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.
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References
Pande, N., Karyakarte, M.: A Review for Semantic Analysis and Text Document Annotation Using Natural Language Processing Techniques. Available at SSRN 3418747 (2019)
Alshemali, B., Kalita, J.: Improving the reliability of deep neural networks in NLP: a review. Knowl. Based Syst. 191, 105210 (2019)
Yankah, S., Adams, K.S., Grimes, L., Price, A.: Age and online social media behavior in prediction of social activism orientation. J. Soc. Media Soc. 6(2), 56–89 (2017)
Costello, M., Hawdon, J.: Who are the online extremists among us? sociodemographic characteristics, social networking, and online experiences of those who produce online hate materials. Violence Gend. 5(1), 55–60 (2018)
Ferrara, E.: Contagion dynamics of extremist propaganda in social networks. Inf. Sci. 418, 1–12 (2017)
Awan, I.: Cyber-extremism: Isis and the power of social media. Society 54(2), 138–149 (2017)
Chetty, N., Alathur, S.: Hate speech review in the context of online social networks. Aggress. Violent. Beh. 40, 108–118 (2018)
Kruglanski, A., Jasko, K., Webber, D., Chernikova, M., Molinario, E.: The making of violent extremists. Rev. Gen. Psychol. 22(1), 107–120 (2018)
Chen, H.: Exploring extremism and terrorism on the web: the dark web project. In: Yang, Christopher C., et al. (eds.) PAISI 2007. LNCS, vol. 4430, pp. 1–20. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-71549-8_1
Finlayson, M.A., Halverson, J.R., Corman, S.R.: The N2 corpus: a semantically annotated collection of Islamist extremist stories. LREC, pp. 896–902 (2014)
Chepovskiy, A., Devyatkin, D., Smirnov, I., Ananyeva, M., Kobozeva, M., Solovyev, F.: Exploring linguistic features for extremist texts detection (on the material of Russian-speaking illegal texts). In: 2017 IEEE International Conference on Intelligence and Security Informatics: Security and Big Data, ISI 2017, pp. 188–190. Institute of Electrical and Electronics Engineers Inc. (2017)
Ménard, P.A., Barriere, C.: PACTE: a colloaborative platform for textual annotation. In: Proceedings of the 13th Joint ISO-ACL Workshop on Interoperable Semantic Annotation (ISA-13) (2017)
Anthony, L.: Visualisation in corpus-based discourse studies, pp. 197–224. A Critical Review, Corpus Approaches to Discourse (2018)
Wolfe, C.R., Dandignac, M., Reyna, V.F.: A theoretically motivated method for automatically evaluating texts for gist inferences. Behav. Res. Methods 51(6), 2419–2437 (2019). https://doi.org/10.3758/s13428-019-01284-4
Danekenova, A., Zhussupova, G., Nurmagambetov, R., Shunayeva, S., Popov, V.: The most used forms and methods of citizens involvement in terrorist and extremist activity. J. Pol. & L. 12, 1 (2019)
Nicholls, T., Bright, J.: Understanding news story chains using information retrieval and network clustering techniques. Commun. Methods Measures 13(1), 43–59 (2019)
Tulkens, S., Hilte, L., Lodewyckx, E., Verhoeven, B., Daelemans, W.: The automated detection of racist discourse in dutch social media. Comput. Linguist. Netherlands J. 6, 3–20 (2016)
Narynov, S., Mukhtarkhanuly, D., Omarov, B.: Dataset of depressive posts in Russian Language collected from social media. Data Brief 29, 105195 (2020)
Ahmad, S., Asghar, M.Z., Alotaibi, F.M., Awan, I.: Detection and classification of social media-based extremist affiliations using sentiment analysis techniques. Hum. Centric Comput. Inf. Sci. 9(1), 24 (2019)
Scrivens, R., Gaudette, T., Davies, G., Frank, R.: Searching for extremist content online using the dark crawler and sentiment analysis. In: Methods of Criminology and Criminal Justice Research. Sociology of Crime, Law and Deviance, vol. 24, pp. 179–194. Emerald Publishing Limited (2019)
Asif, M., Ishtiaq, A., Ahmad, H., Aljuaid, H., Shah, J.: Sentiment analysis of extremism in social media from textual information. Telematics and Informatics, p. 101345 (2020)
Last, M., Markov, A., Kandel, A.: Multi-lingual detection of terrorist content on the web. In: Chen, H., et al. (eds.) WISI 2006. LNCS, vol. 3917, pp. 16–30. Springer, Heidelberg (2006). https://doi.org/10.1007/11734628_3
Enghin Omer Using machine learning to identify jihadist messages on Twitter. http://uu.divaportal.org/smash/get/diva2:846343/FULLTEXT01.pdf
Sureka, A., Agarwal, S.: Learning to classify hate and extremism promoting tweets intelligence and security. In: 2014 IEEE Joint Year Informatics Conference (JISIC), 2014, pp. 320–320 (2014). https://doi.org/10.1109/jisic.2014.65
Ferrara, E., Wang, W.-Q., Varol, O., Flammini, A., Galstyan, A.: Predicting online extremism, content adopters, and interaction reciprocity arXiv:1605.00659 [cs.SI] (2016)
Elovici, Y., et al.: Detection of access to terrorrelated Web sites using an Advanced Terror Detection System (ATDS). J. Am. Soc. Inf. Sci. 61, 405–418 (2010). https://doi.org/10.1002/asi.21249
Bolatbek, M., Mussiraliyeva, S., Tukeyev, U.: Creating the dataset of keywords for detecting an extremist orientation in web-resources in the Kazakh language. J. Math. Mech. Comput. Sci. Farabi Kazakh National Univ. 1(97), 134–142 (2018)
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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