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    Intelligent system for monitoring the information space of news about artificial intelligence
    (Institute of Special Communication and Information Protection of National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”, 2025) Riabtsev, Viacheslav; Marchuk, Yurii
    Under conditions of exponential growth in the volume of information related to the development of artificial intelligence (AI) technologies, traditional methods of monitoring the media space become ineffective. Messengers and social networks, particularly Telegram, have become key channels for distributing real-time news, generating high-intensity streams of unstructured data. The article considers the problem of creating an intelligent system for monitoring the information space that is capable of automatically structuring this chaotic data flow. The aim of this work is the design and software implementation of a platform architecture that provides a full ETL (Extract–Transform–Load) cycle: from collecting data via the Telegram API to its semantic analysis and visualization. A modular architecture is proposed that includes subsystems for asynchronous parsing, text preprocessing (NLP pipeline), and an analytical core. The study focuses primarily on the algorithmic support of the system. The use of a hybrid approach to text classification is substantiated, combining dictionary-based methods (Keyword Matching) for accurate identification of entities (for example, models GPT‑4, Gemini, LLaMA) with machine learning components for determining message sentiment. An algorithm for content deduplication is developed, which makes it possible to filter out reposts and information noise and to highlight the sources of news. The results of experimental testing of the developed system on a sample of more than 10,000 messages from thematic Telegram channels are presented. A categorization accuracy of 91% was achieved, which confirms the effectiveness of the chosen methods. The system’s capabilities in detecting trends in real time, constructing the dynamics of mentions of key technologies, and generating automated analytical reports are demonstrated. The practical value of the work lies in creating a toolkit for data researchers, analysts, and developers that significantly reduces the time required to search for relevant information and to track the AI technology landscape.