Decoding Ideology: Machine Learning-Based Detection of Extremist Content
Shynar Mussiraliyeva, Kymbat Baisylbayeva, Milana Bolatbek, Zhastay Yeltay · 2024
This paper explores the use of machine learning techniques to classify texts with regard to extremist ideology, such as radicalization, propaganda, and recruitment. The relevance of this topic is the effective identification and analysis of content that contributes to the spread of extremist ideas on the Internet. This article discusses various approaches to preprocessing textual data and selecting machine learning models. Text materials on social networks, Internet forums, and other online resources were used as a data set. Experiments have been conducted using various classification algorithms, including Naive Bayes classifier, SVM and Logistic regression, etc. The results of the study confirm the possibility of effective use of machine learning methods for automatic classification of texts according to their extremist ideology, which can be a useful tool for detecting and countering the spread of extremist ideas on the Internet.