A Machine Learning-Based Approach to Personification and Safety Measures in Social Media Applications

The amount of time an average person spends on social media platforms for collecting, sharing, and storing data is exponentially increasing. Social media's rapid growth has brought up serious problems of user impersonation, identity theft, and insecure interactions. As a result, the people's social media data privacy and safety management concerns are drawing urgent attention. Machine learning algorithms' ability to identify customers' usage patterns and their content enables them to identify cyberbullying and fake news. This research provides a multimodal machine learning-based framework for safeguarding user interaction through trustworthy persona detection and safety classification. To identify harmful profiles and misleading material, we utilize diverse text, images, videos, and behavioral pattern data to test the state-of-the-art learning Convolutional Neural Network, Random Forest, Support Vector Machine, Logistic Regression, and Adaptive Boosting algorithms. Among all, Convolutional Neural Network surpassed the rest with an accuracy of 83.1% on benchmark datasets like Face Detection Data Set and Benchmark (FDDB), Annotated Facial Landmarks in the Wild (AFLW ), and the Deepfake Detection Challenge dataset. Our approach allows real-time user emotion and activity recognition, providing a scalable and adaptable approach to enhance safety on social media platforms. This work bridges significant gaps in authenticating content genuineness, trust, and variations in digital communication environments.