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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

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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.

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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

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