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DOI: 10.14489/vkit.2023.04.pp.039-048
Кузнецов С. А., Карпова А. Ю., Савельев А. О.
МЕТОДЫ И ТЕХНОЛОГИИ ИНТЕЛЛЕКТУАЛИЗАЦИИ ПОИСКА ДЕСТРУКТИВНОГО И РАДИКАЛЬНОГО КОНТЕНТА В СОЦИАЛЬНЫХ МЕДИА: АНАЛИЗ СОВРЕМЕННОГО СОСТОЯНИЯ
(с. 39-48)
Аннотация. Представлен сравнительный обзор методов и технологий интеллектуализации поиска деструктивного и радикального контента в социальных медиа. Предложена типологизация и описание основных прикладных задач в данной области. Анализ источников показал, что технологии глубокого обучения и метод опорных векторов являются наиболее популярными в профессиональной среде. Описаны существующие ограничения использования интеллектуальных технологий в предметной области. Прогнозируется тенденция роста двух направлений: развитие организационно-управленческих методов формирования междисциплинарных научных коллективов для решения обозначенных прикладных задач, а также развитие методов классификации контента на базе обработки видео и изображений на фоне снижения значимости текстового контента в социальных медиа.
Ключевые слова: анализ данных социальных сетей; деструктивный контент; радикализация; классификация контента; интеллектуальный анализ данных.
Kuznetsov S. A., Karpova A. Yu., Savelev A.
METHODS AND TECHNOLOGIES FOR INTELLECTUALIZATION OF SEARCHING FOR DESTRUCTIVE AND RADICAL CONTENT IN SOCIAL MEDIA: ANALYSIS OF THE CURRENT STATE
(pp. 39-48)
Abstract. The purpose of this work is a comparative review of methods and technologies for intellectualizing the search for destructive and radical content in social media. The authors propose a typologization and description of the main applied tasks in this field. The analysis of the sources showed that deep learning technologies and the support vector method are the most popular in the professional environment. The authors describe the existing limitations of the intelligent technologies use in the subject area. As a general result of the conducted analytical research, we note the following. Partially formulated conclusions are characteristic not only for such a subject area as the intellectualization of the search for destructive content. In particular, close attention is paid to the problem of compiling qualitative data samples as hindering the further development of artificial intelligence technologies. As trends gaining relevance in the field of countering destructive content, it is worth highlighting, firstly, the need to form interdisciplinary scientific and technical teams, and, secondly, the shift in emphasis in the classification of content towards video and image processing. Intensive involvement of experts in the field of destructive content research – linguists, sociologists and psychologists – to automate its search and analysis will increase the overall degree of formalization of such definitions as “destructive”, “radical” and “extremist”, as well as their inherent features, which will ensure a transition to a more effective level of algorithmization. The growth trend of two directions is predicted: the development of organizational and managerial methods for the formation of interdisciplinary research teams to solve the identified applied tasks, as well as the development of content classification methods based on video and image processing against the background of a decrease in the importance of text content in social media.
Keywords: Social media mining; Destructive content; Radicalization; Content classification; Data mining.
С. А. Кузнецов, А. Ю. Карпова, А. О. Савельев (Национальный исследовательский Томский политехнический университет, Томск, Россия) E-mail:
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S. A. Kuznetsov, A. Yu. Karpova, A. O. Savelev (National Research Tomsk Polytechnic University, Tomsk, Russia) E-mail:
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13. Gokhale R., Fasli M. (2017). Deploying a Co-Training Algorithm to Classify Human-Rights Abuses. 2017 International Conference on the Frontiers and Advances in Data Science, pp. 108 – 113.
14. Johnston A. H., Weiss G. M. (2017). Identifying Sunni Extremist Propaganda with Deep Learning. 2017 IEEE Symposium Series on Computational Intelligence, pp. 1 – 6.
15. Subramani S., Vu H. Q., Wang H. (2017). Intent Classification Using Feature Sets for Domestic Violence Discourse on Social Media. 2017 4th Asia-Pacific World Congress on Computer Science and Engineering, pp. 129 – 136.
16. Shamantha R. B., Shetty S. M., Rai P. (2019). Sentiment Analysis Using Machine Learning Classifiers: Evaluation of Performance. 2019 IEEE 4th International Conference on Computer and Communication Systems (ICCCS), pp. 21 – 25.
17. Kibtiah T. M., Miranda E., Fernando Y., Aryuni M. (2020). Terrorism, Social Media and Text Mining Technique: Review of Six Years Past Studies. 2020 International Conference on Information Management and Technology (ICIMTech), pp. 571 – 576.
18. Miranda E., Aryuni M., Rernando Y., Kibtiah T. M. (2020). A Study of Radicalism Contents Detection in Twitter: Insights from Support Vector Machine Technique. 2020 International Conference on Information Management and Technology (ICIMTech), pp. 549 – 554.
19. Machová K., Mach M., Demková G. (2020). Modelling of the Fake Posting Recognition in On-Line Media Using Machine Learning. International Conference on Current Trends in Theory and Practice of Informatics, pp. 667 – 675.
20. Ayo F. E., Folorunso O., Thomas I. F., Osinuga I. A. et al. (2021). A Probabilistic Clustering Model for Hate Speech Classification in Twitter. Expert System with Applications, Vol. 173.
21. Gruebner O., Sykora M., Lowe S. R., Shankardass K. et al. (2017). Big Data Opportunities for Social Behavioral and Mental Health Research. Social Science & Medicine, Vol. 189, pp. 167 – 169.
22. Mansour S. (2018). Social Media Analysis of User’s Responses to Terrorism Using Sentiment Analysis and Text Mining. Procedia Computer Science, Vol. 140, pp. 95 – 103.
23. Rosé C. P. (2017). A Social Spin on Language Analysis. Nature, Vol. 545, 7653, pp. 166–167.
24. Won D., Steinert-Threlkeld Z. C., Joo J. (2017). Protest Activity Detection and Perceived Violence Estimation from Social Media Images. Proceedings of the 25th ACM International Conference on Multimedia, pp. 786 – 794. New York: ACM.
25. Kotinas I., Fakotakis N. (2018). Text Analysis for Decision Making under Adversarial Environments. Proceedings of the 10th Hellenic Conference on Artificial Intelligence, pp. 1 – 6. New York: ACM.
26. Hammer H. L. (2014). Detecting Threats of Violence in Online Discussions Using Bigrams of Important Words. 2014 IEEE Joint Intelligence and Security Informatics Conference, pp. 319 – 319.
27. Schelter S., Biessmann F., Zobel M., Teneva N. (2016). Structural Patterns in the Rise of Germany’s New Right on Facebook. 2016 IEEE 16th International Conference on Data Mining Workshops, pp. 440 – 445.
28. Chelvachandran N., Jahankhani H. A. (2019). A Study on Keyword Analytics as a Precursor to Machine Learning to Evaluate Radicalisation on Social Media. 2019 IEEE 12th International Conference on Global Security, Safety and Sustainability (ICGS3), pp. 1 – 7.
29. Lopez-Castroman J., Moulahi B., Aze J., Bringay S. et al. (2020). Mining Social Networks to Improve Suicide Prevention: a Scoping Review. Journal of Neuroscience Research, Vol. 98 (4), pp. 616 – 625.
30. Vizcarra J., Fukuda K., Kozaki K. (2020). Violence Identification in Social Media. Semantic Technology, pp. 35 – 49.
31. Wich M., Breitinger M., Strathern W., Naimarevic M. et al. (2021). Are Your Friends Also Haters? Identification of Hater Networks on Social Media. Companion Proceedings of the Web Conference 2021, pp. 481 – 485. New York: ACM.
32. Fan H., Du W., Dahou A., Ewees A. A. et al. (2021). Social Media Toxicity Classification Using Deep Learning: Real-World Application UK Brexit. Electronics, Vol. 10 (11).
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