Social Media Based Open Source Intelligence Analysis with Artificial Intelligence - Selen KAYAN KILIC; Uraz YAVANOĞLU (Communications of International Proceedings, 2025)
In today's digital world, Open-Source Intelligence (OSINT) is of critical importance in cyber threat analysis. However, when the studies in the current literature are examined, it has been realized that there is no comprehensive and automatic framework that allows the processing of visual and textual data obtained from social media platforms, especially Telegram, in an integrated manner with artificial intelligence. In order to fill this gap in literature, an AI-supported OSINT framework is proposed in this study, in which social media data is classified using GPT-based natural language processing models and configured for cyber threat intelligence. As a method, the text and images obtained from Telegram channels are collected automatically, classified according to categorical crime headings via GPT-based models, and the obtained outputs are configured in STIX 2.1 format and integrated into the OpenCTI platform. In addition, the System also provides pattern detection and relationship analysis by establishing correlations between different data types. The findings confirm that artificial intelligence-assisted classification provides superior performance compared to traditional methods in the accurate and rapid detection of threat contents. In addition, it has been observed that the data presented via visual panels and timelines with the OpenCTI platform accelerates the decision-making processes. This study not only provides a scalable and more easily applicable model for cybersecurity professionals but also makes an important contribution to the transformation of raw social media data into meaningful and actionable threat intelligence.
Keywords: Open-Source Intelligence; Artificial Intelligence; Social Media Analysis; Cyber Threat Intelligence.