This research work has focused on a digital forensic analysis of social media through mobile devices to determine the primary criminal. This proposed system considered the mobile device used by the prime suspect as the main evidence of cybercrime and tried to find out the degree of criminal involvement in terms of probability likelihood. At first, the most critical data elements were obtained, e.g., deleted files and keywords, through the forensic analysis of the mobile device. This will also help in the identification of the main culprits in the investigation of cybercrime. Next, the system classified the criminals in one of three zones based on the analysis of the keywords to determine the level of crime. The system also takes into account the most probable timeframe for the crime. Thus, the proposed system helps to identify the main culprits in investigating cybercrime more efficiently than the traditional approaches. The system is also looking into the most probable timeframe for the crime, for example, it has been observed that most cybercrime happens on the weekends. The proposed system investigates using cookies and the logical image of the device that cyber criminals left behind.