GRAPH-BASED ANALYSIS OF INFORMATION FLOWS IN TELEGRAM FOR CYBERSECURITY THREAT DETECTION

This paper explores modern methods for analyzing information flows in messengers, emphasizing their role in cybersecurity. The study compares different approaches, including API-based data collection, the use of graph and relational databases, and the automation of open data gathering. Special attention is given to the theoretical foundations of information flow analysis, focusing on the social graph concept and its application in modeling the dissemination of information across networks. The advantages of graph databases for detecting, visualizing, and analyzing networks of information distribution are examined, highlighting their effectiveness in uncovering hidden connections between channels. A prototype system for automating open data collection has been developed, integrating methods for extracting, processing, and structuring information from messenger platforms. The proposed system employs a combination of graph-based and relational techniques to enhance the accuracy and efficiency of detecting interconnections between communication channels. A series of computational experiments has been conducted to validate the effectiveness of the developed algorithms and software prototypes. The results confirm that combining these methods significantly improves the ability to identify information threats, including disinformation campaigns, automated bot activity, and coordinated attacks within messenger ecosystems. Actionable recommendations for the practical implementation of these approaches in cybersecurity tasks are provided. Specifically, they outline strategies for improving the monitoring and detection of malicious information activities, optimizing data collection and analysis pipelines, and leveraging graph-based insights to enhance situational awareness in digital communication environments. These findings contribute to the ongoing development of advanced cybersecurity solutions aimed at mitigating risks associated with modern information warfare.