Open Access

ARTICLE

Applying Machine Learning Techniques for Religious Extremism Detection on Online User Contents

by Shynar Mussiraliyeva¹, Batyrkhan Omarov^1,*, Paul Yoo^1,2, Milana Bolatbek¹

1 Al-Farabi Kazakh National University, Almaty, Kazakhstan
2 CSIS, Birkbeck College, University of London, London, UK

* Corresponding Author: Batyrkhan Omarov. Email:

Computers, Materials & Continua 2022, 70(1), 915-934. https://doi.org/10.32604/cmc.2022.019189

Received 05 April 2021; Accepted 09 May 2021; Issue published 07 September 2021

Abstract

In this research paper, we propose a corpus for the task of detecting religious extremism in social networks and open sources and compare various machine learning algorithms for the binary classification problem using a previously created corpus, thereby checking whether it is possible to detect extremist messages in the Kazakh language. To do this, the authors trained models using six classic machine-learning algorithms such as Support Vector Machine, Decision Tree, Random Forest, K Nearest Neighbors, Naive Bayes, and Logistic Regression. To increase the accuracy of detecting extremist texts, we used various characteristics such as Statistical Features, TF-IDF, POS, LIWC, and applied oversampling and undersampling techniques to handle imbalanced data. As a result, we achieved 98% accuracy in detecting religious extremism in Kazakh texts for the collected dataset. Testing the developed machine learning models in various databases that are often found in everyday life “Jokes”, “News”, “Toxic content”, “Spam”, “Advertising” has also shown high rates of extremism detection.

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Keywords

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