Development and evaluation of the effectiveness of automated OSINT data collection and analysis methods for detecting and countering disinformation campaigns in social networks
Abstract
Countering disinformation campaigns in the context of modern information warfare is a pressing issue. Particular attention is paid to the role of social networks as a key tool for spreading fake news and shaping public opinion, which poses a threat to a state's social stability. To address this challenge, the use of automated open-source intelligence (OSINT) analysis systems is proposed to detect and neutralize disinformation campaigns in their early stages. This article examines the PolyAnalyst analytical platform, based on Low-code development principles, which automates the collection, processing, and visualization of data from the Telegram social network. The system incorporates machine learning methods and neuro-linguistic programming (NLP) techniques to analyze texts and identify fake news. A key feature of the system is its ability to construct graph models to track the mechanisms of disinformation dissemination, enabling the identification of sources, pathways, and key participants in information attacks.
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Z. Zhang, A. Poguda [Research on the development of data augmentation techniques in the field of machine translation]. International Journal of Open Information Technologies, 2023, no. 5. Available: https://cyberleninka.ru/article/n/research-on-the-development-of-data-augmentation-techniques-in-the-field-of-machine-translation (accessed January 17, 2025).
D. N. Volozhanina, M. N. Zhukova [Review of the procedure for assessing security using OSINT methods in the conditions of an unstable international political environment]. Aktualnye problemy aviatsii i kosmonavtiki, 2022, no. [номер выпуска]. Available: https://cyberleninka.ru/article/n/peresmotr-protsedury-otsenki-zaschischennosti-metodami-osint-v-usloviyah-nestabilnoy-mezhdunarodnoy-politicheskoy-obstanovki (accessed January 17, 2025).
A. E. Lebid, V. V. Stepanov, M. S. Nazarov [Use of OSINT technologies for civil society institutions]. International Journal of Media and Information Literacy, 2023, no. 1. Available: https://cyberleninka.ru/article/n/use-of-the-osint-technologies-for-civil-society-institutions (accessed January 17, 2025).
G. S. Yakovlev, F. F. Ivanov [Use of low-code platforms in the transition to a process approach in the creation of automated systems]. Vestnik KRaUNC. Fiz.-mat. nauki, 2020, no. 1. Available: https://cyberleninka.ru/article/n/ispolzovanie-low-code-platform-pri-perehode-na-protsessnyy-podhod-v-sozdanii-avtomatizirovannyh-sistem (accessed January 17, 2025).
V. S. Magomadov [Low-code and no-code platforms as a way to make programming more accessible to the wider public]. MNIJ, 2021, no. 6-1 (108). Available: https://cyberleninka.ru/article/n/platformy-low-code-i-no-code-kak-sposob-sdelat-programmirovanie-bolee-dostupnym-dlya-shirokoy-obschestvennosti (accessed January 17, 2025).
A. S. Rusakovich [Intelligent data analysis as a decision support tool]. Sovremennye innovatsii, 2022, no. 1 (41). Available: https://cyberleninka.ru/article/n/intellektualnyy-analiz-dannyh-kak-instrument-podderzhki-prinyatiya-resheniy (accessed January 17, 2025).
A. A. Solomonov [Optimization of ETL processes for big data]. Vestnik nauki, 2024, no. 9 (78). Available: https://cyberleninka.ru/article/n/optimizatsiya-etl-protsessov-dlya-bolshih-dannyh (accessed January 17, 2025).
R. Gruetzemacher, (2022). The Power of Natural Language Processing. Harvard Business Review. Retrieved February 1, 2022, from https://hbr.org/2022/04/the-power-of-natural-language-processing
V. Yu. Statyev, V. A. Dokuchaev, V. V. Maklachkova, (2022). [Information security in the "big data" space]. T-Comm, 4. Retrieved January 20, 2025, from https://cyberleninka.ru/article/n/informatsionnaya-bezopasnost-na-prostranstve-bolshih-dannyh.
P. A. Basina, D. O. Dunayeva, A. Yu. Sarkisova, (2022). [Validation of machine learning models for automated determination of the tonality of Russian-language texts]. Vestnik Tomskogo gosudarstvennogo universiteta, 485. Retrieved January 20, 2025, from https://cyberleninka.ru/article/n/validatsiya-modeley-mashinnogo-obucheniya-dlya-avtomatizirovannogo-opredeleniya-tonalnosti-russkoyazychnyh-tekstov.
V. I. Smirnov, O. V. Novoselova, (2024). [Review of modern methods of big data analysis for various subject areas]. Vestnik nauki, 6(75). Retrieved January 18, 2025, from https://cyberleninka.ru/article/n/obzor-sovremennyh-metodov-analiza-bolshih-dannyh-dlya-razlichnyh-predmetnyh-oblastey
T. S. Kuchkarov, (2023). [On the methods and tools of big data analysis]. Ekonomika i sotsium, 12(115)-2. Retrieved January 18, 2025, from https://cyberleninka.ru/article/n/o-metodah-i-instrumentah-analiza-bolshih-dannyh
A. A. Arlanova, A. M. Nobatov, (2023). [Intelligent data analysis: Types and methods]. Vestnik nauki, 1(58). Retrieved January 18, 2025, from https://cyberleninka.ru/article/n/intellektualnyy-analiz-dannyh-vidy-i-metody
D. K. Karpov, (2021). [Big data processing using Python]. StudNet, 6. Retrieved January 18, 2025, from https://cyberleninka.ru/article/n/obrabotka-bolshih-dannyh-s-ispolzovaniem-sredstv-yazyka-python
D. I. Safikanov, A. A. Artamonov, Yu. E. Fomina, A. I. Cherkassky, (2024). [Statistical model for searching target objects in a social network]. International Journal of Open Information Technologies, 10. Retrieved January 18, 2025, from https://cyberleninka.ru/article/n/statisticheskaya-model-poiska-tselevyh-obektov-v-sotsialnoy-seti
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