Content Moderation System 🔍

A machine-learning-based system for automatic content moderation, detecting toxic, spam, and safe content.
The system combines rule-based filters with ML classifiers to simulate real-world moderation tools used by platforms like Facebook, Twitter, YouTube, and OpenAI.

👉 Open in Colab: Click Here

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📌 Project Overview

This project implements a Content Moderation System that automatically classifies and filters user-generated content using machine learning techniques.

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⚙ System Architecture

🔹 Text Preprocessing Module

• Text normalization and cleaning
• Feature extraction (length, word count, capitalization ratio, etc.)
• URL, mention, and hashtag detection

🔹 Rule-Based Filter

• Offensive keyword blacklist
• Pattern matching for spam/promotional content
• Excessive capitalization and character repetition detection

🔹 Machine Learning Classifier

• Algorithms: Naive Bayes, Logistic Regression, SVM, Random Forest
• TF-IDF vectorization
• Multi-class classification: toxic, spam, safe

🔹 Risk Assessment Engine

• Weighted scoring combining rule-based + ML predictions
• Configurable confidence thresholds
• Dynamic decision making based on risk scores

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🚀 Google Colab Notebook

You can run and explore the project notebook here:
👉 Colab Link

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📊 Dataset

The dataset used for training the model is available on Google Drive:
👉 Download Dataset

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👩‍💻 Team Members

• Haneen Mohamed
• Mai Mohamed
• Farida Montser