Real-Time Social Media Monitoring for Data Leak Detection

In today’s digital environment, social media has become a dominant platform for interaction, enabling rapid two-way communication between individuals and organizations. However, its widespread adoption has also introducedevolving cyber risks, including phishing attacks, data breaches, malicious URL dissemination, identity theft, and financial scams. These threats frequently exploit social engineering techniques, making traditional signature-based detection methods slow and ineffective against emerging attack patterns. To address this challenge, this research presents a real-time social media monitoring system based on a hybrid approach integrating machine learning algorithms, natural language processing, and behavioral analysis. The proposed system continuously analyzes posts, comments, and direct messages to identify suspicious indicators and potential malicious activities. Natural language processing techniques extract semantic and contextual features to detect deceptive language and phishing intent, while behavioral analysis captures abnormal interaction patterns associated with compromised or malicious accounts. In addition, advanced feature extraction methods are employed to model linguistic, structural, and behavioral characteristics, which are used to train supervised learning models for accurate threat classification. Designed for real- time operation, the system enables early detection and rapid response to cyber incidents. Experimental evaluation shows superior accuracy, scalability, and response time. The framework offers an intelligent and adaptive security solution for platforms.