An Intelligent Video Analysis Framework For Classifying And Prioritizing Harmful Social Media Content With CNNs

Abstract

Social media platform have develop essential channels intended communication, content sharing, and community building. However, the widespread distribution of user-generated content introduces significant challenges in monitoring and managing harmful, inappropriate, or violative material. Addressing these challenges is crucial to maintaining a safe and respectful online environment. This project presents a comprehensive framework for detecting and rating violative user content in social media, employing advanced computer vision techniques to analyze video content. The framework extracts frames from videos and utilizes (CNNs) to classify various forms of violations with high accurateness. A robust dataset, encompassing diverse categories of violations, is employed to train the model, ensuring its effectiveness across different contexts and platforms. The framework's evaluation component is thorough, incorporating system of measurement such as accurateness, precision, recall, & F1-score to assess model performance. Confusion matrices & classification reports offer detailed insights into the system's effectiveness. The model's capability to process video frames in real- time simplifies its integration into prevailing social media monitoring systems, providing a scalable solution for content moderation. In addition to detection, the framework includes a rating mechanism that evaluates the severity of detected violations. This rating system aids in prioritizing content review processes, ensuring that the most harmful material is addressed promptly. The use of advanced machine learning algorithms and comprehensive training data allows the framework to adapt to evolving content trends and emerging threats effectively. Overall, this project delivers a scalable, accurate, and efficient solution for detecting and rating violative user content on social media platforms. By enhancing content moderation capabilities, it contributes significantly to the creation of safer and more respectful online communities.