nach oben

Tipp

Weitere Kapitel dieses Buchs durch Wischen aufrufen

Erschienen in:

Kapitel lesen Erstes Kapitel lesen

2021 | OriginalPaper | Buchkapitel

Bigram Based Deep Neural Network for Extremism Detection in Online User Generated Contents in the Kazakh Language

verfasst von : Shynar Mussiraliyeva, Batyrkhan Omarov, Milana Bolatbek, Kalamkas Bagitova, Zhanna Alimzhanova

Erschienen in: Advances in Computational Collective Intelligence

Verlag: Springer International Publishing

Einloggen, um Zugang zu erhalten

Abstract

Countering the spread of aggressive information and extremism in the global network is an urgent problem of society and government agencies, which is solved in particular by filtering unwanted Internet resources. A necessary condition for such filtering is the classification of the content of websites, texts and documents of the information flow. Therefore, an urgent problem of information technologies is the classification of texts in natural languages in order to detect extremist texts, such as calls for extremism and other messages that threaten the security of citizens.

Therefore, our research examines the detection of extremist messages in online content in the Kazakh language. To do this, we have collected a corpus of extremist texts from open sources, developed a deep neural network based on bigrams for detecting extremist texts in the Kazakh language. The proposed model has shown high efficiency in comparison with classical methods of machine learning and deep learning.

Vorheriges Kapitel Conversational Machine Reading Comprehension for Vietnamese Healthcare Texts

Nächstes Kapitel Cbow Training Time and Accuracy Optimization Using SkipGram

Diez-Olivan, A., Del Ser, J., Galar, D., Sierra, B.: Data fusion and machine learning for industrial prognosis: trends and perspectives towards Industry 4.0. Inf. Fusion 50, 92–111 (2019) CrossRef

Da Li, X., Duan, L.: Big data for cyber physical systems in industry 4.0: a survey. Enterp. Inf. Syst. 13(2), 148–169 (2019). https://doi.org/10.1080/17517575.2018.1442934 CrossRef

Kendzhaeva, B., Omarov, B., Abdiyeva, G., Anarbayev, A., Dauletbek, Y., Omarov, B.: Providing safety for citizens and tourists in cities: a system for detecting anomalous sounds. In: Luhach, A.K., Jat, D.S., Ghazali, K.H.B., Gao, X.-Z., Lingras, P. (eds.) Advanced Informatics for Computing Research: 4th International Conference, ICAICR 2020, Gurugram, India, December 26–27, 2020, Revised Selected Papers, Part I, pp. 264–273. Springer Singapore, Singapore (2021). https://doi.org/10.1007/978-981-16-3660-8_25 CrossRef

Sinha, S., Basak, S., Dey, Y., Mondal, A.: An educational Chatbot for answering queries. In: Mandal, J.K., Bhattacharya, D. (eds.) Emerging Technology in Modelling and Graphics. AISC, vol. 937, pp. 55–60. Springer, Singapore (2020). https://doi.org/10.1007/978-981-13-7403-6_7 CrossRef

Ait-Mlouk, A., Jiang, L.: KBot: a knowledge graph based chatBot for natural language understanding over linked data. IEEE Access 8, 149220–149230 (2020) CrossRef

Alshemali, B., Kalita, J.: Improving the reliability of deep neural networks in NLP: a review. Knowle. Based Syst. 191, 105210 (2020). KBot: a Knowledge graph based chatBot for natural language understanding over linked data CrossRef

Manogaran, G., Varatharajan, R., Priyan, M.K.: Hybrid recommendation system for heart disease diagnosis based on multiple kernel learning with adaptive neuro-fuzzy inference system. Multimedia Tools Appl. 77(4), 4379–4399 (2017). https://doi.org/10.1007/s11042-017-5515-y CrossRef

Mussiraliyeva, S., Bolatbek, M., Omarov, B., Bagitova, K.: 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: 12th International Conference, ICCCI 2020, Da Nang, Vietnam, November 30 – December 3, 2020, Proceedings, pp. 743–752. Springer International Publishing, Cham (2020). https://doi.org/10.1007/978-3-030-63007-2_58 CrossRef

Altınel, B., Ganiz, M.C.: Semantic text classification: a survey of past and recent advances. Inf. Process. Manage. 54(6), 1129–1153 (2018) CrossRef

10.

Murzamadieva, M., Ivashov, A., Omarov, B., Omarov, B., Kendzhayeva, B., Abdrakhmanov, R.: Development of a system for ensuring humidity in sport complexes. In: 2021 11th International Conference on Cloud Computing, Data Science & Engineering (Confluence), pp. 530–535. IEEE (2021)

11.

Sinoara, R.A., Camacho-Collados, J., Rossi, R.G., Navigli, R., Rezende, S.O.: Knowledge-enhanced document embeddings for text classification. Knowl. Based Syst. 163, 955–971 (2019) CrossRef

12.

Ahmad, S., Asghar, M.Z., Alotaibi, F.M. et al.: Detection and classification of social media-based extremist affiliations using sentiment analysis techniques. Human-centric Comput. Inf. Sci. 9(24) (2019). https://doi.org/10.1186/s13673-019-0185-6

13.

Salminen, J., Hopf, M., Chowdhury, S.A., Jung, S.-G., Almerekhi, H., Jansen, B.J.: Developing an online hate classifier for multiple social media platforms. Human-centric Comput. Inf. Sci. 10(1), 1–34 (2020). https://doi.org/10.1186/s13673-019-0205-6 CrossRef

14.

Johnston, A., Marku, A.: Identifying extremism in text using deep learning. In: Pedrycz, W., Chen, S.-M. (eds.) Development and Analysis of Deep Learning Architectures. SCI, vol. 867, pp. 267–289. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-31764-5_10 CrossRef

15.

Duwairi, R., Hayajneh, A., Quwaider, M.: A deep learning framework for automatic detection of hate speech embedded in Arabic tweets. Arab. J. Sci. Eng. 46(4), 4001–4014 (2021). https://doi.org/10.1007/s13369-021-05383-3 CrossRef

16.

Sharif, O., Hoque, M.M., Kayes, A.S.M., Nowrozy, R., Sarker, I.H.: Detecting suspicious texts using machine learning techniques. Appl. Sci. 10(18), 6527 (2020). https://doi.org/10.3390/app10186527 CrossRef

17.

Armaan, K, Saini, J.K., Bansal, D.: Detecting radical text over online media using deep learning. Comput. Sci. Math. ArXiv abs/1907.12368 (2019)

18.

Vk.com – Vkontakte Social Network

19.

Huang, F., Zhang, S., Zhang, J., Yu, G.: Multimodal learning for topic sentiment analysis in microblogging. Neurocomputing 253, 144–153 (2017). https://doi.org/10.1016/j.neucom.2016.10.086 CrossRef

20.

https://vk.com/dev/methods

21.

Kadhim, A.I.: Survey on supervised machine learning techniques for automatic text classification. Artif. Intell. Rev. 52(1), 273–292 (2019). https://doi.org/10.1007/s10462-018-09677-1 MathSciNetCrossRef

22.

Sun, S., Cao, Z., Zhu, H., Zhao, J.: A survey of optimization methods from a machine learning perspective. IEEE Trans. Cybern. 50(8), 3668–3681 (2019) CrossRef

23.

Khan, F.A., Ibrahim, A.A., Rais, M.S., Rajpoot, P., Khan, A., Akhtar, M.N.: Performance analysis of supervised learning algorithms based on classification approach. In: 2019 IEEE 6th International Conference on Engineering Technologies and Applied Sciences (ICETAS), pp. 1–6. IEEE (2019). DOI: https://doi.org/10.1109/ICETAS48360.2019.9117394

Titel: Bigram Based Deep Neural Network for Extremism Detection in Online User Generated Contents in the Kazakh Language
verfasst von: Shynar Mussiraliyeva
Batyrkhan Omarov
Milana Bolatbek
Kalamkas Bagitova
Zhanna Alimzhanova
Verlag: Springer International Publishing
Buch: Advances in Computational Collective Intelligence
Print ISBN: 978-3-030-88112-2

Electronic ISBN: 978-3-030-88113-9

Copyright-Jahr: 2021
DOI: https://doi.org/10.1007/978-3-030-88113-9_45

Springer Professional

Tipp

Weitere Kapitel dieses Buchs durch Wischen aufrufen

Bigram Based Deep Neural Network for Extremism Detection in Online User Generated Contents in the Kazakh Language

Abstract

Premium Partner

Springer Professional

Tipp

Weitere Kapitel dieses Buchs durch Wischen aufrufen

Abstract

Bitte loggen Sie sich ein, um Zugang zu diesem Inhalt zu erhalten

Premium Partner