Development of Formal-Theory Based Intelligent and Automated Application for Social Media Forensics

Abstract : With the rapid expansion of social media platforms, the need for effective and intelligent investigation techniques has become critical to address rising concerns such as cyber threats, misinformation, and digital crimes. This research focuses on the development of a formal theory-based, intelligent, and automated application for Social Media Forensics designed to aid forensic investigators in efficiently collecting, analyzing, and preserving digital evidence. Traditional forensic approaches often encounter challenges like data inconsistency, lack of standardization, and difficulties in extracting relevant evidence from vast amounts of unstructured data. To overcome these limitations, we designed and implemented a user-friendly social media application using HTML, CSS, Bootstrap, Django, and SQL that incorporates a structured and efficient forensic investigation system. The system allows administrators to define and filter inappropriate content, ensuring that any user-generated post containing such content is intercepted and redirected to the admin for review rather than being published. This feature supports real-time monitoring, automated content moderation, data extraction, and evidence categorization, streamlining the overall forensic investigation process. The integration of database management with structured querying facilitates precise retrieval of digital evidence, reducing the need for extensive manual analysis. A well-defined forensic workflow enhances the admin’s ability to correlate extracted data, analyze user behavior, and detect potential cyber threats more effectively. Additionally, the application employs role-based access control, clearly defining separate functionalities for users and administrators. Users can post content freely, while the system continuously evaluates submissions for compliance with content standards. The application also ensures digital evidence preservation through secure storage mechanisms, maintaining data integrity and supporting compliance with forensic standards. The incorporation of automated processes significantly reduces human effort and minimizes errors, improving the overall accuracy and efficiency of investigations. Results demonstrate that this approach to social media forensics not only prevents the dissemination of harmful or inappropriate content but also facilitates systematic evidence collection and analysis. By leveraging structured data management and automation, the application enhances the investigative workflow, making digital forensic practices more robust, scalable, and effective. This study concludes that the integration of intelligent systems in social media forensics provides a promising solution for contemporary challenges in cybercrime investigations, enabling more responsive and reliable forensic processes.

Keyword Social Media Forensics, Digital Evidence, Automated Content Moderation, Cyber Threat Detection, Forensic Workflow, Structured Data Management