COMPARATIVE ANALYSIS OF MACHINE LEARNING ALGORTMS TO IDENTIFY EXTREMIST TEXTS IN THE KAZAKH LANGUAGE

Abstract

The article explores various models and methods employed in classifying text content with the aim of identifying destructive information within social networks. The study focuses on utilizing machine learning techniques, such as support vector machines, naive Bayes classifiers, random tree methods, decision tree, k-Nearest Neighbors algorithm, logistic regression, gradient boosting to identify extremist texts. The research findings showcase the effectiveness of these methodologies in the identification process. The article also offers an overview of existing research, methodologies, and software products in the analysis of extremist texts, emphasizing the importance of case-based learning, deductive learning models, and automated data collection and analysis. Additionally, the article provides an overview of existing research, methods, and software products within the field of analyzing extremist texts. It highlights the significance of case-based learning and the use of deductive learning models, as well as automated data collection and analysis techniques. These approaches contribute to the overall understanding and detection of extremist content. The article further discusses the relevance and future prospects of the presented research. It emphasizes the need to expand the corpus of documents studied, enabling a more comprehensive analysis of texts, including those in photo, audio, and video formats. The development of complex models for recognizing hidden extremist propaganda is also identified as a key direction for future work. By addressing these areas of focus, the research presented in the article aims to advance the field of identifying and combating extremist content within social networks. The incorporation of advanced techniques and technologies is crucial to effectively detect and address the presence of such content in various forms and formats.