57
Надійшла / Paper received :  19.04.2021 р
Надрукована / Paper Printed : 30.06.2021 р

Анотація мовою оригіналу

В роботі запропоновано метод комп’ютерного прогнозування поведінки пропагандиста при зворотному психологічному впливі. В ході дослідження було вдосконалено метод прогнозування поведінки людини в соціальних мережах, що дозволило вирішити задачу підвищення точності комп’ютерного прогнозування поведінки зловмисника в умовах протидії деструктивній пропаганді. Результати експерименту доводять ефективність та високу точність розробленого методу, яка на 4% вище за точність базового методу та за прогнозом зросте в умовах протидії деструктивній пропаганді.
Ключові слова: психологічний вплив, протидія пропаганді, інформаційно-психологічне протиборство, прогнозування дій зловмисника, психолінгвістичний портрет, профайлінг.

Розширена анотація англійською мовою

It was solved the actual problem of improving the accuracy of computer prediction of the malefactor’s behavior under the conditions of counteracting destructive propaganda, which allowed, taking into account the psycholinguistic study of the propagandist’s behavior, to take into account the peculiarities of the reverse psychological influence on him in order of building the most effective strategy for countering information propaganda. At the same time, the profiling approach has been improved to adapt it to the possibility of predicting the propagandist’s behavior in order to increase the efficiency of further use of a specialized quantum-semantic psycholinguistic analysis method. The method of predicting human behavior in social networks has been improved on the basis of the quantum-semantic psycholinguistic analysis method for the English-language text of propaganda discourse to take into account the peculiarities of information warfare in predicting. It was improved the forecasting accuracy for more efficient distribution of probabilistic estimates of the forecast in order to identify the most probable options for further malefactor’s actions. To prove the effectiveness and accuracy of the developed method, an evaluation of its functioning was conducted. The operation of some key modules of the advanced method was investigated experimentally. It was revealed an increase in the average accuracy of the malefactor’s actions prediction by 1% before the beginning of counteraction to the information and psychological influence and by 4% after the beginning of counteraction. It is expected to increase the percentage of efficiency in the application of the method in the process of counteracting destructive propaganda in the real conditions of the reverse psychological influence. The results of the study can be used by the subjects of combating destructive information and psychological influence in the further adjusting general strategy of counteraction to information propaganda in order to protect citizens from destructive information influence.
Keywords: psychological influence, counter propaganda, information-psychological warfare, predicting the malefactor’s actions, psycholinguistic portrait, profiling.

References

1. On the decision of the National Security and Defense Council of Ukraine from December 29, 2016 «About the Doctrine of information security of Ukraine» : Decree of the President of Ukraine from February 25, 2017 №47/2017 [Electronic resource]. – Available at : https://zakon.rada.gov.ua/laws/show/47/2017#Text. – (accessed on: May 03, 2021).
2. Kobilnyk B.Yu. The role of information and psychological influences in information warfare / B.Yu. Kobilnyk, A.I. Hizun // Current challenges and achievements in the field of cybersecurity: proceedings of the Ukrainian scientific-practical conference (Kropyvnytskyi, November 23-25, 2016). – P. 28-29.
3. Tarasenko Ya.V. Using the principles of quantum linguistics in information warfare / Ya.V. Tarasenko // Ukrainian Scientific Journal of Information Security. – 2019. – № 25 (2). – P. 96-103. – DOI: https://doi.org/10.18372/2225-5036.25.13671.
4. Silverman B.G. Artificial intelligence and human behavior modeling and simulation for mental health conditions / B.G. Silverman, N. Hanrahan, L. Huang et al. // Artificial Intelligence in Behavioral and Mental Health Care. – 2016. – P. 163-183. – DOI: https://doi.org/10.1016/b978-0-12-420248-1.00007-6.
5. Kilany M. Towards a Computational Human Behavioral Model / M. Kilany, A. Adl, A.E. Hassanien, T. Kim // 3rd International Conference on Computer, Information and Application (Yeosu, South Korea, May 21-23, 2015). – P. 42-45. – DOI: https://doi.org/10.1109/CIA.2015.18.
6. Wagner A. Psychological modeling of humans by assistive robots / A. Wagner, E. Briscoe // Human Modelling for Bio-Inspired Robotics. – 2017. – P. 273-296. – DOI: https://doi.org/10.1016/B978-0-12-803137-7.00011-2.
7. Pentlan A. Modeling and Prediction of Human Behavior / A. Pentland, A. Liu // Neural Computation. – 1999. – № 11 (1). – P. 229-242. – DOI: https://doi.org/10.1162/089976699300016890.
8. Phan N. A deep learning approach for human behavior prediction with explanations in health social networks: Social restricted boltzmann machine (srbm+) / N. Phan, D. Dou, B. Piniewski, D. Kil // Social Network Analysis and Mining. – 2016. – № 6 (1) [Electronic resource]. – Available at : https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6368350/. – (accessed on: May 05, 2021). – DOI: https://doi.org/10.1007/s13278-016-0379-0.
9. Jenkins A.C. Predicting human behavior toward members of different social groups / A.C. Jenkins, P. Karashchuk, L. Zhu, M. Hsu // Proceedings of the National Academy of Sciences of the United States of America. – 2018. – № 115 (39). – P. 9696-9701. – DOI: https://doi.org/10.1073.
10. Plonsky O. Predicting human decisions with behavioral theories and machine learning / O. Plonsky, R. Apel, E. Ert, et al. // eprint arXiv:1904.06866. – 2019 [Electronic resource]. – Available at : https://arxiv.org/abs/1904.06866. – (accessed on: May 06, 2021).
11. Davahli M.R. Identification and prediction of human behavior through mining of unstructured textual data / M.R. Davahli, W. Karwowski, E. Gutierrez et al. // Symmetry. – 2020. – № 12 (11). – P. 1902. – DOI: https://doi.org/10.3390/sym12111902.
12. Афанасьева О.Р. Криминологическое прогнозирование индивидуального преступного поведения / О.Р. Афанасьева, О.В. Глеба // Международный научно-исследовательский журнал. – 2017. – № 03 (57). – Часть – С. 118-121. – DOI: https://doi.org/10.23670/IRJ.2017.57.109.
13. Halustian O.A. Forming a psychological profile of an unidentified person according to the characteristics of his written text / O.A. Halustian, L.M. Zakharenko, V.O. Kazmirenko ; under the general ed. of O.I. Motliakh. – Kyiv: National Academy of Internal Affairs, 2020. – 68 p.
14. Tarasenko Ya.V. The content analysis method of the semantic particle in texts with psycholinguistic influence signs / Ya.V. Tarasenko // Control, Navigation and Communication Systems. Academic Journal. – 2019. – № 6 (58). – P. 92-96. – DOI: https://doi.org/10.26906/SUNZ.2019.6.092.
15. Tarasenko Ya. The quantum-semantic psycholinguistic analysis method for the english-language text of propaganda discourse / Ya. Tarasenko // Advanced Information Systems. – 2019. – № 3 (4). – P. 62-68. – DOI: https://doi.org/10.20998/2522-9052.2019.4.09.
16. Tarasenko Ya. Content-criteria of psycholinguistic portrait’s semantic category for researching the group propaganda / Ya. Tarasenko // Ukrainian Scientific Journal of Information Security. – 2020. – № 26 (1). – P. 5-13. – DOI: https://doi.org/10.18372/2225-5036.26.14526.