SafeSpace AI: Intelligent Content Moderation Platform

Abstract

Social media has transformed into a primary medium for communication and content sharing, yet its open nature has simultaneously created avenues for harmful material to spread unchecked. Instances of abusive language, offensive imagery, political misinformation, sexually explicit posts, and targeted harassment have become increasingly common, often posing serious risks to vulnerable users. Existing moderation systems are limited in scope — many lack transparency, fail to support multilingual content, and do not provide effective mechanisms for repeated violators.

This project proposes the development of an AI-based content moderation system that combines natural language processing, image analysis, and reputation scoring to ensure a safe and reliable digital environment. The system is designed to analyze both text and images in real time, identify harmful content, and provide clear explanations when a post is blocked or flagged. An integrated reputation model assigns scores to users based on their behavior, rewarding positive contributions while progressively restricting harmful accounts. Administrators are equipped with a dedicated dashboard to review flagged content, monitor user activity, and configure moderation rules.

To demonstrate the effectiveness of the solution, a prototype social media platform is implemented where posting, commenting, and interactions are actively moderated. The system operates on a lightweight technical stack (React.js, Node.js, Python Flask, and SQLite) to ensure compatibility with student laptops in an offline setup. This ensures accessibility for academic evaluation while aligning with real-world needs for safer online spaces.

In practical terms, the project addresses both academic significance—showcasing the application of AI/ML and web technologies—and societal impact by promoting digital safety and responsible online behavior.