Abstract
At present, the number of terrorist attacks carried out by lone terrorists under the influence of propaganda and extremist ideology, as well as by organized terrorist communities with a network and poorly connected structure, is increasing. The main means of information exchange, recruitment and promotion for such structures is the Internet, namely web resources, social networks and e-mail. In this regard, the task of detecting, identifying topics of communication, connections, as well as monitoring the behavior and forecasting of threats emanating from individual users, groups and network communities that generate and distribute terrorist and extremist information on the Internet arises.
The paper is devoted to the research and application of machine learning methods aimed at solving the problems of detecting potentially dangerous information on the Internet. The study examines the development of a corpus in Kazakh language for detecting extremist messages, and explores machine learning algorithms that used to detect content that contains calls for terrorist attacks and propaganda materials.
Keywords
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Acknowledgements
This research has been funded by the Ministry of Digital Development, Innovations and Aerospace industry of the Republic of Kazakhstan (Grant No. AP06851248, “Development of models, algorithms for semantic analysis to identify extremist content in web resources and creation the tool for cyber forensics”).
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Mussiraliyeva, S., Bolatbek, M., Omarov, B., Bagitova, K. (2020). Detection of Extremist Ideation on Social Media Using Machine Learning Techniques. In: Nguyen, N.T., Hoang, B.H., Huynh, C.P., Hwang, D., Trawiński, B., Vossen, G. (eds) Computational Collective Intelligence. ICCCI 2020. Lecture Notes in Computer Science(), vol 12496. Springer, Cham. https://doi.org/10.1007/978-3-030-63007-2_58
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