Abstract
This work focuses on the proliferation of hate speech among children and adolescents, emphasizing the creation of a particular methodological framework to combat this issue within the Kazakhstani section of social networks. The study incorporates findings from international research undertaken in the USA, Great Britain, and various European nations, employing a complete methodology that encompasses theoretical analysis, synthesis, empirical comparison, and aspects of experimental techniques. Special emphasis is placed on the application of technical solutions for the identification and prevention of hate speech. The research investigates the development of a parser and the methodical accumulation of data required for training machine and deep learning algorithms to detect offensive and toxic remarks in real time. The developed solutions are designed to enhance the efficiency of automatic moderation on online platforms and safeguard vulnerable user groups. The study targets both novices and seasoned professionals in data analysis, highlighting the necessity for enhanced practical research in the utilization of deep learning algorithms. Future plans include the development of the proposed methodologies and the publication of articles focused on a comprehensive examination and optimization of intellectual models to combat the proliferation of hate speech.