CoTBasedAbuseDetection

A Chain-of-Thought (CoT) based abuse detection system that leverages reasoning capabilities to identify and classify abusive content with explainable decision-making.

Overview

This project implements a sophisticated abuse detection system that uses Chain-of-Thought prompting to provide transparent, step-by-step reasoning for content moderation decisions. The system can detect various forms of abuse including harassment, hate speech, cyberbullying, and toxic behavior across different platforms.

Features

• Chain-of-Thought Reasoning: Step-by-step reasoning process for transparent decision-making
• Multi-type Abuse Detection: Supports detection of harassment, hate speech, cyberbullying, and toxicity
• Explainable AI: Provides clear explanations for each moderation decision
• Configurable Thresholds: Adjustable sensitivity levels for different use cases
• Batch Processing: Efficient processing of large content datasets
• Real-time Detection: API endpoints for live content moderation
• Performance Metrics: Comprehensive evaluation and monitoring tools

Project Structure

CoTBasedAbuseDetection/
├── src/
│   ├── models/          # Core detection models
│   ├── cot/            # Chain-of-Thought implementation
│   ├── data/           # Data processing utilities
│   ├── utils/          # Helper functions
│   └── evaluation/     # Evaluation and metrics
├── notebooks/          # Jupyter notebooks for experimentation
├── tests/             # Unit and integration tests
├── data/              # Dataset storage
│   ├── raw/           # Raw datasets
│   ├── processed/     # Processed datasets
│   └── examples/      # Example data for testing
├── results/           # Model outputs and results
├── configs/           # Configuration files
└── scripts/           # Utility scripts

Quick Start

1. Install Dependencies

   pip install -r requirements.txt

2. Run Basic Detection

   python scripts/detect_abuse.py --text "Your text here"

3. Start Interactive Demo

   python scripts/demo.py

Chain-of-Thought Process

The system follows a structured reasoning process:

1. Content Analysis: Initial examination of text features
2. Context Understanding: Interpretation of implicit meanings
3. Pattern Recognition: Identification of abusive patterns
4. Severity Assessment: Evaluation of harm potential
5. Final Classification: Decision with confidence score
6. Explanation Generation: Clear reasoning for the decision

Use Cases

• Social Media Platforms: Automated content moderation
• Online Communities: Forum and comment moderation
• Educational Platforms: Safe learning environment maintenance
• Customer Support: Abuse detection in communications
• Research: Analysis of online abuse patterns

Getting Started

See the notebooks/ directory for detailed examples and tutorials.

Contributing

Contributions are welcome! Please read our contributing guidelines and submit pull requests for any improvements.

License

This project is licensed under the MIT License - see the LICENSE file for details.