Machine learning methods for identifying destructive texts in the Kazakh language

In the digital age, society is faced with an increasing number of destructive messages on the Internet, including insults, racism, violent extremism and national extremism. This paper presents a machine learning-based approach to classify destructive texts in the Kazakh language, collected from social networks. During the study a text corpora has been accumulated on social networks: YouTube, Vkontakte, Telegram. The study uses advanced text vectorization methods, including Bag of Words, TF-IDF, and Word2Vec, to preprocess text data. Our findings demonstrate that Logistic Regression outperforms other models, achieving an accuracy of 86%. These results contribute to the development of automated systems for identifying destructive content, promoting safer online communication."