Socio Net: An Interpretable Deep Neural Network Framework For Crime Detection In Social Media Platforms

Social media platforms (SMPs) are widely used for communication and information sharing, but they are also increasingly exploited for criminal activities. These activities include forming illegal groups, spreading false information, stealing personal data, and conducting cyberattacks. The ease of access and anonymity provided by SMPs make them attractive for criminals to perform such actions. Sensitive information such as passwords, financial details, and personal data can be misused, leading to serious threats like identity theft, data breaches, and malware attacks. This paper focuses on detecting criminal activities on social media using machine learning techniques. By analyzing user-generated content, the system can identify suspicious patterns and classify potentially harmful activities. The proposed approach aims to improve early detection and help in preventing cybercrimes effectively. Additionally, it highlights the importance of user awareness and responsible data sharing to reduce risks associated with social media usage.